Monitoring Global Crop Condition Indicators Using a Web-Based Visualization Tool

Treesearch

Bob Tetrault; Bob Baldwin

2006-01-01

Global crop condition information for major agricultural regions in the world can be monitored using the web-based application called Crop Explorer. With this application, U.S. and international producers, traders, researchers, and the public can access remote sensing information used by agricultural economists and scientists who predict crop production worldwide. For...

Progress toward an advanced condition monitoring system for reusable rocket engines

NASA Technical Reports Server (NTRS)

Maram, J.; Barkhoudarian, S.

1987-01-01

A new generation of advanced sensor technologies will allow the direct measurement of critical/degradable rocket engine components' health and the detection of degraded conditions before component deterioration affects engine performance, leading to substantial improvements in reusable engines' operation and maintenance. When combined with a computer-based engine condition-monitoring system, these sensors can furnish a continuously updated data base for the prediction of engine availability and advanced warning of emergent maintenance requirements. Attention is given to the case of a practical turbopump and combustion device diagnostic/prognostic health-monitoring system.

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a method and system for monitoring and directly displaying the actual thrust produced by a jet aircraft engine under determined operating conditions and the available thrust and predicted (commanded) thrust of a functional model of an ideal engine under the same determined operating conditions. A first set of actual value output signals representative of a plurality of actual performance parameters of the engine under the determined operating conditions is generated and compared with a second set of predicted value output signals representative of the predicted value of corresponding performance parameters of a functional model of the engine under the determined operating conditions to produce a third set of difference value output signals within a range of normal, caution, or warning limit values. A thrust indicator displays when any one of the actual value output signals is in the warning range while shaping function means shape each of the respective difference output signals as each approaches the limit of the respective normal, caution, and warning range limits.

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai

Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought overmore » SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.« less

Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

NASA Astrophysics Data System (ADS)

Aghakouchak, Amir; Tourian, Mohammad J.

2015-04-01

Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014 California Drought will be presented. Further Reading: Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1:140001, 1-10, doi: 10.1038/sdata.2014.1.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

PubMed

Netterberg, Ida; Nielsen, Elisabet I; Friberg, Lena E; Karlsson, Mats O

2017-08-01

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Toward a categorical drought prediction system based on U.S. Drought Monitor (USDM) and climate forecast

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Xia, Youlong; Luo, Lifeng; Singh, Vijay P.; Ouyang, Wei; Hao, Fanghua

2017-08-01

Disastrous impacts of recent drought events around the world have led to extensive efforts in drought monitoring and prediction. Various drought information systems have been developed with different indicators to provide early drought warning. The climate forecast from North American Multimodel Ensemble (NMME) has been among the most salient progress in climate prediction and its application for drought prediction has been considerably growing. Since its development in 1999, the U.S. Drought Monitor (USDM) has played a critical role in drought monitoring with different drought categories to characterize drought severity, which has been employed to aid decision making by a wealth of users such as natural resource managers and authorities. Due to wide applications of USDM, the development of drought prediction with USDM drought categories would greatly aid decision making. This study presented a categorical drought prediction system for predicting USDM drought categories in the U.S., based on the initial conditions from USDM and seasonal climate forecasts from NMME. Results of USDM drought categories predictions in the U.S. demonstrate the potential of the prediction system, which is expected to contribute to operational early drought warning in the U.S.

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes.

PubMed

Zhou, Ming; Tang, Qi; Lang, Lixin; Xing, Jun; Tatsuoka, Kay

2018-04-17

In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. Copyright © 2018 John Wiley & Sons, Ltd.

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

DTIC Science & Technology

2014-10-02

condition monitoring , and equipment time to failure prediction in manufacturing 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 589...Component Level Health Monitoring and Prediction One of the characteristics of a self-aware machine is to be able to detect its components...the annual conference of the prognostics and health management society. Filzmoser, P., Garrett, R. G., & Reimann, C . (2005). Mul- tivariate outlier

PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH

EPA Science Inventory

Empirically derived values associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. However, resources limit the number, magnitude, and frequency of monitoring programs to gather these data. As su...

The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

USGS Publications Warehouse

Tadesse, T.; Wardlow, B.; Hayes, M.; Svoboda, M.; Brown, J.

2010-01-01

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.

Integrating multiple satellite data for crop monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing provides a valuable data source for detecting crop types, monitoring crop condition and predicting crop yields from space. Routine and continuous remote sensing data are critical for agricultural research and operational applications. Since crop field dimensions tend to be relatively ...

National Centers for Environmental Prediction (NCEP)

Science.gov Websites

Tropical Marine Fire Weather Forecast Maps Unified Surface Analysis Climate Climate Prediction Climate forecasts of hazardous flight conditions at all levels within domestic and international air space. Climate Prediction Center monitors and forecasts short-term climate fluctuations and provides information on the

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

Atmospheric Radiation Measurement program facilities newsletter, April 2002.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holdridge, D. J.

The National Oceanic and Atmospheric Administration (NOAA) recently announced the development of El Nino conditions in the tropical Pacific Ocean near the South American coastline. Scientists detected a 4 F increase in the sea-surface temperatures during February. Conrad C. Lautenbacher, NOAA administrator and Under Secretary of Commerce for Oceans and Atmosphere, indicated that this warming is a sign that the Pacific Ocean is heading toward an El Nino condition. Although it is too early to predict how strong the El Nino will become or the conditions it will bring to the United States, Lautenbacher said that the country is likelymore » to feel the effects as soon as midsummer (Figure 1). During the last El Nino in 1997-1998, the United States experienced strong weather impacts. Even though researchers don't understand what causes the onset of El Nino, they do recognize what to expect once development has begun. Scientists can monitor the development of El Nino through NOAA's advanced global climate monitoring system of polar-orbiting satellites and 72 ocean buoys moored across the equator in the Pacific Ocean. The resulting measurements of surface meteorological parameters and upper ocean temperatures are made available to scientists on a real-time basis, allowing for timely monitoring and predictions. This complex monitoring array enabled NOAA to predict the 1997-1998 El Nino six months in advance.« less

What Should We Monitor? Indicators of Human Disturbance and Ecological Impact

EPA Science Inventory

Ecological indicators are physical, chemical, and biological measures of environmental condition that change predictably with levels of human disturbance. Historically, indicators have been used to monitor and assess the status and trends of coastal waters and to diagnose the ma...

Expert monitoring and verbal feedback as sources of performance pressure.

PubMed

Buchanan, John J; Park, Inchon; Chen, Jing; Mehta, Ranjana K; McCulloch, Austin; Rhee, Joohyun; Wright, David L

2018-05-01

The influence of monitoring-pressure and verbal feedback on the performance of the intrinsically stable bimanual coordination patterns of in-phase and anti-phase was examined. The two bimanual patterns were produced under three conditions: 1) no-monitoring, 2) monitoring-pressure (viewed by experts), and 3) monitoring-pressure (viewed by experts) combined with verbal feedback emphasizing poor performance. The bimanual patterns were produced at self-paced movement frequencies. Anti-phase coordination was always less stable than in-phase coordination across all three conditions. When performed under conditions 2 and 3, both bimanual patterns were performed with less variability in relative phase across a wide range of self-paced movement frequencies compared to the no-monitoring condition. Thus, monitoring-pressure resulted in performance stabilization rather than degradation and the presence of verbal feedback had no impact on the influence of monitoring pressure. The current findings are inconsistent with the predictions of explicit monitoring theory; however, the findings are consistent with studies that have revealed increased stability for the system's intrinsic dynamics as a result of attentional focus and intentional control. The results are discussed within the contexts of the dynamic pattern theory of coordination, explicit monitoring theory, and action-focused theories as explanations for choking under pressure. Copyright © 2018. Published by Elsevier B.V.

Gauging climate change effects at local scales: weather-based indices to monitor insect harassment in caribou.

PubMed

Witter, Leslie A; Johnson, Chris J; Croft, Bruno; Gunn, Anne; Poirier, Lisa M

2012-09-01

Climate change is occurring at an accelerated rate in the Arctic. Insect harassment may be an important link between increased summer temperature and reduced body condition in caribou and reindeer (both Rangifer tarandus). To examine the effects of climate change at a scale relevant to Rangifer herds, we developed monitoring indices using weather to predict activity of parasitic insects across the central Arctic. During 2007-2009, we recorded weather conditions and used carbon dioxide baited traps to monitor activity of mosquitoes (Culicidae), black flies (Simuliidae), and oestrid flies (Oestridae) on the post-calving and summer range of the Bathurst barren-ground caribou (Rangifer tarandus groenlandicus) herd in Northwest Territories and Nunavut, Canada. We developed statistical models representing hypotheses about effects of weather, habitat, location, and temporal variables on insect activity. We used multinomial logistic regression to model mosquito and black fly activity, and logistic regression to model oestrid fly presence. We used information theory to select models to predict activity levels of insects. Using historical weather data, we used hindcasting to develop a chronology of insect activity on the Bathurst range from 1957 to 2008. Oestrid presence and mosquito and black fly activity levels were explained by temperature. Wind speed, light intensity, barometric pressure, relative humidity, vegetation, topography, location, time of day, and growing degree-days also affected mosquito and black fly levels. High predictive ability of all models justified the use of weather to index insect activity. Retrospective analyses indicated conditions favoring mosquito activity declined since the late 1950s, while predicted black fly and oestrid activity increased. Our indices can be used as monitoring tools to gauge potential changes in insect harassment due to climate change at scales relevant to caribou herds.

Monitoring and predicting shrink potential and future processing quality of potato tubers

USDA-ARS?s Scientific Manuscript database

Long-term storage of potato tubers increases risks, which are often attributed to shrink and quality loss. To minimize shrink and ensure high quality tubers, producers must closely monitor the condition of the crop during storage and make necessary adjustments to management plans. Evaluation procedu...

Anxiety and Error Monitoring: Increased Error Sensitivity or Altered Expectations?

ERIC Educational Resources Information Center

Compton, Rebecca J.; Carp, Joshua; Chaddock, Laura; Fineman, Stephanie L.; Quandt, Lorna C.; Ratliff, Jeffrey B.

2007-01-01

This study tested the prediction that the error-related negativity (ERN), a physiological measure of error monitoring, would be enhanced in anxious individuals, particularly in conditions with threatening cues. Participants made gender judgments about faces whose expressions were either happy, angry, or neutral. Replicating prior studies, midline…

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

Predictive maintenance of critical equipment in industrial processes

NASA Astrophysics Data System (ADS)

Hashemian, Hashem M.

This dissertation is an account of present and past research and development (R&D) efforts conducted by the author to develop and implement new technology for predictive maintenance and equipment condition monitoring in industrial processes. In particular, this dissertation presents the design of an integrated condition-monitoring system that incorporates the results of three current R&D projects with a combined funding of $2.8 million awarded to the author by the U.S. Department of Energy (DOE). This system will improve the state of the art in equipment condition monitoring and has applications in numerous industries including chemical and petrochemical plants, aviation and aerospace, electric power production and distribution, and a variety of manufacturing processes. The work that is presented in this dissertation is unique in that it introduces a new class of condition-monitoring methods that depend predominantly on the normal output of existing process sensors. It also describes current R&D efforts to develop data acquisition systems and data analysis algorithms and software packages that use the output of these sensors to determine the condition and health of industrial processes and their equipment. For example, the output of a pressure sensor in an operating plant can be used not only to indicate the pressure, but also to verify the calibration and response time of the sensor itself and identify anomalies in the process such as blockages, voids, and leaks that can interfere with accurate measurement of process parameters or disturb the plant's operation, safety, or reliability. Today, process data are typically collected at a rate of one sample per second (1 Hz) or slower. If this sampling rate is increased to 100 samples per second or higher, much more information can be extracted from the normal output of a process sensor and then used for condition monitoring, equipment performance measurements, and predictive maintenance. A fast analog-to-digital (A/D) converter can bring this about when it is combined with a large data storage unit to save the massive volume of data that will result from fast data sampling. Due to recent advances in electronics and computer technologies, fast A/Ds and large data storage units are now readily available and very affordable. Furthermore, advanced signal processing and interpretation techniques are readily available in commercial packages that provide fast Fourier transform (FFT) and wavelet analysis, correlation and cross-correlation results, application-specific neural networks, fuzzy data clustering, and other techniques to help arrive at test results quickly. When existing process sensors are not available to provide the necessary data, wireless sensors can be deployed to fill the gap. It is understood that wireless sensors are still evolving, but an assessment of these sensors performed under one of the R&D projects described in this dissertation shows that they are ready to play a positive role in equipment and process condition monitoring in industrial installations. Another class of predictive maintenance and condition-monitoring technologies now available is called by a number of names, such as "non-destructive examination," "non-destructive testing," "non-destructive inspection," or "in-service inspection" methods. These methods are not described in this dissertation as they are a separate discipline of their own and require a different set of skills to be implemented in an industrial process. They are nevertheless mentioned here to acknowledge their availability for predictive maintenance and to recognize their prominence as an important class of condition-monitoring techniques. These methods are used for detecting defects such as cracks, corrosion, and wear in metals, plastics, composites, ceramics, and other material except for wood and paper products. Some of these techniques are also used in medical diagnostics.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

Software framework for prognostic health monitoring of ocean-based power generation

NASA Astrophysics Data System (ADS)

Bowren, Mark

On August 5, 2010 the U.S. Department of Energy (DOE) has designated the Center for Ocean Energy Technology (COET) at Florida Atlantic University (FAU) as a national center for ocean energy research and development of prototypes for open-ocean power generation. Maintenance on ocean-based machinery can be very costly. To avoid unnecessary maintenance it is necessary to monitor the condition of each machine in order to predict problems. This kind of prognostic health monitoring (PHM) requires a condition-based maintenance (CBM) system that supports diagnostic and prognostic analysis of large amounts of data. Research in this field led to the creation of ISO13374 and the development of a standard open-architecture for machine condition monitoring. This thesis explores an implementation of such a system for ocean-based machinery using this framework and current open-standard technologies.

Main Pipelines Corrosion Monitoring Device

NASA Astrophysics Data System (ADS)

Anatoliy, Bazhenov; Galina, Bondareva; Natalia, Grivennaya; Sergey, Malygin; Mikhail, Goryainov

2017-01-01

The aim of the article is to substantiate the technical solution for the problem of monitoring corrosion changes in oil and gas pipelines with use (using) of an electromagnetic NDT method. Pipeline wall thinning under operating conditions can lead to perforations and leakage of the product to be transported outside the pipeline. In most cases there is danger for human life and environment. Monitoring of corrosion changes in pipeline inner wall under operating conditions is complicated because pipelines are mainly made of structural steels with conductive and magnetic properties that complicate test signal passage through the entire thickness of the object under study. The technical solution of this problem lies in monitoring of the internal corrosion changes in pipes under operating conditions in order to increase safety of pipelines by automated prediction of achieving the threshold pre-crash values due to corrosion.

Methods for evaluating stream, riparian, and biotic conditions

Treesearch

William S. Platts; Walter F. Megahan; G. Wayne Minshall

1983-01-01

This report develops a standard way of measuring stream, riparian, and biotic conditions and evaluates the validity of the measurements recommended. Accuracy and precision of most measurements are defined. This report will be of value to those persons documenting, monitoring, or predicting stream conditions and their biotic resources, especially those related to...

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

A predictive model for anti-degradation monitoring of the Delaware River mainstem

EPA Science Inventory

The non-tidal portion of the Delaware River can be considered to be in minimally disturbed condition, but there is increasing pressure on the watershed. Thus, the primary goal of this research was to develop a monitoring tool that can be used by the Delaware River Basin Commissi...

Measurement of semiochemical release rates with a dedicated environmental control system

Treesearch

Heping Zhu; Harold W. Thistle; Christopher M. Ranger; Hongping Zhou; Brian L. Strom

2015-01-01

Insect semiochemical dispensers are commonly deployed under variable environmental conditions over a specified period. Predictions of their longevity are hampered by a lack of methods to accurately monitor and predict how primary variables affect semiochemical release rate. A system was constructed to precisely determine semiochemical release rates under...

40 CFR 64.3 - Monitoring design criteria.

Code of Federal Regulations, 2013 CFR

2013-07-01

... indicators of emission control performance for the control device, any associated capture system and, if.... Indicators of performance may include, but are not limited to, direct or predicted emissions (including...(s) or designated condition(s) for the selected indicator(s) such that operation within the ranges...

Vehicle Dynamics Monitoring and Tracking System (VDMTS): Monitoring Mission Impacts in Support of Installation Land Management

DTIC Science & Technology

2012-06-01

transfer This report will be made accessible through the World Wide Web (WWW) at URLs: http://www.cecer.army.mil http://libweb.erdc.usace.army.mil...conditions (e.g., wetter or dryer conditions). Using the same live event tracking data, predictions can be made for vegetation loss in wet soils, even...WWW World Wide Web ERDC/CERL TR-12-11 107 References Ahlvin, R. B., and P. W. Haley. 1992. NATO reference mobility model edition II, NRMM II

Development of IoT-based Urban Sinkhole and Road Collapse Monitoring System

NASA Astrophysics Data System (ADS)

Jung, B.; Bang, E.; Lee, H. J.; Jeong, S. W.; Ryu, D.; Kim, S. W.; Kim, B. K.; Yum, B. W.; Lee, I. H.

2015-12-01

The consortium of Korean government-funded research institutes is developing IoT- (Internet of things) based underground safety monitoring and alerting system to manage risks arisen from land subsidence and road collapses in metropolitan areas in South Korea. The system consists of four major functional units: subsurface monitoring sensors sending data directly through the internet, centralized servers capable of collecting and processing big data, computational modules providing physical and statistical models for predicting high-risk areas, and geologic information service platforms visualizing underground safety maps for the public. The target urban area will be regionally covered by multi-sensors monitoring soil and groundwater conditions, and by high resolution satellite InSAR images filtering vertical land movements in a centimeter scale. Integrity of buried water supply and sewer lines are also monitored for the possibility of underground cavity formation. Once high-risk area is predicted, more tangible surveying methods such as ground penetrating radar (GPR) and resistivity survey can be applied for locating the cavities. Additionally, laboratory and field experiments are performed to understand overall road collapsing mechanism from the initial cavity creation to its progressive development depending on soil types, degree of compaction, and groundwater condition. Acquired results will update existing fully-coupled hydromechanical models for more accurate prediction of the collapsing-vulnerable area. Preliminary laboratory experiments show that the upward propagation of subsurface cavity is closely related to the soil properties, such as sand-clay ratios and moisture contents, and groundwater dynamics.

A Seamless Framework for Global Water Cycle Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sheffield, J.; Wood, E. F.; Chaney, N.; Fisher, C. K.; Caylor, K. K.

2013-12-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ('From Observations to Decisions') recognizes that 'water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity', and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the development of a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

Context cue focality influences strategic prospective memory monitoring.

PubMed

Hunter Ball, B; Bugg, Julie M

2018-02-12

Monitoring the environment for the occurrence of prospective memory (PM) targets is a resource-demanding process that produces cost (e.g., slower responding) to ongoing activities. However, research suggests that individuals are able to monitor strategically by using contextual cues to reduce monitoring in contexts in which PM targets are not expected to occur. In the current study, we investigated the processes supporting context identification (i.e., determining whether or not the context is appropriate for monitoring) by testing the context cue focality hypothesis. This hypothesis predicts that the ability to monitor strategically depends on whether the ongoing task orients attention to the contextual cues that are available to guide monitoring. In Experiment 1, participants performed an ongoing lexical decision task and were told that PM targets (TOR syllable) would only occur in word trials (focal context cue condition) or in items starting with consonants (nonfocal context cue condition). In Experiment 2, participants performed an ongoing first letter judgment (consonant/vowel) task and were told that PM targets would only occur in items starting with consonants (focal context cue condition) or in word trials (nonfocal context cue condition). Consistent with the context cue focality hypothesis, strategic monitoring was only observed during focal context cue conditions in which the type of ongoing task processing automatically oriented attention to the relevant features of the contextual cue. These findings suggest that strategic monitoring is dependent on limited-capacity processing resources and may be relatively limited when the attentional demands of context identification are sufficiently high.

[Prescribing monitoring in clinical practice: from enlightened empiricism to rational strategies].

PubMed

Buclin, Thierry; Herzig, Lilli

2013-05-15

Monitoring of a medical condition is the periodic measurement of one or several physiological or biological variables to detect a signal regarding its clinical progression or its response to treatment. We distinguish different medical situations between diagnostic, clinical and therapeutic process to apply monitoring. Many clinical, variables can be used for monitoring, once their intrinsic properties (normal range, critical difference, kinetics, reactivity) and external validity (pathophysiological importance, predictive power for clinical outcomes) are established. A formal conceptualization of monitoring is being developed and should support the rational development of monitoring strategies and their validation through appropriate clinical trials.

Benzene patterns in different urban environments and a prediction model for benzene rates based on NOx values

NASA Astrophysics Data System (ADS)

Paz, Shlomit; Goldstein, Pavel; Kordova-Biezuner, Levana; Adler, Lea

2017-04-01

Exposure to benzene has been associated with multiple severe impacts on health. This notwithstanding, at most monitoring stations, benzene is not monitored on a regular basis. The aims of the study were to compare benzene rates in different urban environments (region with heavy traffic and industrial region), to analyse the relationship between benzene and meteorological parameters in a Mediterranean climate type, to estimate the linkages between benzene and NOx and to suggest a prediction model for benzene rates based on NOx levels in order contribute to a better estimation of benzene. Data were used from two different monitoring stations, located on the eastern Mediterranean coast: 1) a traffic monitoring station in Tel Aviv, Israel (TLV) located in an urban region with heavy traffic; 2) a general air quality monitoring station in Haifa Bay (HIB), located in Israel's main industrial region. At each station, hourly, daily, monthly, seasonal, and annual data of benzene, NOx, mean temperature, relative humidity, inversion level, and temperature gradient were analysed over three years: 2008, 2009, and 2010. A prediction model for benzene rates based on NOx levels (which are monitored regularly) was developed to contribute to a better estimation of benzene. The severity of benzene pollution was found to be considerably higher at the traffic monitoring station (TLV) than at the general air quality station (HIB), despite the location of the latter in an industrial area. Hourly, daily, monthly, seasonal, and annual patterns have been shown to coincide with anthropogenic activities (traffic), the day of the week, and atmospheric conditions. A strong correlation between NOx and benzene allowed the development of a prediction model for benzene rates, based on NOx, the day of the week, and the month. The model succeeded in predicting the benzene values throughout the year (except for September). The severity of benzene pollution was found to be considerably higher at the traffic station (TLV) than at the general air quality station (HIB), despite being located in an industrial area. Hourly, daily, seasonal, and annual patterns of benzene rates have been shown to coincide with anthropogenic activities (traffic), day of the week, and atmospheric conditions. A prediction model for benzene rates was developed, based on NOx, the day of the week, and the month. The model suggested in this study might be useful for identifying potential risk of benzene in other urban environments.

Intelligent Performance Analysis with a Natural Language Interface

NASA Astrophysics Data System (ADS)

Juuso, Esko K.

2017-09-01

Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

Mapping the biological condition of USA rivers and streams

EPA Science Inventory

We predicted the probable (pr) biological condition (BC) of ~5.4 million km of stream within the conterminous USA (CONUS). National maps of prBC could provide an important tool for prioritizing monitoring and restoration of streams. The USEPA uses a spatially balanced survey desi...

Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland.

PubMed

Tolleson, D R; Schafer, D W

2014-01-01

Monitoring the nutritional status of range cows is difficult. Near-infrared spectroscopy (NIRS) of feces has been used to predict diet quality in cattle. When fecal NIRS is coupled with decision support software such as the Nutritional Balance Analyzer (NUTBAL PRO), nutritional status and animal performance can be monitored. Approximately 120 Hereford and 90 CGC composite (50% Red Angus, 25% Tarentaise, and 25% Charolais) cows grazing in a single herd were used in a study to determine the ability of fecal NIRS and NutbalPro to project BCS (1 = thin and 9 = fat) under commercial scale rangeland conditions in central Arizona. Cattle were rotated across the 31,000 ha allotment at 10 to 20 d intervals. Cattle BCS and fecal samples (approximately 500 g) composited from 5 to 10 cows were collected in the pasture approximately monthly at the midpoint of each grazing period. Samples were frozen and later analyzed by NIRS for prediction of diet crude protein (CP) and digestible organic matter (DOM). Along with fecal NIRS predicted diet quality, animal breed type, reproductive status, and environmental conditions were input to the software for each fecal sampling and BCS date. Three different evaluations were performed. First, fecal NIRS and NutbalPro derived BCS was projected forward from each sampling as if it were a "one-time only" measurement. Second, BCS was derived from the average predicted weight change between 2 sampling dates for a given period. Third, inputs to the model were adjusted to better represent local animals and conditions. Fecal NIRS predicted diet quality varied from a minimum of approximately 5% CP and 57% DOM in winter to a maximum of approximately 11% CP and 60% DOM in summer. Diet quality correlated with observed seasonal changes and precipitation events. In evaluation 1, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.1 to 1.1 BCS in Herefords and 0.0 to 0.9 in CGC. In evaluation 2, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.00 to 0.46 in Hereford and 0.00 to 0.67 in CGC. In evaluation 3, the range of differences between observed and projected BCS was 0.04 to 0.28. The greatest difference in projected versus observed BCS occurred during periods of lowest diet quality. Body condition was predicted accurately enough to be useful in monitoring the nutrition of range beef cows under the conditions of this study.

Evaluation of relative response factor methodology for demonstrating attainment of ozone in Houston, Texas.

PubMed

Vizuete, William; Biton, Leiran; Jeffries, Harvey E; Couzo, Evan

2010-07-01

In 2007, the U.S. Environmental Protection Agency (EPA) released guidance on demonstrating attainment of the federal ozone (O3) standard. This guidance recommended a change in the use of air quality model (AQM) predictions from an absolute to a relative way. This was accomplished by using a ratio, and not the absolute difference of AQM O3 predictions from a historical year to an attainment year. This ratio of O3 concentrations, labeled the relative response factor (RRF), is multiplied by an average of observed concentrations at every monitor. In this analysis, whether the methodology used to calculate RRFs is severing the source-receptor relationship for a given monitor was investigated. Model predictions were generated with a regulatory AQM system used to support the 2004 Houston-Galveston-Brazoria State Implementation Plan. Following the procedures in the EPA guidance, an attainment demonstration was completed using regulatory AQM predictions and measurements from the Houston ground-monitoring network. Results show that the model predictions used for the RRF calculation were often based on model conditions that were geographically remote from observations and counter to wind flow. Many of the monitors used the same model predictions for an RRF, even if that O3 plume did not impact it. The RRF methodology resulted in severing the true source-receptor relationship for a monitor. This analysis also showed that model performance could influence RRF values, and values at monitoring sites appear to be sensitive to model bias. Results indicate an inverse linear correlation of RRFs with model bias at each monitor (R2 = 0.47), resulting in a change in future O3 design values up to 5 parts per billion (ppb). These results suggest that the application of RRF methodology in Houston, TX, should be changed from using all model predictions above 85 ppb to a method that removes any predictions that are not relevant to the observed source-receptor relationship.

Drought: A comprehensive R package for drought monitoring, prediction and analysis

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang

2015-04-01

Drought may impose serious challenges to human societies and ecosystems. Due to complicated causing effects and wide impacts, a universally accepted definition of drought does not exist. The drought indicator is commonly used to characterize drought properties such as duration or severity. Various drought indicators have been developed in the past few decades for the monitoring of a certain aspect of drought condition along with the development of multivariate drought indices for drought characterizations from multiple sources or hydro-climatic variables. Reliable drought prediction with suitable drought indicators is critical to the drought preparedness plan to reduce potential drought impacts. In addition, drought analysis to quantify the risk of drought properties would provide useful information for operation drought managements. The drought monitoring, prediction and risk analysis are important components in drought modeling and assessments. In this study, a comprehensive R package "drought" is developed to aid the drought monitoring, prediction and risk analysis (available from R-Forge and CRAN soon). The computation of a suite of univariate and multivariate drought indices that integrate drought information from various sources such as precipitation, temperature, soil moisture, and runoff is available in the drought monitoring component in the package. The drought prediction/forecasting component consists of statistical drought predictions to enhance the drought early warning for decision makings. Analysis of drought properties such as duration and severity is also provided in this package for drought risk assessments. Based on this package, a drought monitoring and prediction/forecasting system is under development as a decision supporting tool. The package will be provided freely to the public to aid the drought modeling and assessment for researchers and practitioners.

Prognostics using Engineering and Environmental Parameters as Applied to State of Health (SOH) Radionuclide Aerosol Sampler Analyzer (RASA) Real-Time Monitoring

NASA Astrophysics Data System (ADS)

Hutchenson, K. D.; Hartley-McBride, S.; Saults, T.; Schmidt, D. P.

2006-05-01

The International Monitoring System (IMS) is composed in part of radionuclide particulate and gas monitoring systems. Monitoring the operational status of these systems is an important aspect of nuclear weapon test monitoring. Quality data, process control techniques, and predictive models are necessary to detect and predict system component failures. Predicting failures in advance provides time to mitigate these failures, thus minimizing operational downtime. The Provisional Technical Secretariat (PTS) requires IMS radionuclide systems be operational 95 percent of the time. The United States National Data Center (US NDC) offers contributing components to the IMS. This effort focuses on the initial research and process development using prognostics for monitoring and predicting failures of the RASA two (2) days into the future. The predictions, using time series methods, are input to an expert decision system, called SHADES (State of Health Airflow and Detection Expert System). The results enable personnel to make informed judgments about the health of the RASA system. Data are read from a relational database, processed, and displayed to the user in a GIS as a prototype GUI. This procedure mimics the real time application process that could be implemented as an operational system, This initial proof-of-concept effort developed predictive models focused on RASA components for a single site (USP79). Future work shall include the incorporation of other RASA systems, as well as their environmental conditions that play a significant role in performance. Similarly, SHADES currently accommodates specific component behaviors at this one site. Future work shall also include important environmental variables that play an important part of the prediction algorithms.

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems.

PubMed

Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang

2014-01-01

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.

Sensors and systems for space applications: a methodology for developing fault detection, diagnosis, and recovery

NASA Astrophysics Data System (ADS)

Edwards, John L.; Beekman, Randy M.; Buchanan, David B.; Farner, Scott; Gershzohn, Gary R.; Khuzadi, Mbuyi; Mikula, D. F.; Nissen, Gerry; Peck, James; Taylor, Shaun

2007-04-01

Human space travel is inherently dangerous. Hazardous conditions will exist. Real time health monitoring of critical subsystems is essential for providing a safe abort timeline in the event of a catastrophic subsystem failure. In this paper, we discuss a practical and cost effective process for developing critical subsystem failure detection, diagnosis and response (FDDR). We also present the results of a real time health monitoring simulation of a propellant ullage pressurization subsystem failure. The health monitoring development process identifies hazards, isolates hazard causes, defines software partitioning requirements and quantifies software algorithm development. The process provides a means to establish the number and placement of sensors necessary to provide real time health monitoring. We discuss how health monitoring software tracks subsystem control commands, interprets off-nominal operational sensor data, predicts failure propagation timelines, corroborate failures predictions and formats failure protocol.

Challenges in sensor development for the electric utility industry

NASA Astrophysics Data System (ADS)

Ward, Barry H.

1999-01-01

The electric utility industry is reducing operating costs in order to prepare for deregulation. The reduction in operating cost has meant a reduction in manpower. The ability to utilize remaining maintenance staff more effectively and to stay competitive in a deregulated environment has therefore become critical. In recent years, the industry has moved away from routine or periodic maintenance to predictive or condition based maintenance. This requires the assessment of equipment condition by frequent testing and inspection; a requirement that is incompatible with cost reduction. To overcome this dilemma, industry trends are toward condition monitoring, whereby the health of apparatus is monitored continuously. This requires the installation of sensors hr transducers on power equipment and the data taken forwarded to an intelligent device for further processing. These devices then analyze the data and make evaluations based on parameter levels or trends, in an attempt to predict possible deterioration. This continuous monitoring allows the electric utility to schedule maintenance on an as needed basis. The industry has been faced with many challenges in sensor design. The measurement of physical, chemical and electrical parameters under extreme conditions of electric fields, magnetic fields, temperature, corrosion, etc. is extensive. This paper will give an overview of these challenges and the solutions adopted for apparatus such as power transformers, circuit breakers, boilers, cables, batteries, and rotating machinery.

Virginia Water Resources: Utilizing NASA Earth Observations to Monitor the Extent of Harmful Algal Blooms in Virginia Rivers

NASA Astrophysics Data System (ADS)

Lubkin, S. H.; Morgan, C.

2015-12-01

Harmful algal bloom species have had an increasing ecological impact on the Chesapeake Bay Watershed where they disrupt water chemistry, kill fish and cause human illness. In Virginia, scientists from Virginia Institute of Marine Science and Old Dominion University monitor HABs and their effect on water quality; however, these groups lack a method to monitor HABs in real time. This limits the ability to document associated water quality conditions and predict future blooms. Band reflectance values from Landsat 8 Surface Reflectance data (USGS Earth Explorer) and MODIS Chlorophyll imagery (NOAA CoastWatch) were cross calibrated to create a regression model that calculated concentrations of chlorophyll. Calculations were verified with in situ measurements from the Virginia Estuarine and Coastal Observing System. Imagery produced with the Chlorophyll-A calculation model will allow VIMS and ODU scientists to assess the timing, magnitude, duration and frequency of HABs in Virginia's Chesapeake watershed and to predict the environmental and water quality conditions that favor bloom development.

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Lee H., Jr. (Inventor)

1988-01-01

The invention is believed a major improvement that will have a broad application in governmental and commercial aviation. It provides a dynamic method and system for monitoring and simultaneously displaying in easily scanned form the available, predicted, and actual thrust of a jet aircraft engine under actual operating conditions. The available and predicted thrusts are based on the performance of a functional model of the aircraft engine under the same operating conditions. Other critical performance parameters of the aircraft engine and functional model are generated and compared, the differences in value being simultaneously displayed in conjunction with the displayed thrust values. Thus, the displayed information permits the pilot to make power adjustments directly while keeping him aware of total performance at a glance of a single display panel.

Predicting Near-Term Water Quality from Satellite Observations of Watershed Conditions

NASA Astrophysics Data System (ADS)

Weiss, W. J.; Wang, L.; Hoffman, K.; West, D.; Mehta, A. V.; Lee, C.

2017-12-01

Despite the strong influence of watershed conditions on source water quality, most water utilities and water resource agencies do not currently have the capability to monitor watershed sources of contamination with great temporal or spatial detail. Typically, knowledge of source water quality is limited to periodic grab sampling; automated monitoring of a limited number of parameters at a few select locations; and/or monitoring relevant constituents at a treatment plant intake. While important, such observations are not sufficient to inform proactive watershed or source water management at a monthly or seasonal scale. Satellite remote sensing data on the other hand can provide a snapshot of an entire watershed at regular, sub-monthly intervals, helping analysts characterize watershed conditions and identify trends that could signal changes in source water quality. Accordingly, the authors are investigating correlations between satellite remote sensing observations of watersheds and source water quality, at a variety of spatial and temporal scales and lags. While correlations between remote sensing observations and direct in situ measurements of water quality have been well described in the literature, there are few studies that link remote sensing observations across a watershed with near-term predictions of water quality. In this presentation, the authors will describe results of statistical analyses and discuss how these results are being used to inform development of a desktop decision support tool to support predictive application of remote sensing data. Predictor variables under evaluation include parameters that describe vegetative conditions; parameters that describe climate/weather conditions; and non-remote sensing, in situ measurements. Water quality parameters under investigation include nitrogen, phosphorus, organic carbon, chlorophyll-a, and turbidity.

DESIGNS FOR THE FUTURE: BRIDGING THE GAP BETWEEN ASSESSMENT OF CONDITION AND DIAGNOSIS OF IMPAIRMENT

EPA Science Inventory

As the EPA, states, and tribes move towards a consolidated assessment and listing process to satisfy requirements of the Clean Water Act, multi-purpose monitoring designs will be needed to assess regional condition as well as predict site-specific probabilities of impairment. Th...

SMALL AREA ESTIMATION OF INDICATORS OF STREAM CONDITION FOR MAIA USING HIERARCHICAL BAYES PREDICTION MODELS

EPA Science Inventory

Probability surveys of stream and river resources (hereafter referred to as streams) provide reliable estimates of stream condition when the areas for the estimates have sufficient number of sample sites. Monitoring programs are frequently asked to provide estimates for areas th...

Second language proficiency modulates conflict-monitoring in an oculomotor Stroop task: evidence from Hindi-English bilinguals

PubMed Central

Singh, Niharika; Mishra, Ramesh K.

2013-01-01

Many studies have confirmed the presence of a bilingual advantage which is manifested as enhanced cognitive and attention control. However, very few studies have investigated the role of second language proficiency on the modulation of conflict-monitoring in bilinguals. We investigated this by comparing high and low proficient Hindi-English bilinguals on a modified saccadic arrow Stroop task under different monitoring conditions, and tested the predictions of the bilingual executive control advantage proposal. The task of the participants was to make an eye movement toward the color patch in the same color as the central arrow, ignoring the patch to which the arrow was pointing. High-proficient bilinguals had overall faster saccade latency on all types of trials as compared to the low proficient bilinguals. The overall saccadic latency for high proficiency bilinguals was similarly affected by the different types of monitoring conditions, whereas conflict resolution advantage was found only for high monitoring demanding condition. The results support a conflict-monitoring account in a novel oculomotor task and also suggest that language proficiency could modulate executive control in bilinguals. PMID:23781210

Monitoring and forecasting the 2009-2010 severe drought in Southwest China

NASA Astrophysics Data System (ADS)

Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

2015-12-01

From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating

PubMed Central

Lee, Young-Joo; Cho, Soojin

2016-01-01

Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed. PMID:26950125

The strategic control of prospective memory monitoring in response to complex and probabilistic contextual cues.

PubMed

Bugg, Julie M; Ball, B Hunter

2017-07-01

Participants use simple contextual cues to reduce deployment of costly monitoring processes in contexts in which prospective memory (PM) targets are not expected. This study investigated whether this strategic monitoring pattern is observed in response to complex and probabilistic contextual cues. Participants performed a lexical decision task in which words or nonwords were presented in upper or lower locations on screen. The specific condition was informed that PM targets ("tor" syllable) would occur only in words in the upper location, whereas the nonspecific condition was informed that targets could occur in any location or word type. Context was blocked such that word type and location changed every 8 trials. In Experiment 1, the specific condition used the complex contextual cue to reduce monitoring in unexpected contexts relative to the nonspecific condition. This pattern largely was not evidenced when the complex contextual cue was probabilistic (Experiment 2). Experiment 3 confirmed that strategic monitoring is observed for a complex cue that is deterministic, but not one that is probabilistic. Additionally, Experiments 1 and 3 demonstrated a disadvantage associated with strategic monitoring-namely, that the specific condition was less likely to respond to a PM target in an unexpected context. Experiment 3 provided evidence that this disadvantage is attributable to impaired noticing of the target. The novel findings suggest use of a complex contextual cue per se is not a boundary condition for the strategic, context-specific allocation of monitoring processes to support prospective remembering; however, strategic monitoring is constrained by the predictive utility of the complex contextual cue.

Prediction of human core body temperature using non-invasive measurement methods.

PubMed

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

Method for detecting gas turbine engine flashback

DOEpatents

Singh, Kapil Kumar; Varatharajan, Balachandar; Kraemer, Gilbert Otto; Yilmaz, Ertan; Lacy, Benjamin Paul

2012-09-04

A method for monitoring and controlling a gas turbine, comprises predicting frequencies of combustion dynamics in a combustor using operating conditions of a gas turbine, receiving a signal from a sensor that is indicative of combustion dynamics in the combustor, and detecting a flashback if a frequency of the received signal does not correspond to the predicted frequencies.

Intelligent monitoring and control of semiconductor manufacturing equipment

NASA Technical Reports Server (NTRS)

Murdock, Janet L.; Hayes-Roth, Barbara

1991-01-01

The use of AI methods to monitor and control semiconductor fabrication in a state-of-the-art manufacturing environment called the Rapid Thermal Multiprocessor is described. Semiconductor fabrication involves many complex processing steps with limited opportunities to measure process and product properties. By applying additional process and product knowledge to that limited data, AI methods augment classical control methods by detecting abnormalities and trends, predicting failures, diagnosing, planning corrective action sequences, explaining diagnoses or predictions, and reacting to anomalous conditions that classical control systems typically would not correct. Research methodology and issues are discussed, and two diagnosis scenarios are examined.

The Global Integrated Drought Monitoring and Prediction System (GIDMaPS): Overview and Capabilities

NASA Astrophysics Data System (ADS)

AghaKouchak, A.; Hao, Z.; Farahmand, A.; Nakhjiri, N.

2013-12-01

Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness and management. Motivated by the Global Drought Information Systems (GDIS) activities, this paper presents the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which provides near real-time drought information using both remote sensing observations and model simulations. The monthly data from the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-Land), North American Land Data Assimilation System (NLDAS), and remotely sensed precipitation data are used as input to GIDMaPS. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is based on two input data sets (MERRA and NLDAS) and three drought indicators (SPI, SSI and MSDI). The drought prediction model provides the empirical probability of drought for different severity levels. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The results indicate that GIDMaPS advances our drought monitoring and prediction capabilities through integration of multiple data and indicators.

An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2014-01-01

This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

The Challenges of Developing a Framework for Global Water Cycle Monitoring and Prediction (Alfred Wegener Medal Lecture)

NASA Astrophysics Data System (ADS)

Wood, Eric F.

2014-05-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ("From Observations to Decisions") recognizes that "water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity", and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the developments at Princeton University towards a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. At every step there are scientific challenges whose solutions are only partially being solved. In addition there are challenges in delivering such systems as "climate services", especially to societies with low technical capacity such as rural agriculturalists in sub-Saharan Africa, but whose needs for such information are great. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

PubMed Central

Li, Xiaoqing; Wang, Yu

2018-01-01

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. PMID:29351254

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

PubMed

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

Developing Watershed Level Indicators for Predicting Aquatic Condition in Stream Networks

EPA Science Inventory

Landscape level information is critical for resource managers to monitor, assess and prioritize protection and restoration efforts within individual watersheds. Spatial landscape indicators incorporate information on natural infrastructure (undeveloped and vegetated riparian area...

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.

PubMed

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-05-31

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring.

PubMed

Joch, Michael; Hegele, Mathias; Maurer, Heiko; Müller, Hermann; Maurer, Lisa Katharina

2017-07-01

The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring. NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite. Copyright © 2017 the American Physiological Society.

The Cubesat Heliospheric Imaging Experiment for Space Weather Prediction

NASA Astrophysics Data System (ADS)

DeForest, Craig; Howard, T.; Dickinson, J.; Epperly, M.; Kief, C.

2010-05-01

Heliospheric imaging data have been shown to improve space weather prediction by an order of magnitude, and heliospheric monitoring by the SMEI and STEREO-HI instruments have proven to be extremely useful for understanding heliospheric conditions near Earth. However, SMEI is approaching end-of-life and the STEREOs are drifting away from favorable Earth-viewing geometry just as the new solar cycle begins. CHIME is an innovative, miniaturized, fully functional space weather heliospheric monitor that fits within the 3U CubeSat envelope and can be flown individually (as a scientific or demonstrator mission) or in a swarm (to attain operational-class reliability) at a small fraction of the cost of a conventional mission. Here we describe the CHIME concept and its use with the automated processing pipeline AICMED to improve space weather prediction.

Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan.

PubMed

Kashima, Saori; Yorifuji, Takashi; Sawada, Norie; Nakaya, Tomoki; Eboshida, Akira

2018-08-01

Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO 2 ) based on routine and campaign monitoring data obtained from an urban area. We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO 2 concentrations from each model with measured annual average NO 2 concentrations from evaluation sites. The routine-LUR-All and routine-LUR-BS models both predicted NO 2 concentrations well: adjusted R 2 =0.68 and 0.76, respectively, and root mean square error=3.4 and 2.1ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO 2 concentrations at evaluation sites. Although the predicted NO 2 concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city. The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization of microwave-assisted extraction (MAP) for ginseng components by response surface methodology.

PubMed

Kwon, Joong-Ho; Bélanger, Jacqueline M R; Paré, J R Jocelyn

2003-03-26

Response surface methodology (RSM) was applied to predict optimum conditions for microwave-assisted extraction-a MAP technology-of saponin components from ginseng roots. A central composite design was used to monitor the effect of ethanol concentration (30-90%, X(1)) and extraction time (30-270 s, X(2)) on dependent variables, such as total extract yield (Y(1)), crude saponin content (Y(2)), and saponin ratio (Y(3)), under atmospheric pressure conditions when focused microwaves were applied at an emission frequency of 2450 MHz. In MAP under pre-established conditions, correlation coefficients (R (2)) of the models for total extract yield and crude saponin were 0.9841 (p < 0.001) and 0.9704 (p < 0.01). Optimum extraction conditions were predicted for each variable as 52.6% ethanol and 224.7 s in extract yield and as 77.3% ethanol and 295.1 s in crude saponins, respectively. Estimated maximum values at predicted optimum conditions were in good agreement with experimental values.

Context-dependent survival, fecundity and predicted population-level consequences of brucellosis in African buffalo

USGS Publications Warehouse

Gorsich, Erin E.; Ezenwa, Vanessa O.; Cross, Paul C.; Bengis, Roy G.; Jolles, Anna E.

2015-01-01

Our results suggest that brucellosis infection can potentially result in reduced population growth rates, but because these effects varied with demographic and environmental conditions, they may remain unseen without intensive, longitudinal monitoring.

Many-objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering and Evolutionary Optimization

NASA Astrophysics Data System (ADS)

Kollat, J. B.; Reed, P. M.

2009-12-01

This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem formulation provides a tremendous amount of information for decision makers. Specifically our many-objective analysis highlights the limitations and potentially negative design consequences of traditional single and two-objective problem formulations. These consequences become apparent through visual exploration of high-dimensional tradeoffs and the identification of regions with interesting compromise solutions. The prediction characteristics of these compromise designs are explored in detail, as well as their implications for subsequent design decisions in both space and time.

Unraveling past impacts of climate change and land management on historic peatland development using proxy-based reconstruction, monitoring data and process modeling.

PubMed

Heinemeyer, Andreas; Swindles, Graeme T

2018-05-08

Peatlands represent globally significant soil carbon stores that have been accumulating for millennia under water-logged conditions. However, deepening water-table depths (WTD) from climate change or human-induced drainage could stimulate decomposition resulting in peatlands turning from carbon sinks to carbon sources. Contemporary WTD ranges of testate amoebae (TA) are commonly used to predict past WTD in peatlands using quantitative transfer function models. Here we present, for the first time, a study comparing TA-based WTD reconstructions to instrumentally monitored WTD and hydrological model predictions using the MILLENNIA peatland model to examine past peatland responses to climate change and land management. Although there was very good agreement between monitored and modeled WTD, TA-reconstructed water table was consistently deeper. Predictions from a larger European TA transfer function data set were wetter, but the overall directional fit to observed WTD was better for a TA transfer function based on data from northern England. We applied a regression-based offset correction to the reconstructed WTD for the validation period (1931-2010). We then predicted WTD using available climate records as MILLENNIA model input and compared the offset-corrected TA reconstruction to MILLENNIA WTD predictions over an extended period (1750-1931) with available climate reconstructions. Although the comparison revealed striking similarities in predicted overall WTD patterns, particularly for a recent drier period (1965-1995), there were clear periods when TA-based WTD predictions underestimated (i.e. drier during 1830-1930) and overestimated (i.e. wetter during 1760-1830) past WTD compared to MILLENNIA model predictions. Importantly, simulated grouse moor management scenarios may explain the drier TA WTD predictions, resulting in considerable model predicted carbon losses and reduced methane emissions, mainly due to drainage. This study demonstrates the value of a site-specific and combined data-model validation step toward using TA-derived moisture conditions to understand past climate-driven peatland development and carbon budgets alongside modeling likely management impacts. © 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Individualized Behavioral Health Monitoring Tool

NASA Technical Reports Server (NTRS)

Mollicone, Daniel

2015-01-01

Behavioral health risks during long-duration space exploration missions are among the most difficult to predict, detect, and mitigate. Given the anticipated extended duration of future missions and their isolated, extreme, and confined environments, there is the possibility that behavior conditions and mental disorders will develop among astronaut crew. Pulsar Informatics, Inc., has developed a health monitoring tool that provides a means to detect and address behavioral disorders and mental conditions at an early stage. The tool integrates all available behavioral measures collected during a mission to identify possible health indicator warning signs within the context of quantitatively tracked mission stressors. It is unobtrusive and requires minimal crew time and effort to train and utilize. The monitoring tool can be deployed in space analog environments for validation testing and ultimate deployment in long-duration space exploration missions.

AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana.

PubMed

Frost, Kali D

2014-03-01

An evaluation of the steady-state dispersion model AERMOD was conducted to determine its accuracy at predicting hourly ground-level concentrations of sulfur dioxide (SO2) by comparing model-predicted concentrations to a full year of monitored SO2 data. The two study sites are comprised of three coal-fired electrical generating units (EGUs) located in southwest Indiana. The sites are characterized by tall, buoyant stacks,flat terrain, multiple SO2 monitors, and relatively isolated locations. AERMOD v12060 and AERMOD v12345 with BETA options were evaluated at each study site. For the six monitor-receptor pairs evaluated, AERMOD showed generally good agreement with monitor values for the hourly 99th percentile SO2 design value, with design value ratios that ranged from 0.92 to 1.99. AERMOD was within acceptable performance limits for the Robust Highest Concentration (RHC) statistic (RHC ratios ranged from 0.54 to 1.71) at all six monitors. Analysis of the top 5% of hourly concentrations at the six monitor-receptor sites, paired in time and space, indicated poor model performance in the upper concentration range. The amount of hourly model predicted data that was within a factor of 2 of observations at these higher concentrations ranged from 14 to 43% over the six sites. Analysis of subsets of data showed consistent overprediction during low wind speed and unstable meteorological conditions, and underprediction during stable, low wind conditions. Hourly paired comparisons represent a stringent measure of model performance; however given the potential for application of hourly model predictions to the SO2 NAAQS design value, this may be appropriate. At these two sites, AERMOD v12345 BETA options do not improve model performance. A regulatory evaluation of AERMOD utilizing quantile-quantile (Q-Q) plots, the RHC statistic, and 99th percentile design value concentrations indicates that model performance is acceptable according to widely accepted regulatory performance limits. However, a scientific evaluation examining hourly paired monitor and model values at concentrations of interest indicates overprediction and underprediction bias that is outside of acceptable model performance measures. Overprediction of 1-hr SO2 concentrations by AERMOD presents major ramifications for state and local permitting authorities when establishing emission limits.

Availability analysis of mechanical systems with condition-based maintenance using semi-Markov and evaluation of optimal condition monitoring interval

NASA Astrophysics Data System (ADS)

Kumar, Girish; Jain, Vipul; Gandhi, O. P.

2018-03-01

Maintenance helps to extend equipment life by improving its condition and avoiding catastrophic failures. Appropriate model or mechanism is, thus, needed to quantify system availability vis-a-vis a given maintenance strategy, which will assist in decision-making for optimal utilization of maintenance resources. This paper deals with semi-Markov process (SMP) modeling for steady state availability analysis of mechanical systems that follow condition-based maintenance (CBM) and evaluation of optimal condition monitoring interval. The developed SMP model is solved using two-stage analytical approach for steady-state availability analysis of the system. Also, CBM interval is decided for maximizing system availability using Genetic Algorithm approach. The main contribution of the paper is in the form of a predictive tool for system availability that will help in deciding the optimum CBM policy. The proposed methodology is demonstrated for a centrifugal pump.

Multitasking During Degraded Speech Recognition in School-Age Children

PubMed Central

Ward, Kristina M.; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children’s multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children’s accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children’s dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children’s proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition. PMID:28105890

Multitasking During Degraded Speech Recognition in School-Age Children.

PubMed

Grieco-Calub, Tina M; Ward, Kristina M; Brehm, Laurel

2017-01-01

Multitasking requires individuals to allocate their cognitive resources across different tasks. The purpose of the current study was to assess school-age children's multitasking abilities during degraded speech recognition. Children (8 to 12 years old) completed a dual-task paradigm including a sentence recognition (primary) task containing speech that was either unprocessed or noise-band vocoded with 8, 6, or 4 spectral channels and a visual monitoring (secondary) task. Children's accuracy and reaction time on the visual monitoring task was quantified during the dual-task paradigm in each condition of the primary task and compared with single-task performance. Children experienced dual-task costs in the 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the visual monitoring task relative to baseline performance. In all conditions, children's dual-task performance on the visual monitoring task was strongly predicted by their single-task (baseline) performance on the task. Results suggest that children's proficiency with the secondary task contributes to the magnitude of dual-task costs while multitasking during degraded speech recognition.

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

PubMed Central

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-01-01

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

A space weather forecasting system with multiple satellites based on a self-recognizing network.

PubMed

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-05-05

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

Sensitivity of Hypoxia Predictions for the Northern Gulf of Mexico to Sediment Oxygen Consumption and Model Nesting

NASA Astrophysics Data System (ADS)

Fennel, Katja; Hu, Jiatang; Laurent, Arnaud; Marta-Almeida, Martinho; Hetland, Robert

2014-05-01

Interannual variations of the hypoxic area that develops every summer over the Texas-Louisiana Shelf are large. The 2008 Action Plan put forth by an alliance of multiple state and federal agencies and tribes calls for a decrease of the hypoxic area through nutrient management in the watershed. Realistic models help build mechanistic understanding of the processes underlying hypoxia formation and are thus indispensable for devising efficient nutrient reduction strategies. Here we present such a model, evaluate its hypoxia predictions against monitoring observations and assess the sensitivity of hypoxia predictions to model resolution, variations in sediment oxygen consumption and choice of physical horizontal boundary conditions. We find that hypoxia predictions on the shelf are very sensitive to the parameterization of sediment oxygen consumption, a result of the fact that hypoxic conditions are restricted to a relatively thin layer above the bottom over most of the shelf. We also show that the strength of vertical stratification is an important predictor of oxygen concentration in bottom waters and that modification of physical horizontal boundary conditions can have a large effect on hypoxia predictions.

Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird

PubMed Central

Milenkaya, Olga; Catlin, Daniel H.; Legge, Sarah; Walters, Jeffrey R.

2015-01-01

Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population (e.g., breeding stage) such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous interpretation of condition indices as surrogates for individual quality and fitness. PMID:26305457

Modulation of the error-related negativity by response conflict.

PubMed

Danielmeier, Claudia; Wessel, Jan R; Steinhauser, Marco; Ullsperger, Markus

2009-11-01

An arrow version of the Eriksen flanker task was employed to investigate the influence of conflict on the error-related negativity (ERN). The degree of conflict was modulated by varying the distance between flankers and the target arrow (CLOSE and FAR conditions). Error rates and reaction time data from a behavioral experiment were used to adapt a connectionist model of this task. This model was based on the conflict monitoring theory and simulated behavioral and event-related potential data. The computational model predicted an increased ERN amplitude in FAR incompatible (the low-conflict condition) compared to CLOSE incompatible errors (the high-conflict condition). A subsequent ERP experiment confirmed the model predictions. The computational model explains this finding with larger post-response conflict in far trials. In addition, data and model predictions of the N2 and the LRP support the conflict interpretation of the ERN.

Spectral variations of canopy reflectance in support of precision agriculture

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi; Borisova, Denitsa; Nikolov, Hristo

2014-05-01

Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth observations. It supplies precise, reliable and valuable information on current crop condition and growth processes. In agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the seasonal spectral behavior of different species and their relation to crop vigor. For this reason, we performed ground-based study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in main ontogenetic periods. Phenology and agro-specific relationships allow assessing crop condition during different portions of the growth cycle and thus effectively tracking plant development, and finally make yield predictions. The applicability of a number of vegetation indices (VIs) for monitoring crop seasonal dynamics, its health condition, and yield potential was examined. Special emphasis we put on narrow-band indices as the availability of data from hyperspectral imagers is unavoidable future. The temporal behavior of vegetation indices revealed increased sensitivity to crop growth. The derived spectral-biophysical relationships allowed extraction of quantitative information about crop variables and yield at different stages of the phenological development. Relating plant spectral and biophysical variables in a phenology-based manner allows crop monitoring, that is crop diagnosis and predictions to be performed multiple times during plant ontogenesis. During active vegetative periods spectral data was highly indicative of plant growth trends and yield potential. The VIs values contributed to reliable yield prediction and showed very good correspondence with the estimates from biophysical models. For dates before full maturity most of the examined VIs proved to be meaningful statistical predictors of crop state-indicative biophysical variables. High correlations were obtained for canopy cover fraction, LAI, and biomass. Sensitivity to red, near-infrared and green reflectance showed both vigorous and stressed plants. As crops attained advanced growth stages, decreased sensitivity of VIs and weaker correlations with bioparameters were observed, yet still significant in a statistical sense. The results highlight the capability of the presented approach to track the dynamics of crop growth from multitemporal spectral data, and illustrate the prediction accuracy of the spectral models. The results are useful in assessing the efficiency of various spectral band ratios and other vegetation indices often used in remote sensing studies of natural and agricultural vegetation. They suggest that the used algorithm for data processing is particularly suitable for airborne cropland monitoring and could be expanded to sites at farm or municipality scale. The results reported are from pilot study carried out on a plot located in one of the established polygons for experimental crop monitoring. In the mentioned research GIS database is established for supporting the experiments and modelling process. Recommendations on good farming practices for medium sized farms for monitoring stress conditions such as drought and overfertilizing are developed.

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles.

PubMed

Wen, Dongqi; Zhai, Wenjuan; Xiang, Sheng; Hu, Zhice; Wei, Tongchuan; Noll, Kenneth E

2017-11-01

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO 2 concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models-CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)-are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations. The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.

Towards a Seamless Framework for Drought Analysis and Prediction from Seasonal to Climate Change Time Scales (Plinius Medal Lecture)

NASA Astrophysics Data System (ADS)

Sheffield, Justin

2013-04-01

Droughts arguably cause the most impacts of all natural hazards in terms of the number of people affected and the long-term economic costs and ecosystem stresses. Recent droughts worldwide have caused humanitarian and economic problems such as food insecurity across the Horn of Africa, agricultural economic losses across the central US and loss of livelihoods in rural western India. The prospect of future increases in drought severity and duration driven by projected changes in precipitation patterns and increasing temperatures is worrisome. Some evidence for climate change impacts on drought is already being seen for some regions, such as the Mediterranean and east Africa. Mitigation of the impacts of drought requires advance warning of developing conditions and enactment of drought plans to reduce vulnerability. A key element of this is a drought early warning system that at its heart is the capability to monitor evolving hydrological conditions and water resources storage, and provide reliable and robust predictions out to several months, as well as the capacity to act on this information. At longer time scales, planning and policy-making need to consider the potential impacts of climate change and its impact on drought risk, and do this within the context of natural climate variability, which is likely to dominate any climate change signal over the next few decades. There are several challenges that need to be met to advance our capability to provide both early warning at seasonal time scales and risk assessment under climate change, regionally and globally. Advancing our understanding of drought predictability and risk requires knowledge of drought at all time scales. This includes understanding of past drought occurrence, from the paleoclimate record to the recent past, and understanding of drought mechanisms, from initiation, through persistence to recovery and translation of this understanding to predictive models. Current approaches to monitoring and predicting drought are limited in many parts of the world, and especially in developing countries where national capacity is limited. Evaluation of past droughts and their mechanisms is limited by data availability and especially before the instrumental period of the last 50-100 years, for which there is reliance on incomplete spatial proxy data, such as tree rings. Seasonal predictability is currently mainly limited to tropical and sub-tropical regions through connections with sea surface temperature variations such as ENSO. Predictability in mid-latitudes is low and especially for precipitation, although dynamical model predictions appear to be edging statistical models in many aspects of seasonal prediction. This presentation describes ongoing research on evaluation of drought risk and drought mechanisms at regional to global scales with the eventual goal of developing a seamless monitoring and prediction framework at all time scales. Such a framework would allow consistent assessment of drought from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental global drought monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of drought variability over the 20th century, which forms the background climatology to which current conditions can be assessed and drought mechanisms can be diagnosed. Future drought risk is quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. Current research is focused on several aspects, including: 1) quantifying the uncertainties in historic drought reconstructions; 2) analysis of drought propagation through the coupled hydrological/vegetation system; 3) the utility of new data sources such as on the ground sensors and new satellite products for terrestrial hydrology and vegetation, for improved monitoring and prediction, especially in poorly observed regions; 4) advancing predictive skill for all aspects of drought occurrence through diagnosis of the driving mechanisms and feedbacks of historic droughts; and 5) quantification and reduction of uncertainties in future projections of drought under climate change. The steps towards the development of a seamless framework for analysis and prediction in the context of this research are discussed.

A Wearable Cardiac Monitor for Long-Term Data Acquisition and Analysis

PubMed Central

Winokur, Eric S.; Delano, Maggie K.; Sodini, Charles G.

2015-01-01

A low-power wearable ECG monitoring system has been developed entirely from discrete electronic components and a custom PCB. This device removes all loose wires from the system and minimizes the footprint on the user. The monitor consists of five electrodes, which allow a cardiologist to choose from a variety of possible projections. Clinical tests to compare our wearable monitor with a commercial clinical ECG recorder are conducted on ten healthy adults under different ambulatory conditions, with nine of the datasets used for analysis. Data from both monitors were synchronized and annotated with PhysioNet's waveform viewer WAVE (physionet.org) [1]. All gold standard annotations are compared to the results of the WQRS detection algorithm [2] provided by PhysioNet. QRS sensitivity and QRS positive predictability are extracted from both monitors to validate the wearable monitor. PMID:22968205

Experiments with the living dead: Plants as monitors and recorders of Biosphere Geosphere interactions.

NASA Astrophysics Data System (ADS)

Lomax, Barry; Fraser, Wesley

2016-04-01

Understanding variations in the Earth's climate history will enhance our understanding of and capacity to predict future climate change. Importantly this information can then be used to reduce uncertainty around future climate change predictions. However to achieve this, it is necessary to develop well constrained and robustly tested palaeo-proxies. Plants are innately coupled to the atmosphere requiring both sunlight and CO2 to drive photosynthesis and carbon assimilation. When combined with their resilience and persistence, the study of plant responses to climate change in concert with the analysis of fossil plants offer the opportunity to monitor past atmospheric conditions and infer palaeoclimate change. In this presentation we highlight how this approach is leading to the development of mechanistic palaeoproxies tested on palaeobotanically relevant extant species showing that plant fossils can be used as both monitors and geochemical recorders of atmospheric changes.

Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose Ramon; Biswas, Gautam; Goebel, Kai

2012-01-01

This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

PubMed Central

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-01-01

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL. PMID:27258277

Acoustic monitoring of the tide height and slope-water intrusion at the New Jersey Shelf in winter conditions.

PubMed

Turgut, Altan; Orr, Marshall; Pasewark, Bruce

2007-05-01

Waveguide invariant theory is used to describe the frequency shifts of constant acoustic intensity level curves in broadband signal spectrograms measured at the New Jersey Shelf during the winter of 2003. The broadband signals (270-330 Hz) were transmitted from a fixed source and received at three fixed receivers, located at 10, 20, and 30 km range along a cross-shelf propagation track. The constant acoustic intensity level curves of the received signals indicate regular frequency shifts that can be well predicted by the change in water depth observed through tens of tidal cycles. A second pattern of frequency shifts is observed at only 30 km range where significant variability of slope-water intrusion was measured. An excellent agreement between observed frequency shifts of the constant acoustic intensity levels and those predicted by the change in tide height and slope water elevations suggests the capability of long-term acoustic monitoring of tide and slope water intrusions in winter conditions.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

PubMed Central

Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

2015-01-01

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

PubMed

Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

2015-09-15

The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H. (Inventor)

1989-01-01

The invention is a real-time takeoff and landing performance monitoring system which provides the pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V sub R) within the safe zone of the runway or stopping the aircraft on the runway after landing or take off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. An important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in headwind occurring as the takeoff roll progresses. The system displays the position of the airplane on the runway, indicating runway used and runway available, summarizes the critical information into a situation advisory flag, flags engine failures and off-nominal acceleration performance, and indicates where on the runway particular events such as decision speed (V sub 1), rotation speed (V sub R) and expected stop points will occur based on actual or predicted performance. The display also indicates airspeed, wind vector, engine pressure ratios, second segment climb speed, and balanced field length (BFL). The system detects performance deficiencies by comparing the airplane's present performance with a predicted nominal performance based upon the given conditions.

Development of a decision support system for monitoring, reporting and forecasting ecological conditions of the Appalachian Trail

USGS Publications Warehouse

Wang, Yeqiao; Nemani, Ramakrishna; Dieffenbach, Fred; Stolte, Kenneth; Holcomb, Glenn B.; Robinson, Matt; Reese, Casey C.; McNiff, Marcia; Duhaime, Roland; Tierney, Geri; Mitchell, Brian; August, Peter; Paton, Peter; LaBash, Charles

2010-01-01

This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decisionmaking on management of the A.T. by providing a coherent framework for data integration, status reporting and trend analysis. The A.T. MEGA-Transect DSS is to integrate NASA multi-platform sensor data and modeling through the Terrestrial Observation and Prediction System (TOPS) and in situ measurements from A.T. MEGA-Transect partners to address identified natural resource priorities and improve resource management decisions.

A water marker monitored by satellites to predict seasonal endemic cholera.

PubMed

Jutla, Antarpreet; Akanda, Ali Shafqat; Huq, Anwar; Faruque, Abu Syed Golam; Colwell, Rita; Islam, Shafiqul

2013-01-01

The ability to predict an occurrence of cholera, a water-related disease, offers a significant public health advantage. Satellite based estimates of chlorophyll, a surrogate for plankton abundance, have been linked to cholera incidence. However, cholera bacteria can survive under a variety of coastal ecological conditions, thus constraining the predictive ability of the chlorophyll, since it provides only an estimate of greenness of seawater. Here, a new remote sensing based index is proposed: Satellite Water Marker (SWM), which estimates condition of coastal water, based on observed variability in the difference between blue (412 nm) and green (555 nm) wavelengths that can be related to seasonal cholera incidence. The index is bounded between physically separable wavelengths for relatively clear (blue) and turbid (green) water. Using SWM, prediction of cholera with reasonable accuracy, with at least two month in advance, can potentially be achieved in the endemic coastal regions.

WRF model forecasts and their use for hydroclimate monitoring over southern South America

NASA Astrophysics Data System (ADS)

Muller, Omar; Lovino, Miguel; Berbery, E. Hugo

2017-04-01

Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF model simulations at 15 km grid spacing over a domain that encompasses La Plata Basin (LPB) in southern South America, where agriculture and water resources are essential. The model's skill up to a lead-time of 7 days is evaluated with daily precipitation and 2m temperature in-situ observations. Results show high prediction performance with 7 days lead-time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. The scores tend to be better over humid climates than over arid-to-semiarid climates. Compared to the arid-semiarid climate, the humid climate has a higher probability of detection and less false alarms. The ranges of the skill scores are similar to those found over the United States, suggesting that proper choice of parameterizations lead to no loss of performance of the model. Daily mean, minimum and maximum forecast temperatures are highly correlated with observations up to 7 day lead time. The best performance is for daily mean temperature, followed by minimum temperature and a slightly weaker performance for maximum temperature over arid regions. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration and runoff. This evaluation validates the model's usefulness to fore-cast weather up to one week and to monitor climate conditions in real time. The scores suggest that the forecast lead-time can be extended into week two, while bias correction methods can reduce part of the systematic errors.

Optimal Performance Monitoring of Hybrid Mid-Infrared Wavelength MIMO Free Space Optical and RF Wireless Networks in Fading Channels

NASA Astrophysics Data System (ADS)

Schmidt, Barnet Michael

An optimal performance monitoring metric for a hybrid free space optical and radio-frequency (RF) wireless network, the Outage Capacity Objective Function, is analytically developed and studied. Current and traditional methods of performance monitoring of both optical and RF wireless networks are centered on measurement of physical layer parameters, the most common being signal-to-noise ratio, error rate, Q factor, and eye diagrams, occasionally combined with link-layer measurements such as data throughput, retransmission rate, and/or lost packet rate. Network management systems frequently attempt to predict or forestall network failures by observing degradations of these parameters and to attempt mitigation (such as offloading traffic, increasing transmitter power, reducing the data rate, or combinations thereof) prior to the failure. These methods are limited by the frequent low sensitivity of the physical layer parameters to the atmospheric optical conditions (measured by optical signal-to-noise ratio) and the radio frequency fading channel conditions (measured by signal-to-interference ratio). As a result of low sensitivity, measurements of this type frequently are unable to predict impending failures sufficiently in advance for the network management system to take corrective action prior to the failure. We derive and apply an optimal measure of hybrid network performance based on the outage capacity of the hybrid optical and RF channel, the outage capacity objective function. The objective function provides high sensitivity and reliable failure prediction, and considers both the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The radio frequency segment analysis considers the three most common RF channel fading statistics: Rayleigh, Ricean, and Nakagami-m. The novel application of information theory to the underlying physics of the gamma-gamma optical channel and radio fading channels in determining the joint hybrid channel outage capacity provides the best performance estimate under any given set of operating conditions. It is shown that, unlike traditional physical layer performance monitoring techniques, the objective function based upon the outage capacity of the hybrid channel at any combination of OSNR and SIR, is able to predict channel degradation and failure well in advance of the actual outage. An outage in the information-theoretic definition occurs when the offered load exceeds the outage capacity under the current conditions of OSNR and SIR. The optical channel is operated at the "long" mid-infrared wavelength of 10000 nm. which provides improved resistance to scattering compared to shorter wavelengths such as 1550 nm.

Towards generalised reference condition models for environmental assessment: a case study on rivers in Atlantic Canada.

PubMed

Armanini, D G; Monk, W A; Carter, L; Cote, D; Baird, D J

2013-08-01

Evaluation of the ecological status of river sites in Canada is supported by building models using the reference condition approach. However, geography, data scarcity and inter-operability constraints have frustrated attempts to monitor national-scale status and trends. This issue is particularly true in Atlantic Canada, where no ecological assessment system is currently available. Here, we present a reference condition model based on the River Invertebrate Prediction and Classification System approach with regional-scale applicability. To achieve this, we used biological monitoring data collected from wadeable streams across Atlantic Canada together with freely available, nationally consistent geographic information system (GIS) environmental data layers. For the first time, we demonstrated that it is possible to use data generated from different studies, even when collected using different sampling methods, to generate a robust predictive model. This model was successfully generated and tested using GIS-based rather than local habitat variables and showed improved performance when compared to a null model. In addition, ecological quality ratio data derived from the model responded to observed stressors in a test dataset. Implications for future large-scale implementation of river biomonitoring using a standardised approach with global application are presented.

Qualitative and temporal reasoning in engine behavior analysis

NASA Technical Reports Server (NTRS)

Dietz, W. E.; Stamps, M. E.; Ali, M.

1987-01-01

Numerical simulation models, engine experts, and experimental data are used to generate qualitative and temporal representations of abnormal engine behavior. Engine parameters monitored during operation are used to generate qualitative and temporal representations of actual engine behavior. Similarities between the representations of failure scenarios and the actual engine behavior are used to diagnose fault conditions which have already occurred, or are about to occur; to increase the surveillance by the monitoring system of relevant engine parameters; and to predict likely future engine behavior.

Neutronic analysis of the 1D and 1E banks reflux detection system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanchard, A.

1999-12-21

Two H Canyon neutron monitoring systems for early detection of postulated abnormal reflux conditions in the Second Uranium Cycle 1E and 1D Mixer-Settle Banks have been designed and built. Monte Carlo neutron transport simulations using the general purpose, general geometry, n-particle MCNP code have been performed to model expected response of the monitoring systems to varying conditions.The confirmatory studies documented herein conclude that the 1E and 1D neutron monitoring systems are able to achieve adequate neutron count rates for various neutron source and detector configurations, thereby eliminating excessive integration count time. Neutron count rate sensitivity studies are also performed. Conversely,more » the transport studies concluded that the neutron count rates are statistically insensitive to nitric acid content in the aqueous region and to the transition region length. These studies conclude that the 1E and 1D neutron monitoring systems are able to predict the postulated reflux conditions for all examined perturbations in the neutron source and detector configurations. In the cases examined, the relative change in the neutron count rates due to postulated transitions from normal {sup 235}U concentration levels to reflux levels remain satisfactory detectable.« less

System and Method for Providing Model-Based Alerting of Spatial Disorientation to a Pilot

NASA Technical Reports Server (NTRS)

Johnson, Steve (Inventor); Conner, Kevin J (Inventor); Mathan, Santosh (Inventor)

2015-01-01

A system and method monitor aircraft state parameters, for example, aircraft movement and flight parameters, applies those inputs to a spatial disorientation model, and makes a prediction of when pilot may become spatially disoriented. Once the system predicts a potentially disoriented pilot, the sensitivity for alerting the pilot to conditions exceeding a threshold can be increased and allow for an earlier alert to mitigate the possibility of an incorrect control input.

Effect of age on the performance of bispectral and entropy indices during sevoflurane pediatric anesthesia: a pharmacometric study.

PubMed

Sciusco, Alberto; Standing, Joseph F; Sheng, Yucheng; Raimondo, Pasquale; Cinnella, Gilda; Dambrosio, Michele

2017-04-01

Bispectral index (BIS) and entropy monitors have been proposed for use in children, but research has not supported their validity for infants. However, effective monitoring of young children may be even more important than for adults, to aid appropriate anesthetic dosing and reduce the chance of adverse consequences. This prospective study aimed to investigate the relationships between age and the predictive performance of BIS and entropy monitors in measuring the anesthetic drug effects within a pediatric surgery setting. We concurrently recorded BIS and entropy (SE/RE) in 48 children aged 1 month-12 years, undergoing general anesthesia with sevoflurane and fentanyl. Nonlinear mixed effects modeling was used to characterize the concentration-response relationship independently between the three monitor indicators with sevoflurane. The model's goodness-of-fit was assessed by prediction-corrected visual predictive checks. Model fit with age was evaluated using absolute conditional individual weighted residuals (|CIWRES|). The ability of BIS and entropy monitors to describe the effect of anesthesia was compared with prediction probabilities (P K ) in different age groups. Intraoperative and awakening values were compared in the age groups. The correlation between BIS and entropy was also calculated. |CIWRES| vs age showed an increasing trend in the model's accuracy for all three indicators. P K probabilities were similar for all three indicators within each age group, though lower in infants. The linear correlations between BIS and entropy in different age groups were lower for infants. Infants also tended to have lower values during surgery and at awakening than older children, while toddlers had higher values. Performance of both monitors improves as age increases. Our results suggest a need for the development of new monitor algorithms or calibration to better account for the age-specific EEG dynamics of younger patients. © 2017 John Wiley & Sons Ltd.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

PubMed

Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-25

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Monitoring results and analysis of thermal comfort conditions in experimental buildings for different heating systems and ventilation regimes during heating and cooling seasons

NASA Astrophysics Data System (ADS)

Gendelis, S.; Jakovičs, A.; Ratnieks, J.; Bandeniece, L.

2017-10-01

This paper focuses on the long-term monitoring of thermal comfort and discomfort parameters in five small test buildings equipped with different heating and cooling systems. Calculations of predicted percentage of dissatisfied people (PPD) index and discomfort factors are provided for the room in winter season running three different heating systems - electric heater, air-air heat pump and air-water heat pump, as well as for the summer cooling with split type air conditioning systems. It is shown that the type of heating/cooling system and its working regime has an important impact on thermal comfort conditions in observed room. Recommendations for the optimal operating regimes and choice of the heating system from the thermal comfort point of view are summarized.

Prognostics and Health Monitoring: Application to Electric Vehicles

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a predicted nominal performance based upon given conditions, performance deficiencies are detected by the system.

Real-Time Impact Visualization Inspection of Aerospace Composite Structures with Distributed Sensors.

PubMed

Si, Liang; Baier, Horst

2015-07-08

For the future design of smart aerospace structures, the development and application of a reliable, real-time and automatic monitoring and diagnostic technique is essential. Thus, with distributed sensor networks, a real-time automatic structural health monitoring (SHM) technique is designed and investigated to monitor and predict the locations and force magnitudes of unforeseen foreign impacts on composite structures and to estimate in real time mode the structural state when impacts occur. The proposed smart impact visualization inspection (IVI) technique mainly consists of five functional modules, which are the signal data preprocessing (SDP), the forward model generator (FMG), the impact positioning calculator (IPC), the inverse model operator (IMO) and structural state estimator (SSE). With regard to the verification of the practicality of the proposed IVI technique, various structure configurations are considered, which are a normal CFRP panel and another CFRP panel with "orange peel" surfaces and a cutout hole. Additionally, since robustness against several background disturbances is also an essential criterion for practical engineering demands, investigations and experimental tests are carried out under random vibration interfering noise (RVIN) conditions. The accuracy of the predictions for unknown impact events on composite structures using the IVI technique is validated under various structure configurations and under changing environmental conditions. The evaluated errors all fall well within a satisfactory limit range. Furthermore, it is concluded that the IVI technique is applicable for impact monitoring, diagnosis and assessment of aerospace composite structures in complex practical engineering environments.

Real-Time Impact Visualization Inspection of Aerospace Composite Structures with Distributed Sensors

PubMed Central

Si, Liang; Baier, Horst

2015-01-01

For the future design of smart aerospace structures, the development and application of a reliable, real-time and automatic monitoring and diagnostic technique is essential. Thus, with distributed sensor networks, a real-time automatic structural health monitoring (SHM) technique is designed and investigated to monitor and predict the locations and force magnitudes of unforeseen foreign impacts on composite structures and to estimate in real time mode the structural state when impacts occur. The proposed smart impact visualization inspection (IVI) technique mainly consists of five functional modules, which are the signal data preprocessing (SDP), the forward model generator (FMG), the impact positioning calculator (IPC), the inverse model operator (IMO) and structural state estimator (SSE). With regard to the verification of the practicality of the proposed IVI technique, various structure configurations are considered, which are a normal CFRP panel and another CFRP panel with “orange peel” surfaces and a cutout hole. Additionally, since robustness against several background disturbances is also an essential criterion for practical engineering demands, investigations and experimental tests are carried out under random vibration interfering noise (RVIN) conditions. The accuracy of the predictions for unknown impact events on composite structures using the IVI technique is validated under various structure configurations and under changing environmental conditions. The evaluated errors all fall well within a satisfactory limit range. Furthermore, it is concluded that the IVI technique is applicable for impact monitoring, diagnosis and assessment of aerospace composite structures in complex practical engineering environments. PMID:26184196

Assessing the effects of underground mining activities on high-voltage overhead power lines

NASA Astrophysics Data System (ADS)

Gusev, Vladimir; Zhuravlyov, Alexei; Maliukhina, Elena

2017-11-01

This paper introduces a technique for predictive assessment of changes in the position of power transmission towers and condition of overhead power lines, located in the zone of influence of displacements and deformations of the Earth's surface caused by mining activities. A special approach for monitoring the technical condition of towers and cables is proposed. It is intended to address the issue of controlling the condition of transmission lines that are under the influence of underground mining activities and to checkmate such impact.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Raymond H.; Stone, James; Truax, Ryan

Batch tests, column tests, and predictive reactive transport modeling can be done before ISR begins as part of the decision making/permitting process by bracketing possible post-restoration conditions; Help address stakeholder concerns; The best predictions require actual restored groundwater in contact with the downgradient solid phase; Resulting modeling provides a range of natural attenuation rates and assists with designing the best locations and time frames for continued monitoring; Field pilot tests are the best field-scale data and can provide the best model input and calibration data

On-line condition monitoring applications in nuclear power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hastiemian, H. M.; Feltus, M. A.

2006-07-01

Existing signals from process instruments in nuclear power plants can be sampled while the plant is operating and analyzed to verify the static and dynamic performance of process sensors, identify process-to-sensor problems, detect instrument anomalies such as venturi fouling, measure the vibration of the reactor vessel and its internals, or detect thermal hydraulic anomalies within the reactor coolant system. These applications are important in nuclear plants to satisfy a variety of objectives such as: 1) meeting the plant technical specification requirements; 2) complying with regulatory regulations; 3) guarding against equipment and process degradation; 4) providing a means for incipient failuremore » detection and predictive maintenance; or 5) identifying the root cause of anomalies in equipment and plant processes. The technologies that are used to achieve these objectives are collectively referred to as 'on-line condition monitoring.' This paper presents a review of key elements of these technologies, provides examples of their use in nuclear power plants, and illustrates how they can be integrated into an on-line condition monitoring system for nuclear power plants. (authors)« less

Electrical Resistance Technique to Monitor SiC Composite Detection

NASA Technical Reports Server (NTRS)

Smith, Craig; Morscher, Gregory; Xia, Zhenhai

2008-01-01

Ceramic matrix composites are suitable for high temperature structural applications such as turbine airfoils and hypersonic thermal protection systems. The employment of these materials in such applications is limited by the ability to process components reliable and to accurately monitor and predict damage evolution that leads to failure under stressed-oxidation conditions. Current nondestructive methods such as ultrasound, x-ray, and thermal imaging are limited in their ability to quantify small scale, transverse, in-plane, matrix cracks developed over long-time creep and fatigue conditions. Electrical resistance of SiC/SiC composites is one technique that shows special promise towards this end. Since both the matrix and the fibers are conductive, changes in matrix or fiber properties should relate to changes in electrical conductivity along the length of a specimen or part. The effect of matrix cracking on electrical resistivity for several composite systems will be presented and some initial measurements performed at elevated temperatures under stress-rupture conditions. The implications towards electrical resistance as a technique applied to composite processing, damage detection (health monitoring), and life-modeling will be discussed.

Prediction of the containment of HIV infection by antiretroviral therapy - a variable structure control approach.

PubMed

Anelone, Anet J N; Spurgeon, Sarah K

2017-02-01

It is demonstrated that the reachability paradigm from variable structure control theory is a suitable framework to monitor and predict the progression of the human immunodeficiency virus (HIV) infection following initiation of antiretroviral therapy (ART). A manifold is selected which characterises the infection-free steady-state. A model of HIV infection together with an associated reachability analysis is used to formulate a dynamical condition for the containment of HIV infection on the manifold. This condition is tested using data from two different HIV clinical trials which contain measurements of the CD4+ T cell count and HIV load in the peripheral blood collected from HIV infected individuals for the six month period following initiation of ART. The biological rates of the model are estimated using the multi-point identification method and data points collected in the initial period of the trial. Using the parameter estimates and the numerical solutions of the model, the predictions of the reachability analysis are shown to be consistent with the clinical diagnosis at the conclusion of the trial. The methodology captures the dynamical characteristics of eventual successful, failed and marginal outcomes. The findings evidence that the reachability analysis is an appropriate tool to monitor and develop personalised antiretroviral treatment.

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

NASA Astrophysics Data System (ADS)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

Hybrid Modeling Improves Health and Performance Monitoring

NASA Technical Reports Server (NTRS)

2007-01-01

Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

Estimating population extinction thresholds with categorical classification trees for Louisiana black bears

USGS Publications Warehouse

Laufenberg, Jared S.; Clark, Joseph D.; Chandler, Richard B.

2018-01-01

Monitoring vulnerable species is critical for their conservation. Thresholds or tipping points are commonly used to indicate when populations become vulnerable to extinction and to trigger changes in conservation actions. However, quantitative methods to determine such thresholds have not been well explored. The Louisiana black bear (Ursus americanus luteolus) was removed from the list of threatened and endangered species under the U.S. Endangered Species Act in 2016 and our objectives were to determine the most appropriate parameters and thresholds for monitoring and management action. Capture mark recapture (CMR) data from 2006 to 2012 were used to estimate population parameters and variances. We used stochastic population simulations and conditional classification trees to identify demographic rates for monitoring that would be most indicative of heighted extinction risk. We then identified thresholds that would be reliable predictors of population viability. Conditional classification trees indicated that annual apparent survival rates for adult females averaged over 5 years () was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when , suggesting that this statistic can be used as threshold to trigger management intervention. Our evaluation produced monitoring protocols that reliably predicted population persistence and was cost-effective. We conclude that population projections and conditional classification trees can be valuable tools for identifying extinction thresholds used in monitoring programs.

Estimating population extinction thresholds with categorical classification trees for Louisiana black bears.

PubMed

Laufenberg, Jared S; Clark, Joseph D; Chandler, Richard B

2018-01-01

Monitoring vulnerable species is critical for their conservation. Thresholds or tipping points are commonly used to indicate when populations become vulnerable to extinction and to trigger changes in conservation actions. However, quantitative methods to determine such thresholds have not been well explored. The Louisiana black bear (Ursus americanus luteolus) was removed from the list of threatened and endangered species under the U.S. Endangered Species Act in 2016 and our objectives were to determine the most appropriate parameters and thresholds for monitoring and management action. Capture mark recapture (CMR) data from 2006 to 2012 were used to estimate population parameters and variances. We used stochastic population simulations and conditional classification trees to identify demographic rates for monitoring that would be most indicative of heighted extinction risk. We then identified thresholds that would be reliable predictors of population viability. Conditional classification trees indicated that annual apparent survival rates for adult females averaged over 5 years ([Formula: see text]) was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when [Formula: see text], suggesting that this statistic can be used as threshold to trigger management intervention. Our evaluation produced monitoring protocols that reliably predicted population persistence and was cost-effective. We conclude that population projections and conditional classification trees can be valuable tools for identifying extinction thresholds used in monitoring programs.

Use of Continuous Monitors and Autosamplers to Predict Unmeasured Water-Quality Constituents in Tributaries of the Tualatin River, Oregon

USGS Publications Warehouse

Anderson, Chauncey W.; Rounds, Stewart A.

2010-01-01

Management of water quality in streams of the United States is becoming increasingly complex as regulators seek to control aquatic pollution and ecological problems through Total Maximum Daily Load programs that target reductions in the concentrations of certain constituents. Sediment, nutrients, and bacteria, for example, are constituents that regulators target for reduction nationally and in the Tualatin River basin, Oregon. These constituents require laboratory analysis of discrete samples for definitive determinations of concentrations in streams. Recent technological advances in the nearly continuous, in situ monitoring of related water-quality parameters has fostered the use of these parameters as surrogates for the labor intensive, laboratory-analyzed constituents. Although these correlative techniques have been successful in large rivers, it was unclear whether they could be applied successfully in tributaries of the Tualatin River, primarily because these streams tend to be small, have rapid hydrologic response to rainfall and high streamflow variability, and may contain unique sources of sediment, nutrients, and bacteria. This report evaluates the feasibility of developing correlative regression models for predicting dependent variables (concentrations of total suspended solids, total phosphorus, and Escherichia coli bacteria) in two Tualatin River basin streams: one draining highly urbanized land (Fanno Creek near Durham, Oregon) and one draining rural agricultural land (Dairy Creek at Highway 8 near Hillsboro, Oregon), during 2002-04. An important difference between these two streams is their response to storm runoff; Fanno Creek has a relatively rapid response due to extensive upstream impervious areas and Dairy Creek has a relatively slow response because of the large amount of undeveloped upstream land. Four other stream sites also were evaluated, but in less detail. Potential explanatory variables included continuously monitored streamflow (discharge), stream stage, specific conductance, turbidity, and time (to account for seasonal processes). Preliminary multiple-regression models were identified using stepwise regression and Mallow's Cp, which maximizes regression correlation coefficients and accounts for the loss of additional degrees of freedom when extra explanatory variables are used. Several data scenarios were created and evaluated for each site to assess the representativeness of existing monitoring data and autosampler-derived data, and to assess the utility of the available data to develop robust predictive models. The goodness-of-fit of candidate predictive models was assessed with diagnostic statistics from validation exercises that compared predictions against a subset of the available data. The regression modeling met with mixed success. Functional model forms that have a high likelihood of success were identified for most (but not all) dependent variables at each site, but there were limitations in the available datasets, notably the lack of samples from high-flows. These limitations increase the uncertainty in the predictions of the models and suggest that the models are not yet ready for use in assessing these streams, particularly under high-flow conditions, without additional data collection and recalibration of model coefficients. Nonetheless, the results reveal opportunities to use existing resources more efficiently. Baseline conditions are well represented in the available data, and, for the most part, the models reproduced these conditions well. Future sampling might therefore focus on high flow conditions, without much loss of ability to characterize the baseline. Seasonal cycles, as represented by trigonometric functions of time, were not significant in the evaluated models, perhaps because the baseline conditions are well characterized in the datasets or because the other explanatory variables indirectly incorporate seasonal aspects. Multicollinearity among independent variabl

Model complexity in carbon sequestration:A design of experiment and response surface uncertainty analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Li, S.

2014-12-01

Geologic carbon sequestration (GCS) is proposed for the Nugget Sandstone in Moxa Arch, a regional saline aquifer with a large storage potential. For a proposed storage site, this study builds a suite of increasingly complex conceptual "geologic" model families, using subsets of the site characterization data: a homogeneous model family, a stationary petrophysical model family, a stationary facies model family with sub-facies petrophysical variability, and a non-stationary facies model family (with sub-facies variability) conditioned to soft data. These families, representing alternative conceptual site models built with increasing data, were simulated with the same CO2 injection test (50 years at 1/10 Mt per year), followed by 2950 years of monitoring. Using the Design of Experiment, an efficient sensitivity analysis (SA) is conducted for all families, systematically varying uncertain input parameters. Results are compared among the families to identify parameters that have 1st order impact on predicting the CO2 storage ratio (SR) at both end of injection and end of monitoring. At this site, geologic modeling factors do not significantly influence the short-term prediction of the storage ratio, although they become important over monitoring time, but only for those families where such factors are accounted for. Based on the SA, a response surface analysis is conducted to generate prediction envelopes of the storage ratio, which are compared among the families at both times. Results suggest a large uncertainty in the predicted storage ratio given the uncertainties in model parameters and modeling choices: SR varies from 5-60% (end of injection) to 18-100% (end of monitoring), although its variation among the model families is relatively minor. Moreover, long-term leakage risk is considered small at the proposed site. In the lowest-SR scenarios, all families predict gravity-stable supercritical CO2 migrating toward the bottom of the aquifer. In the highest-SR scenarios, supercritical CO2 footprints are relatively insignificant by the end of monitoring.

Mapping forest conditions: past, present, and future

Treesearch

Maggi Kelly

2017-01-01

Mapping and mapped data have always been critical to public land managers and researchers for identifying and characterizing wildlife habitat across scales, monitoring species and habitat change, and predicting and planning future scenarios. Maps and mapping protocols are often incorporated into wildlife and habitat management plans, as is the case with the California...

Monitoring a local extreme weather event with the scope of hyperspectral sounding

NASA Astrophysics Data System (ADS)

Satapathy, Jyotirmayee; Jangid, Buddhi Prakash

2018-06-01

Operational space-based hyperspectral Infrared sounders retrieve atmospheric temperature and humidity profiles from the measured radiances. These sounders like Atmospheric InfraRed Sounder, Infrared Atmospheric Sounding Interferometer as well as INSAT-3D sounders on geostationary orbit have proved to be very successful in providing these retrievals on global and regional scales, respectively, with good enough spatio-temporal resolutions and are well competent with that of traditional profiles from radiosondes and models fields. The aim of this work is to show how these new generation hyperspectral Infrared sounders can benefit in real-time weather monitoring. We have considered a regional extreme weather event to demonstrate how the profiles retrieved from these operational sounders are consistent with the environmental conditions which have led to this severe weather event. This work has also made use of data products of Moderate Resolution Imaging Spectroradiometer as well as by radiative transfer simulation of clear and cloudy atmospheric conditions using Numerical Weather Prediction profiles in conjunction with INSAT-3D sounder. Our results indicate the potential use of high-quality hyperspectral atmospheric profiles to aid in delineation of real-time weather prediction.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.

PubMed

Kolovos, Alexander; Skupin, André; Jerrett, Michael; Christakos, George

2010-09-01

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Toward Objective Monitoring of Ingestive Behavior in Free-living Population

PubMed Central

Sazonov, Edward S.; Schuckers, Stephanie A.C.; Lopez-Meyer, Paulo; Makeyev, Oleksandr; Melanson, Edward L.; Neuman, Michael R.; Hill, James O.

2010-01-01

Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions. PMID:19444225

Toward objective monitoring of ingestive behavior in free-living population.

PubMed

Sazonov, Edward S; Schuckers, Stephanie A C; Lopez-Meyer, Paulo; Makeyev, Oleksandr; Melanson, Edward L; Neuman, Michael R; Hill, James O

2009-10-01

Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions.

A Low-Cost Sensor Buoy System for Monitoring Shallow Marine Environments

PubMed Central

Albaladejo, Cristina; Soto, Fulgencio; Torres, Roque; Sánchez, Pedro; López, Juan A.

2012-01-01

Monitoring of marine ecosystems is essential to identify the parameters that determine their condition. The data derived from the sensors used to monitor them are a fundamental source for the development of mathematical models with which to predict the behaviour of conditions of the water, the sea bed and the living creatures inhabiting it. This paper is intended to explain and illustrate a design and implementation for a new multisensor monitoring buoy system. The system design is based on a number of fundamental requirements that set it apart from other recent proposals: low cost of implementation, the possibility of application in coastal shallow-water marine environments, suitable dimensions for deployment and stability of the sensor system in a shifting environment like the sea bed, and total autonomy of power supply and data recording. The buoy system has successfully performed remote monitoring of temperature and marine pressure (SBE 39 sensor), temperature (MCP9700 sensor) and atmospheric pressure (YOUNG 61302L sensor). The above requirements have been satisfactorily validated by operational trials in a marine environment. The proposed buoy sensor system thus seems to offer a broad range of applications. PMID:23012562

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

PubMed Central

Hu, Hai-Feng

2018-01-01

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes. PMID:29621175

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring.

PubMed

Wang, Si-Yuan; Yang, Ding-Xin; Hu, Hai-Feng

2018-04-05

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants' multi-parameters and the bearings' wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.

Ambulatory Seizure Monitoring: From Concept to Prototype Device.

PubMed

Myers, Mark H; Threatt, Madeline; Solies, Karsten M; McFerrin, Brent M; Hopf, Lindsey B; Birdwell, J Douglas; Sillay, Karl A

2016-07-01

The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient.

Ambulatory Seizure Monitoring: From Concept to Prototype Device

PubMed Central

Myers, Mark H.; Threatt, Madeline; Solies, Karsten M.; McFerrin, Brent M.; Hopf, Lindsey B.; Birdwell, J. Douglas; Sillay, Karl A.

2016-01-01

Background The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. Purpose The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. Methods The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. Result This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. Conclusion The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient. PMID:27647960

Proceedings of the Annual Conference of the Prognostics and Health Management Society (PHM 2014) Held in Fort Worth, TX on September 29 - October 2, 2014. Invited Session on Corrosion Monitoring, Sensing, Detection and Prediction

DTIC Science & Technology

2014-12-23

Campbell 225 Using Johnson Distribution for Automatic Threshold Setting in Wind Turbine Condition Monitoring System Kun S. Marhadi and Georgios Alexandros...Victoria M. Catterson, Craig Love, and Andrew Robb 725 Detection of Wind Turbine Power Performance Abnormalities Using Eigenvalue Analysis Georgios...instance, in wind turbine gearbox analysis (Zappalà et al., 2012). Various other techniques for frequency domain analysis have been explored for

Increased False-Memory Susceptibility After Mindfulness Meditation.

PubMed

Wilson, Brent M; Mickes, Laura; Stolarz-Fantino, Stephanie; Evrard, Matthew; Fantino, Edmund

2015-10-01

The effect of mindfulness meditation on false-memory susceptibility was examined in three experiments. Because mindfulness meditation encourages judgment-free thoughts and feelings, we predicted that participants in the mindfulness condition would be especially likely to form false memories. In two experiments, participants were randomly assigned to either a mindfulness induction, in which they were instructed to focus attention on their breathing, or a mind-wandering induction, in which they were instructed to think about whatever came to mind. The overall number of words from the Deese-Roediger-McDermott paradigm that were correctly recalled did not differ between conditions. However, participants in the mindfulness condition were significantly more likely to report critical nonstudied items than participants in the control condition. In a third experiment, which tested recognition and used a reality-monitoring paradigm, participants had reduced reality-monitoring accuracy after completing the mindfulness induction. These results demonstrate a potential unintended consequence of mindfulness meditation in which memories become less reliable. © The Author(s) 2015.

Model-based evaluation of subsurface monitoring networks for improved efficiency and predictive certainty of regional groundwater models

NASA Astrophysics Data System (ADS)

Gosses, M. J.; Wöhling, Th.; Moore, C. R.; Dann, R.; Scott, D. M.; Close, M.

2012-04-01

Groundwater resources worldwide are increasingly under pressure. Demands from different local stakeholders add to the challenge of managing this resource. In response, groundwater models have become popular to make predictions about the impact of different management strategies and to estimate possible impacts of changes in climatic conditions. These models can assist to find optimal management strategies that comply with the various stakeholder needs. Observations of the states of the groundwater system are essential for the calibration and evaluation of groundwater flow models, particularly when they are used to guide the decision making process. On the other hand, installation and maintenance of observation networks are costly. Therefore it is important to design monitoring networks carefully and cost-efficiently. In this study, we analyse the Central Plains groundwater aquifer (~ 4000 km2) between the Rakaia and Waimakariri rivers on the Eastern side of the Southern Alps in New Zealand. The large sedimentary groundwater aquifer is fed by the two alpine rivers and by recharge from the land surface. The area is mainly under agricultural land use and large areas of the land are irrigated. The other major water use is the drinking water supply for the city of Christchurch. The local authority in the region, Environment Canterbury, maintains an extensive groundwater quantity and quality monitoring programme to monitor the effects of land use and discharges on groundwater quality, and the suitability of the groundwater for various uses, especially drinking-water supply. Current and projected irrigation water demand has raised concerns about possible impacts on groundwater-dependent lowland streams. We use predictive uncertainty analysis and the Central Plains steady-state groundwater flow model to evaluate the worth of pressure head observations in the existing groundwater well monitoring network. The data worth of particular observations is dependent on the problem-specific prediction target under consideration. Therefore, the worth of individual observation locations may differ for different prediction targets. Our evaluation is based on predictions of lowland stream discharge resulting from changes in land use and irrigation in the upper Central Plains catchment. In our analysis, we adopt the model predictive uncertainty analysis method by Moore and Doherty (2005) which accounts for contributions from both measurement errors and uncertain structural heterogeneity. The method is robust and efficient due to a linearity assumption in the governing equations and readily implemented for application in the model-independent parameter estimation and uncertainty analysis toolkit PEST (Doherty, 2010). The proposed methods can be applied not only for the evaluation of monitoring networks, but also for the optimization of networks, to compare alternative monitoring strategies, as well as to identify best cost-benefit monitoring design even prior to any data acquisition.

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

NASA Astrophysics Data System (ADS)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Distinct state anxiety after predictable and unpredictable fear training in mice.

PubMed

Seidenbecher, Thomas; Remmes, Jasmin; Daldrup, Thiemo; Lesting, Jörg; Pape, Hans-Christian

2016-05-01

Sustained fear paradigms in rodents have been developed to monitor states of anxious apprehension and to model situations in patients suffering from long-lasting anxiety disorders. A recent report describes a fear conditioning paradigm, allowing distinction between phasic and sustained states of conditioned fear in non-restrained mice. However, so far no prospective studies have yet been conducted to elucidate whether induction of phasic or sustained fear can affect states of anxiety. Here, we used CS (conditioned stimulus) and US (unconditioned stimulus) pairing with predictable and unpredictable timing to induce phasic and sustained fear in mice. State anxiety during various fear response components was assessed using the elevated plus-maze test. Training with unpredictable CS-US timing resulted in CS-evoked sustained components of fear (freezing), while predictable CS-US timing resulted in rapid decline. Data suggested the influence of training procedure on state anxiety which is dependent on progression of conditioned fear during fear memory retrieval. Animals trained with unpredictable CS-US timing showed an unchanged high anxiety state throughout behavioral observation. In contrast, mice trained with predictable CS-US timing showed anxiolytic-like behavior 3 min after CS onset, which was accompanied by a fast decline of the fear conditioned response (freezing). Further systematic studies are needed to validate the phasic/sustained fear model in rodents as translational model for anxiety disorders in humans. Copyright © 2016 Elsevier B.V. All rights reserved.

Structural kinematics based damage zone prediction in gradient structures using vibration database

NASA Astrophysics Data System (ADS)

Talha, Mohammad; Ashokkumar, Chimpalthradi R.

2014-05-01

To explore the applications of functionally graded materials (FGMs) in dynamic structures, structural kinematics based health monitoring technique becomes an important problem. Depending upon the displacements in three dimensions, the health of the material to withstand dynamic loads is inferred in this paper, which is based on the net compressive and tensile displacements that each structural degree of freedom takes. These net displacements at each finite element node predicts damage zones of the FGM where the material is likely to fail due to a vibration response which is categorized according to loading condition. The damage zone prediction of a dynamically active FGMs plate have been accomplished using Reddy's higher-order theory. The constituent material properties are assumed to vary in the thickness direction according to the power-law behavior. The proposed C0 finite element model (FEM) is applied to get net tensile and compressive displacement distributions across the structures. A plate made of Aluminum/Ziconia is considered to illustrate the concept of structural kinematics-based health monitoring aspects of FGMs.

The Wake Vortex Prediction and Monitoring System WSVBS

NASA Astrophysics Data System (ADS)

Gerz, T.; Holzäpfel, F.

2009-09-01

Design and performance of the Wake Vortex Prediction and Monitoring System WSVBS are described. The WSVBS has been developed to tactically increase airport capacity for approach and landing on closely-spaced parallel runways. It is thought to dynamically adjust aircraft separations dependent on weather conditions and the resulting wake vortex behaviour without compromising safety. The WSVBS consists of components that consider meteorological conditions, aircraft glide path adherence, aircraft parameter combinations representing aircraft weight categories, the resulting wake-vortex behaviour, the surrounding safety areas, wake vortex monitoring, and the integration of the predictions into the arrival manager. The WSVBS has been designed and applied to Frankfurt Airport. However, its components are generic and can well be adjusted to any runway system and or airport location. The prediction horizon is larger than 45 min (as required by air traffic control) and updated every 10 minutes. It predicts the concepts of operations and procedures established by DFS and it further predicts additional temporal separations for in-trail traffic. A specific feature of the WSVBS is the usage of both measured and predicted meteorological quantities as input to wake vortex prediction. In ground proximity where the probability to encounter wake vortices is highest, the wake predictor employs measured environmental parameters that yield superior prediction results. For the less critical part aloft, which can not be monitored completely by instrumentation, the meteorological parameters are taken from dedicated numerical terminal weather predictions. The wake vortex model predicts envelopes for vortex position and strength which implicitly consider the quality of the meteorological input data. This feature is achieved by a training procedure which employs statistics of measured and predicted meteorological parameters and the resulting wake vortex behaviour. The WSVBS combines various conservative elements that presumably lead to a very high overall safety level of the WSVBS. The combination of these conservative measures certainly leads to a very high but currently unknown overall safety. Once the methodology of a comprehensive risk analysis will be established, it is planned to adjust all components to appropriate and consistent confidence levels. The WSVBS has demonstrated its functionality at Frankfurt airport during 66 days in the period from 18/12/06 until 28/02/07. The performance test indicates that (i) the system ran stable - no forecast breakdowns occurred, (ii) aircraft separations could have been reduced in 75% of the time compared to ICAO standards, (iii) reduced separation procedures could have been continuously applied for at least several tens of minutes and up to several hours occasionally, (iv) the predictions were correct as for about 1100 landings observed during 16 days no warnings occurred from the LIDAR. Fast-time simulations reveal that adapted concepts of operation yield significant reductions in delay and/or an increase in capacity to 3% taking into account the real traffic mix and operational constraints in the period of one month. Before the WSVBS can be handed over for final adaptations to become a customized fully operational system some further steps are planned. A risk analysis needs to be pursued to convince all stakeholders of the usefulness and capabilities of the system.

Development of a model to predict ash transport and water pollution risk in fire-affected environments

NASA Astrophysics Data System (ADS)

Neris, Jonay; Elliot, William J.; Doerr, Stefan H.; Robichaud, Peter R.

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

First application of the WEPP model to predict runoff and erosion risk in fire-affected volcanic areas in Europe

NASA Astrophysics Data System (ADS)

Neris, Jonay; Robichaud, Peter R.; Elliot, William J.; Doerr, Stefan H.; Notario del Pino, Jesús S.; Lado, Marcos

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

Submerged Medium Voltage Cable Systems at Nuclear Power Plants. A Review of Research Efforts Relevant to Aging Mechanisms and Condition Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Jason; Bernstein, Robert; White, II, Gregory Von

In a submerged environment, power cables may experience accelerated insulation degradation due to water - related aging mechanisms . Direct contact with water or moisture intrusion in the cable insulation s ystem has been identified in the literature as a significant aging stressor that can affect performance and lifetime of electric cables . Progressive reduction of the dielectric strength is commonly a result of water treeing which involves the development of permanent hydrophilic structures in the insulation coinciding with the absorption of water into the cable . Water treeing is a phenomenon in which dendritic microvoids are formed in electricmore » cable insulation due to electrochemic al reactions , electromechanical forces , and diffusion of contaminants over time . These reactions are caused by the combined effect s of water presence and high electrical stress es in the material . Water tree growth follow s a tree - like branching pattern , i ncreasing in volume and length over time . Although these cables can be "dried out," water tree degradation , specifically the growth of hydrophilic regions, is believed to be permanent and typically worsens over time. Based on established research , water treeing or water induced damage can occur in a variety of electric cables including XLPE, TR - XLPE and other insulating materials, such as EPR and butyl rubber . Once water trees or water induced damage form, the dielectric strength of an insulation materia l will decrease gradually with time as the water trees grow in length, which could eventually result in failure of the insulating material . Under wet conditions or i n submerged environments , several environmental and operational parameters can influence w ater tree initiation and affect water tree growth . These parameters include voltage cycling, field frequency, temperature, ion concentration and chemistry, type of insula tion material , and the characteristics of its defects. In this effort, a review of academic and industrial literature was performed to identify : 1) findings regarding the degradation mechanisms of submerged cabling and 2) condition monitoring methods that may prove useful in predict ing the remaining lifetime of submerged medium voltage p ower cables . The re search was conducted by a multi - disciplinary team , and s ources includ ed official NRC reports, n ational l aboratory reports , IEEE standards, conference and journal proceedings , magazine articles , PhD dissertations , and discussions with experts . The purpose of this work was to establish the current state - of - the - art in material degradation modeling and cable condition monitoring techniques and to identify research gaps . Subsequently, future areas of focus are recommended to address these research gaps and thus strengthen the efficacy of the NRC's developing cable condition monitoring program . Results of this literature review and details of the test ing recommendations are presented in this report . FOREWORD To ensure the safe, re liable, and cost - effective long - term operation of nuclear power plants, many systems, structures, and components must be continuously evaluated. The Nuclear Regulatory Commission (NRC) has identified that cables in submerged environments are of concern, particularly as plants are seeking license renewal. To date, there is a lack of consensus on aging and degradation mechanisms even though the area of submerged cables has been extensively studied. Consequently, the ability to make lifetime predictions for submerged cable does not yet exist. The NRC has engaged Sandia National Laboratories (SNL) to lead a coordinated effort to help elucidate the aging and degradation of cables in submerged environments by collaborating with cable manufacturers, utilities, universities, and other government agencies. A team of SNL experts was assembled from the laboratories including electrical condition monitoring, mat erial science, polymer degradation, plasma physics, nuclear systems, and statistics. An objective of this research program is to perform a l iterature r eview to gather a body of knowledge on prior research projects, technical papers, and literature related to cable degradation in a submerged environment. In addition, the information gathered from the literature review will be employed to gain insights for developing an aging coefficient, and to determine which condition monitoring techniques are capable of tracking cable degradation in a submerged environment. Moreover, the information gathered from the l iterature r eview will also be used to determine which approach or approaches are best suited to develop test methods for accelerated aging and condition m onitoring of medium voltage cables. In summary of this initial effort, s ignificant work has been performed on submerged cable insulation degradation; however, there is a lack of uniform theories and acceptance of chemical and physical pathways. This lack of fundamental understanding is coupled with the inability to make predictive statements about material performance in wet or submerged environments. S elect condition monitoring methods known to the industry are discussed in this report and a dditional co ndition monitoring methods were added in this effort based on recommendations from the Nuclear Energy Standards Coordinating Collaborative and available literature. This NUREG review provides additional clarity on the use of condition monitoring methods t o detect water - related damage to medium voltage cable and new methods and approaches proposed in academia and industry. In order t o ensure continued improvement in the efficacy of a cable condition monitoring program, continued research and development (R&D) efforts are necessary. R&D efforts should complement operations, iteratively improving condition monitoring policies, procedures and outcomes. Ideally, field and laboratory data enable improved understanding of material science which in turn inform s the development of new or improved condition monitoring methods and lifetime models. Finally, these improved methods and models aid in the refinement of condition monitoring policies and procedures.« less

Finite Difference Formulation for Prediction of Water Pollution

NASA Astrophysics Data System (ADS)

Johari, Hanani; Rusli, Nursalasawati; Yahya, Zainab

2018-03-01

Water is an important component of the earth. Human being and living organisms are demand for the quality of water. Human activity is one of the causes of the water pollution. The pollution happened give bad effect to the physical and characteristic of water contents. It is not practical to monitor all aspects of water flow and transport distribution. So, in order to help people to access to the polluted area, a prediction of water pollution concentration must be modelled. This study proposed a one-dimensional advection diffusion equation for predicting the water pollution concentration transport. The numerical modelling will be produced in order to predict the transportation of water pollution concentration. In order to approximate the advection diffusion equation, the implicit Crank Nicolson is used. For the purpose of the simulation, the boundary condition and initial condition, the spatial steps and time steps as well as the approximations of the advection diffusion equation have been encoded. The results of one dimensional advection diffusion equation have successfully been used to predict the transportation of water pollution concentration by manipulating the velocity and diffusion parameters.

Condition index monitoring supports conservation priorities for the protection of threatened grass-finch populations

PubMed Central

French, Kristine; Legge, Sarah; Astheimer, Lee; Garnett, Stephen

2015-01-01

Abstract Conservation agencies are often faced with the difficult task of prioritizing what recovery actions receive support. With the number of species under threat of decline growing globally, research that informs conservation priorities is greatly needed. The relative vulnerability of cryptic or nomadic species is often uncertain, because populations are difficult to monitor and local populations often seem stable in the short term. This uncertainty can lead to inaction when populations are in need of protection. We tested the feasibility of using differences in condition indices as an indication of population vulnerability to decline for related threatened Australian finch sub-species. The Gouldian finch represents a relatively well-studied endangered species, which has a seasonal and site-specific pattern of condition index variation that differs from the closely related non-declining long-tailed finch. We used Gouldian and long-tailed finch condition variation as a model to compare with lesser studied, threatened star and black-throated finches. We compared body condition (fat and muscle scores), haematocrit and stress levels (corticosterone) among populations, seasons and years to determine whether lesser studied finch populations matched the model of an endangered species or a non-declining species. While vulnerable finch populations often had lower muscle and higher fat and corticosterone concentrations during moult (seasonal pattern similar to Gouldian finches), haematocrit values did not differ among populations in a predictable way. Star and black-throated finch populations, which were predicted to be vulnerable to decline, showed evidence of poor condition during moult, supporting their status as vulnerable. Our findings highlight how measures of condition can provide insight into the relative vulnerability of animal and plant populations to decline and will allow the prioritization of efforts towards the populations most likely to be in jeopardy of extinction. PMID:27293710

Condition index monitoring supports conservation priorities for the protection of threatened grass-finch populations.

PubMed

Maute, Kimberly; French, Kristine; Legge, Sarah; Astheimer, Lee; Garnett, Stephen

2015-01-01

Conservation agencies are often faced with the difficult task of prioritizing what recovery actions receive support. With the number of species under threat of decline growing globally, research that informs conservation priorities is greatly needed. The relative vulnerability of cryptic or nomadic species is often uncertain, because populations are difficult to monitor and local populations often seem stable in the short term. This uncertainty can lead to inaction when populations are in need of protection. We tested the feasibility of using differences in condition indices as an indication of population vulnerability to decline for related threatened Australian finch sub-species. The Gouldian finch represents a relatively well-studied endangered species, which has a seasonal and site-specific pattern of condition index variation that differs from the closely related non-declining long-tailed finch. We used Gouldian and long-tailed finch condition variation as a model to compare with lesser studied, threatened star and black-throated finches. We compared body condition (fat and muscle scores), haematocrit and stress levels (corticosterone) among populations, seasons and years to determine whether lesser studied finch populations matched the model of an endangered species or a non-declining species. While vulnerable finch populations often had lower muscle and higher fat and corticosterone concentrations during moult (seasonal pattern similar to Gouldian finches), haematocrit values did not differ among populations in a predictable way. Star and black-throated finch populations, which were predicted to be vulnerable to decline, showed evidence of poor condition during moult, supporting their status as vulnerable. Our findings highlight how measures of condition can provide insight into the relative vulnerability of animal and plant populations to decline and will allow the prioritization of efforts towards the populations most likely to be in jeopardy of extinction.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Lee H., Jr. (Inventor)

1994-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (VR) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Airplane takeoff and landing performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B. (Inventor); Srivatsan, Raghavachari (Inventor); Person, Jr., Lee H. (Inventor)

1996-01-01

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

A generalized correlation of experimental flat-plate collector performance. [solar collectors, performance tests, energy policy

NASA Technical Reports Server (NTRS)

Simon, F. F.; Miller, D. R.

1975-01-01

A generalized collector performance correlation was derived and shown by experimental verification to be of the proper form to account for the majority of the variable conditions encountered both in outdoor and in indoor collector tests. This correlation permits a determination of collector parameters which are essentially nonvarying under conditions which do vary randomly (outdoors) or conditions which vary in a controlled manner (indoors - simulator). It was shown that correlation of the experimental performance of collectors allows the following: (1) comparisons of different collector designs; (2) collector performance prediction under conditions that differ from the conditions of the test program; and (3) monitoring performance degradation effects.

Utility of ERTS for monitoring the breeding habitat of migratory waterfowl

NASA Technical Reports Server (NTRS)

Work, E. A., Jr.; Gilmer, D. S.; Klett, A. T.

1974-01-01

Since 1968 the Bureau of Sport Fisheries and Wildlife (BSF&W) and the Environmental Research Institute of Michigan have cooperated on developing applications of remote sensing to the management of migratory waterfowl. Basically, this work has been concerned with (1) the assimilation of data on surface water conditions so that the data can be used as an index of annual waterfowl production, and (2) the collection of data on land use and wetland quality so that a measure of habitat carrying capacity is obtained. To date, efforts have been directed toward utilizing ERTS to monitor surface water conditions. An example of a model used for predicting the annual production of mallards (Anas platyrhynchos) is presented. The data inputs to this model and the potential for acquiring these data using ERTS are described.

Turbine engine rotor health monitoring evaluation by means of finite element analyses and spin tests data

NASA Astrophysics Data System (ADS)

Abdul-Aziz, Ali; Woike, Mark R.; Clem, Michelle; Baaklini, George Y.

2014-04-01

Generally, rotating engine components undergo high centrifugal loading environment which subject them to various types of failure initiation mechanisms. Health monitoring of these components is a necessity and is often challenging to implement. This is primarily due to numerous factors including the presence of scattered loading conditions, flaw sizes, component geometry and materials properties, all which hinder the simplicity of applying health monitoring applications. This paper represents a summary work of combined experimental and analytical modeling that included data collection from a spin test experiment of a rotor disk addressing the aforementioned durability issues. It further covers presentation of results obtained from a finite element modeling study to characterize the structural durability of a cracked rotor as it relates to the experimental findings. The experimental data include blade tip clearance, blade tip timing and shaft displacement measurements. The tests were conducted at the NASA Glenn Research Center's Rotordynamics Laboratory, a high precision spin rig. The results are evaluated and examined to determine their significance on the development of a health monitoring system to pre-predict cracks and other anomalies and to assist in initiating a supplemental physics based fault prediction analytical model.

Nowcast modeling of Escherichia coli concentrations at multiple urban beaches of southern Lake Michigan

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2005-01-01

Predictive modeling for Escherichia coli concentrations at effluent-dominated beaches may be a favorable alternative to current, routinely criticized monitoring standards. The ability to model numerous beaches simultaneously and provide real-time data decreases cost and effort associated with beach monitoring. In 2004, five Lake Michigan beaches and the nearby Little Calumet River outfall were monitored for E. coli 7 days a week; on nine occasions, samples were analyzed for coliphage to indicate a sewage source. Ambient lake, river, and weather conditions were measured or obtained from independent monitoring sources. Positive tests for coliphage analysis indicated sewage was present in the river and on bathing beaches following heavy rainfall. Models were developed separately for days with prevailing onshore and offshore winds due to the strong influence of wind direction in determining the river's impact on the beaches. Using regression modeling, it was determined that during onshore winds, E. coli   could be adequately predicted using wave height, lake chlorophyll and turbidity, and river turbidity (R2=0.635, N=94); model performance decreased for offshore winds using wave height, wave period, and precipitation (R2=0.320, N=124). Variation was better explained at individual beaches. Overall, the models only failed to predict E. coli levels above the EPA closure limit (235 CFU/100 ml) on five of eleven occasions, indicating that the model is a more reliable alternative to the monitoring approach employed at most recreational beaches.

Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; 4 Blyth, Eleanor; de Roo, Ad; Doell. Petra; Ek, Mike; Famiglietti, James;

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

NASA Astrophysics Data System (ADS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; Blyth, Eleanor; de Roo, Ad; DöLl, Petra; Ek, Mike; Famiglietti, James; Gochis, David; van de Giesen, Nick; Houser, Paul; Jaffé, Peter R.; Kollet, Stefan; Lehner, Bernhard; Lettenmaier, Dennis P.; Peters-Lidard, Christa; Sivapalan, Murugesu; Sheffield, Justin; Wade, Andrew; Whitehead, Paul

2011-05-01

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (˜10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a "grand challenge" to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

A novel apparatus for non-contact measurement of heart rate variability: a system to prevent secondary exposure of medical personnel to toxic materials under biochemical hazard conditions, in monitoring sepsis or in predicting multiple organ dysfunction syndrome.

PubMed

Matsui, T; Arai, I; Gotoh, S; Hattori, H; Takase, B; Kikuchi, M; Ishihara, M

2005-10-01

The impaired balance of the low-frequency/high-frequency ratio obtained from spectral components of RR intervals can be a diagnostic test for sepsis. In addition, it is known that a reduction of heart rate variability (HRV) is useful in identifying septic patients at risk of the development of multiple organ dysfunction syndrome (MODS). We have reported a non-contact method using a microwave radar to monitor the heart and respiratory rates of a healthy person placed inside an isolator or of experimental animals exposed to toxic materials. With the purpose of preventing secondary exposure of medical personnel to toxic materials under biochemical hazard conditions, we designed a novel apparatus for non-contact measurement of HRV using a 1215 MHz microwave radar, a high-pass filter, and a personal computer. The microwave radar monitors only the small reflected waves from the subject's chest wall, which are modulated by the cardiac and respiratory motion. The high-pass filter enhances the cardiac signal and attenuates the respiratory signal. In a human trial, RR intervals derived from the non-contact apparatus significantly correlated with those derived from ECG (r=0.98, P<0.0001). The non-contact apparatus showed a similar power spectrum of RR intervals to that of ECG. Our non-contact HRV measurement apparatus appears promising for future pre-hospital monitoring of septic patients or for predicting MODS patients, inside isolators or in the field for mass casualties under biochemical hazard circumstances.

Monitoring subsurface hydrologic response for precipitation-induced shallow landsliding in the San Francisco Bay area, California, USA

USGS Publications Warehouse

Collins, Brian D.; Stock, Jonathan; Weber, Lisa C.; Whitman, K.; Knepprath, N.

2012-01-01

Intense winter storms in the San Francisco Bay area (SFBA) of California, USA often trigger shallow landslides. Some of these landslides mobilize into potentially hazardous debris flows. A growing body of research indicates that rainfall intensity-duration thresholds are insufficient for accurate prediction of landslide occurrence. In response, we have begun long-term monitoring of the hydrologic response of land-slide-prone hillslopes to rainfall in several areas of the SFBA. Each monitoring site is equipped with sensors for measuring soil moisture content and piezometric pressure at several soil depths along with a rain gauge connected to a cell phone or satellite telemetered data logger. The data are transmitted in near-real-time, providing the ability to monitor hydrologic conditions before, during, and after storms. Results are guiding the establishment of both antecedent and storm-specific rainfall and moisture content thresholds which must be achieved before landslide-causative positive pore water pressures are generated. Although widespread shallow landsliding has not yet occurred since the deployment of the monitoring sites, several isolated land-slides have been observed in the area of monitoring. The landslides occurred during a period when positive pore water pressures were measured as a result of intense rainfall that followed higher-than-average season precipitation totals. Continued monitoring and analysis will further guide the establishment of more general-ized thresholds for different regions of the SFBA and contribute to the development and calibration of physi-cally-based predictive models.

A Distributed Fiber Optic Sensor Network for Online 3-D Temperature and Neutron Fluence Mapping in a VHTR Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsvetkov, Pavel; Dickerson, Bryan; French, Joseph

2014-04-30

Robust sensing technologies allowing for 3D in-core performance monitoring in real time are of paramount importance for already established LWRs to enhance their reliability and availability per year, and therefore, to further facilitate their economic competitiveness via predictive assessment of the in-core conditions.

Psychosocial Correlates of Adolescent Drug Dealing in the Inner City: Potential Roles of Opportunity, Conventional Commitments, and Maturity

ERIC Educational Resources Information Center

Little, Michelle; Steinberg, Laurence

2006-01-01

This study examined a model of the simultaneous and interactive influence of social context, psychosocial attitudes, and individual maturity on the prediction of urban adolescent drug dealing. Five factors were found to significantly increase adolescents' opportunity for drug selling: low parental monitoring, poor neighborhood conditions, low…

Adaptive management on public lands in the United States: commitment or rhetoric?

Treesearch

William H. Moir; William M. Block

2001-01-01

Adaptive management (AM is the process of implementing land management activities in incremental steps and evaluating whether desired outcomes are being achieved at each step. If conditions deviate substantially from predictions, management activities are adjusted to achieve the desired outcomes. Thus, AM is a kind of monitoring, an activity that land management...

The effects of divided attention at study and reporting procedure on regulation and monitoring for episodic recall.

PubMed

Sauer, James; Hope, Lorraine

2016-09-01

Eyewitnesses regulate the level of detail (grain size) reported to balance competing demands for informativeness and accuracy. However, research to date has predominantly examined metacognitive monitoring for semantic memory tasks, and used relatively artificial phased reporting procedures. Further, although the established role of confidence in this regulation process may affect the confidence-accuracy relation for volunteered responses in predictable ways, previous investigations of the confidence-accuracy relation for eyewitness recall have largely overlooked the regulation of response granularity. Using a non-phased paradigm, Experiment 1 compared reporting and monitoring following optimal and sub-optimal (divided attention) encoding conditions. Participants showed evidence of sacrificing accuracy for informativeness, even when memory quality was relatively weak. Participants in the divided (cf. full) attention condition showed reduced accuracy for fine- but not coarse-grained responses. However, indices of discrimination and confidence diagnosticity showed no effect of divided attention. Experiment 2 compared the effects of divided attention at encoding on reporting and monitoring using both non-phased and 2-phase procedures. Divided attention effects were consistent with Experiment 1. However, compared to those in the non-phased condition, participants in the 2-phase condition displayed a more conservative control strategy, and confidence ratings were less diagnostic of accuracy. When memory quality was reduced, although attempts to balance informativeness and accuracy increased the chance of fine-grained response errors, confidence provided an index of the likely accuracy of volunteered fine-grained responses for both condition. Copyright © 2016 Elsevier B.V. All rights reserved.

Towards a geophysical decision-support system for monitoring and managing unstable slopes

NASA Astrophysics Data System (ADS)

Chambers, J. E.; Meldrum, P.; Wilkinson, P. B.; Uhlemann, S.; Swift, R. T.; Inauen, C.; Gunn, D.; Kuras, O.; Whiteley, J.; Kendall, J. M.

2017-12-01

Conventional approaches for condition monitoring, such as walk over surveys, remote sensing or intrusive sampling, are often inadequate for predicting instabilities in natural and engineered slopes. Surface observations cannot detect the subsurface precursors to failure events; instead they can only identify failure once it has begun. On the other hand, intrusive investigations using boreholes only sample a very small volume of ground and hence small scale deterioration process in heterogeneous ground conditions can easily be missed. It is increasingly being recognised that geophysical techniques can complement conventional approaches by providing spatial subsurface information. Here we describe the development and testing of a new geophysical slope monitoring system. It is built around low-cost electrical resistivity tomography instrumentation, combined with integrated geotechnical logging capability, and coupled with data telemetry. An automated data processing and analysis workflow is being developed to streamline information delivery. The development of this approach has provided the basis of a decision-support tool for monitoring and managing unstable slopes. The hardware component of the system has been operational at a number of field sites associated with a range of natural and engineered slopes for up to two years. We report on the monitoring results from these sites, discuss the practicalities of installing and maintaining long-term geophysical monitoring infrastructure, and consider the requirements of a fully automated data processing and analysis workflow. We propose that the result of this development work is a practical decision-support tool that can provide near-real-time information relating to the internal condition of problematic slopes.

Developing Best Practices for Detecting Change at Marine Renewable Energy Sites

NASA Astrophysics Data System (ADS)

Linder, H. L.; Horne, J. K.

2016-02-01

In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.

The long oasis: understanding and managing saline floodplains in southeastern Australia

NASA Astrophysics Data System (ADS)

Woods, J.; Green, G.; Laattoe, T.; Purczel, C.; Riches, V.; Li, C.; Denny, M.

2017-12-01

In a semi-arid region of southeastern Australia, the River Murray is the predominant source of freshwater for town water supply, irrigation, and floodplain ecosystems. The river interacts with aquifers where the salinity routinely exceeds 18,000 mg/l. River regulation, extraction, land clearance, and irrigation have reduced the size and frequency of floods while moving more salt into the floodplain. Floodplain ecosystem health has declined. Management options to improve floodplain health under these modified conditions include environmental watering, weirpool manipulation, and groundwater pumping. To benefit long-lived tree species, floodplain management needs to increase soil moisture availability. A conceptual model was developed of floodplain processes impacting soil moisture availability. The implications and limitations of the conceptualization were investigated using a series of numerical models, each of which simulated a subset of the processes under current and managed conditions. The aim was to determine what range of behaviors the models predicted, and to identify which parameters were key to accurately predicting the success of management options. Soil moisture availability was found to depend strongly on the properties of the floodplain clay, which controls vertical recharge during inundation. Groundwater freshening near surface water features depended on the riverbed conductivity and the penetration of the river into the floodplain sediments. Evapotranspiration is another critical process, and simulations revealed the limitations of standard numerical codes in environments where both evaporation and transpiration depend on salinity. Finally, maintenance of viable populations of floodplain trees is conceptually understood to rely on the persistence of adequate soil moisture availability over time, but thresholds for duration of exposure to low moisture availability that lead to decline and irreversible decline in tree condition are a major knowledge gap. The work identified critical data gaps which will be addressed in monitoring guidelines to improve management. This includes: hydrogeochemical sampling; in situ soil monitoring combined with tree health observations; monitoring of actual evapotranspiration; and monitoring of bores close to surface water sources.

Electrical Resistance of SiC/SiC Ceramic Matrix Composites for Damage Detection and Life-Prediction

NASA Technical Reports Server (NTRS)

Smith, Craig; Morscher, Gregory; Xia, Zhenhai

2009-01-01

Ceramic matrix composites (CMC) are suitable for high temperature structural applications such as turbine airfoils and hypersonic thermal protection systems due to their low density high thermal conductivity. The employment of these materials in such applications is limited by the ability to accurately monitor and predict damage evolution. Current nondestructive methods such as ultrasound, x-ray, and thermal imaging are limited in their ability to quantify small scale, transverse, in-plane, matrix cracks developed over long-time creep and fatigue conditions. CMC is a multifunctional material in which the damage is coupled with the material s electrical resistance, providing the possibility of real-time information about the damage state through monitoring of resistance. Here, resistance measurement of SiC/SiC composites under mechanical load at both room temperature monotonic and high temperature creep conditions, coupled with a modal acoustic emission technique, can relate the effects of temperature, strain, matrix cracks, fiber breaks, and oxidation to the change in electrical resistance. A multiscale model can in turn be developed for life prediction of in-service composites, based on electrical resistance methods. Results of tensile mechanical testing of SiC/SiC composites at room and high temperatures will be discussed. Data relating electrical resistivity to composite constituent content, fiber architecture, temperature, matrix crack formation, and oxidation will be explained, along with progress in modeling such properties.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

The human orbitofrontal cortex monitors outcomes even when no reward is at stake.

PubMed

Schnider, Armin; Treyer, Valerie; Buck, Alfred

2005-01-01

The orbitofrontal cortex (OFC) processes the occurrence or omission of anticipated rewards, but clinical evidence suggests that it might serve as a generic outcome monitoring system, independent of tangible reward. In this positron emission tomography (PET) study, normal human subjects performed a series of tasks in which they simply had to predict behind which one of two colored rectangles a drawing of an object was hidden. While all tasks involved anticipation in that they had an expectation phase between the subject's prediction and the presentation of the outcome, they varied with regards to the uncertainty of outcome. No comment on the correctness of the prediction, no record of ongoing performance, and no reward, not even a score, was provided. Nonetheless, we found strong activation of the OFC: in comparison with a baseline task, the left anterior medial OFC showed activation in all conditions, indicating a basic role in anticipation; the left posterior OFC was activated in all tasks with some uncertainty of outcome, suggesting a role in the monitoring of outcomes; the right medial OFC showed activation exclusively during guessing. The data indicate a generic role of the human OFC, with some topical specificity, in the generation of hypotheses and processing of outcomes, independent of the presence of explicit reward.

Long-term seafloor monitoring at an open ocean aquaculture site in the western Gulf of Maine, USA: development of an adaptive protocol.

PubMed

Grizzle, R E; Ward, L G; Fredriksson, D W; Irish, J D; Langan, R; Heinig, C S; Greene, J K; Abeels, H A; Peter, C R; Eberhardt, A L

2014-11-15

The seafloor at an open ocean finfish aquaculture facility in the western Gulf of Maine, USA was monitored from 1999 to 2008 by sampling sites inside a predicted impact area modeled by oceanographic conditions and fecal and food settling characteristics, and nearby reference sites. Univariate and multivariate analyses of benthic community measures from box core samples indicated minimal or no significant differences between impact and reference areas. These findings resulted in development of an adaptive monitoring protocol involving initial low-cost methods that required more intensive and costly efforts only when negative impacts were initially indicated. The continued growth of marine aquaculture is dependent on further development of farming methods that minimize negative environmental impacts, as well as effective monitoring protocols. Adaptive monitoring protocols, such as the one described herein, coupled with mathematical modeling approaches, have the potential to provide effective protection of the environment while minimize monitoring effort and costs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms

USGS Publications Warehouse

Stumpf, Richard P.

2001-01-01

The new satellite ocean color sensors offer a means of detecting and monitoring algal blooms in the ocean and coastal zone. Beginning with SeaWiFS (Sea Wide Field-of-view Sensor) in September 1997, these sensors provide coverage every 1 to 2 days with 1-km pixel view at nadir. Atmospheric correction algorithms designed for the coastal zone combined with regional chlorophyll algorithms can provide good and reproducible estimates of chlorophyll, providing the means of monitoring various algal blooms. Harmful algal blooms (HABs) caused by Karenia brevis in the Gulf of Mexico are particularly amenable to remote observation. The Gulf of Mexico has relatively clear water and K. brevis, in bloom conditions, tends to produce a major portion of the phytoplankton biomass. A monitoring program has begun in the Gulf of Mexico that integrates field data from state monitoring programs with satellite imagery, providing an improved capability for the monitoring of K. brevis blooms.

Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates

Remote monitoring of a thermal plume

NASA Technical Reports Server (NTRS)

Kuo, C. Y.; Talay, T. A.

1979-01-01

A remote-sensing experiment conducted on May 17, 1977, over the Surry nuclear power station on the James River, Virginia is discussed. Isotherms of the thermal plume from the power station were derived from remotely sensed data and compared with in situ water temperature measurements provided by the Virginia Electric and Power Company, VEPCO. The results of this study were also qualitatively compared with those from other previous studies under comparable conditions of the power station's operation and the ambient flow. These studies included hydraulic model predictions carried out by Pritchard and Carpenter and a 5-year in situ monitoring program based on boat surveys.

A System for Monitoring and Forecasting Land Surface Phenology Using Time Series of JPSS VIIRS Observations and Its Applications

NASA Astrophysics Data System (ADS)

Zhang, X.; Yu, Y.; Liu, L.

2015-12-01

Land surface phenology quantifies seasonal dynamics of vegetation properties including the timing and magnitude of vegetation greenness from satellite observations. Over the last decade, historical time series of AVHRR and MODIS data has been used to characterize the seasonal and interannual variation in terrestrial ecosystems and their responses to a changing and variable climate. The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the operational JPSS satellites provides land surface observations in a timely fashion, which has the capability to monitor phenological development in near real time. This capability is particularly important for assisting agriculture, natural resource management, and land modeling for weather prediction systems. Here we introduce a system to monitor in real time and forecast in the short term phenological development based on daily VIIRS observations available with a one-day latency. The system integrates a climatological land surface phenology from long-term MODIS data and available VIIRS observations to simulate a set of potential temporal trajectories of greenness development at a given time and pixel. The greenness trajectories, which are qualified using daily two-band Enhanced Vegetation Index (EVI2), are applied to identify spring green leaf development and autumn color foliage status in real time and to predict the occurrence of future phenological events. This system currently monitors vegetation development across the North America every three days and makes prediction to 10 days ahead. We further introduce the applications of near real time spring green leaf and fall color foliage. Specifically, this system is used for tracing the crop progress across the United States, guiding the field observations in US National Phenology Network, servicing tourists for the observation of color fall foliage, and parameterizing seasonal surface physical conditions for numerical weather prediction models.

Evaluation of low wind modeling approaches for two tall-stack databases.

PubMed

Paine, Robert; Samani, Olga; Kaplan, Mary; Knipping, Eladio; Kumar, Naresh

2015-11-01

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO2 monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO2 monitors within 10 km of the Dakota Gasification Company's plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO2 monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the over-prediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions. AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the over-prediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.

Nondestructive detection of total viable count changes of chilled pork in high oxygen storage condition based on hyperspectral technology

NASA Astrophysics Data System (ADS)

Zheng, Xiaochun; Peng, Yankun; Li, Yongyu; Chao, Kuanglin; Qin, Jianwei

2017-05-01

The plate count method is commonly used to detect the total viable count (TVC) of bacteria in pork, which is timeconsuming and destructive. It has also been used to study the changes of the TVC in pork under different storage conditions. In recent years, many scholars have explored the non-destructive methods on detecting TVC by using visible near infrared (VIS/NIR) technology and hyperspectral technology. The TVC in chilled pork was monitored under high oxygen condition in this study by using hyperspectral technology in order to evaluate the changes of total bacterial count during storage, and then evaluate advantages and disadvantages of the storage condition. The VIS/NIR hyperspectral images of samples stored in high oxygen condition was acquired by a hyperspectral system in range of 400 1100nm. The actual reference value of total bacteria was measured by standard plate count method, and the results were obtained in 48 hours. The reflection spectra of the samples are extracted and used for the establishment of prediction model for TVC. The spectral preprocessing methods of standard normal variate transformation (SNV), multiple scatter correction (MSC) and derivation was conducted to the original reflectance spectra of samples. Partial least squares regression (PLSR) of TVC was performed and optimized to be the prediction model. The results show that the near infrared hyperspectral technology based on 400-1100nm combined with PLSR model can describe the growth pattern of the total bacteria count of the chilled pork under the condition of high oxygen very vividly and rapidly. The results obtained in this study demonstrate that the nondestructive method of TVC based on NIR hyperspectral has great potential in monitoring of edible safety in processing and storage of meat.

PROSNOW - Provision of a prediction system allowing for management and optimization of snow in Alpine ski resorts

NASA Astrophysics Data System (ADS)

Morin, Samuel; Ghislain, Dubois

2017-04-01

Snow on the ground is a critical resource for mountain regions to sustain river flow, to provide freshwater input to ecosystems and to support winter tourism, in particular in ski resorts. The level of activity, employment, turnover and profit of hundreds of ski resorts in the European Alps primarily depends on meteorological conditions, in particular natural snowfall but also increasingly conditions favourable for snowmaking (production of machine made snow, also referred to as technical snow). Ski resorts highly depend on appropriate conditions for snowmaking (mainly the availability of cold water, as well as sub-freezing temperature with sufficiently low humidity conditions). However, beyond the time scale of weather forecasts (a few days), managers of ski resorts have to rely on various and scattered sources of information, hampering their ability to cope with highly variable meteorological conditions. Improved anticipation capabilities at all time scales, spanning from "weather forecast" (up to 5 days typically) to "climate prediction" at the seasonal scale (up to several months) holds significant potential to increase the resilience of socio-economic stakeholders and supports their real-time adaptation potential. To address this issue, the recently funded (2017-2020) H2020 PROSNOW project will build a demonstrator of a meteorological and climate prediction and snow management system from one week to several months ahead, specifically tailored to the needs of the ski industry. PROSNOW will apply state-of-the-art knowledge relevant to the predictability of atmospheric and snow conditions, and investigate and document the added value of such services. The project proposes an Alpine-wide system (including ski resorts located in France, Switzerland, Germany, Austria and Italy). It will join and link providers of weather forecasts and climate predictions at the seasonal scale, research institutions specializing in snowpack modelling, a relevant ensemble of at least 8 representative resorts in the Alps, technical bodies representing ski resorts managers, and private technology companies. These companies are already providing services for snow management such as snow depth monitoring, snowmaking operations monitoring and planning using latest technologies. The added value of the demonstrator will be assessed for the ski industry, but also for additional stakeholders including local and regional tourism authorities, hydropower managers, and natural hazard forecasters and planners. This presentation will introduce the main goals and concepts of the PROSNOW project, in order to foster interactions with the specialized scientific communities relevant to this challenge.

Smart homes and home health monitoring technologies for older adults: A systematic review.

PubMed

Liu, Lili; Stroulia, Eleni; Nikolaidis, Ioanis; Miguel-Cruz, Antonio; Rios Rincon, Adriana

2016-07-01

Around the world, populations are aging and there is a growing concern about ways that older adults can maintain their health and well-being while living in their homes. The aim of this paper was to conduct a systematic literature review to determine: (1) the levels of technology readiness among older adults and, (2) evidence for smart homes and home-based health-monitoring technologies that support aging in place for older adults who have complex needs. We identified and analyzed 48 of 1863 relevant papers. Our analyses found that: (1) technology-readiness level for smart homes and home health monitoring technologies is low; (2) the highest level of evidence is 1b (i.e., one randomized controlled trial with a PEDro score ≥6); smart homes and home health monitoring technologies are used to monitor activities of daily living, cognitive decline and mental health, and heart conditions in older adults with complex needs; (3) there is no evidence that smart homes and home health monitoring technologies help address disability prediction and health-related quality of life, or fall prevention; and (4) there is conflicting evidence that smart homes and home health monitoring technologies help address chronic obstructive pulmonary disease. The level of technology readiness for smart homes and home health monitoring technologies is still low. The highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coble, Jamie; Orton, Christopher; Schwantes, Jon

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test threemore » fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.« less

Prediction of dynamic strains on a monopile offshore wind turbine using virtual sensors

NASA Astrophysics Data System (ADS)

Iliopoulos, A. N.; Weijtjens, W.; Van Hemelrijck, D.; Devriendt, C.

2015-07-01

The monitoring of the condition of the offshore wind turbine during its operational states offers the possibility of performing accurate assessments of the remaining life-time as well as supporting maintenance decisions during its entire life. The efficacy of structural monitoring in the case of the offshore wind turbine, though, is undermined by the practical limitations connected to the measurement system in terms of cost, weight and feasibility of sensor mounting (e.g. at muddline level 30m below the water level). This limitation is overcome by reconstructing the full-field response of the structure based on the limited number of measured accelerations and a calibrated Finite Element Model of the system. A modal decomposition and expansion approach is used for reconstructing the responses at all degrees of freedom of the finite element model. The paper will demonstrate the possibility to predict dynamic strains from acceleration measurements based on the aforementioned methodology. These virtual dynamic strains will then be evaluated and validated based on actual strain measurements obtained from a monitoring campaign on an offshore Vestas V90 3 MW wind turbine on a monopile foundation.

Introducing seasonal hydro-meteorological forecasts in local water management. First reflections from the Messara site, Crete, Greece.

NASA Astrophysics Data System (ADS)

Koutroulis, Aristeidis; Grillakis, Manolis; Tsanis, Ioannis

2017-04-01

Seasonal prediction is recently at the center of the forecasting research efforts, especially for regions that are projected to be severely affected by global warming. The value of skillful seasonal forecasts can be considerable for many sectors and especially for the agricultural in which water users and managers can benefit to better anticipate against drought conditions. Here we present the first reflections from the user/stakeholder interactions and the design of a tailored drought decision support system in an attempt to bring seasonal predictions into local practice for the Messara valley located in the central-south area of Crete, Greece. Findings from interactions with the users and stakeholders reveal that although long range and seasonal predictions are not used, there is a strong interest for this type of information. The increase in the skill of short range weather predictions is also of great interest. The drought monitoring and prediction tool under development that support local water and agricultural management will include (a) sources of skillful short to medium term forecast information, (b) tailored drought monitoring and forecasting indices for the local groundwater aquifer and rain-fed agriculture, and (c) seasonal inflow forecasts for the local dam through hydrologic simulation to support management of freshwater resources and drought impacts on irrigated agriculture.

Scaffolding across the lifespan in history-dependent decision-making.

PubMed

Cooper, Jessica A; Worthy, Darrell A; Gorlick, Marissa A; Maddox, W Todd

2013-06-01

We examined the relationship between pressure and age-related changes in decision-making using a task for which currently available rewards depend on the participant's previous history of choices. Optimal responding in this task requires the participant to learn how his or her current choices affect changes in the future rewards given for each option. Building on the scaffolding theory of aging and cognition, we predicted that when additional frontal resources are available, compensatory recruitment leads to increased monitoring and increased use of heuristic-based strategies, ultimately leading to better performance. Specifically, we predicted that scaffolding would result in an age-related performance advantage under no pressure conditions. We also predicted that, although younger adults would engage in scaffolding under pressure, older adults would not have additional resources available for increased scaffolding under pressure-packed conditions, leading to an age-related performance deficit. Both predictions were supported by the data. In addition, computational models were used to evaluate decision-making strategies employed by each participant group. As expected, older adults under no pressure conditions and younger adults under pressure conditions showed increased use of heuristic-based strategies relative to older adults under pressure and younger adults under no pressure, respectively. These results are consistent with the notion that scaffolding can occur across the life span in the face of an environmental challenge. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Biodegradation – Monitored Natural Attenuation (MNA) for Oxygenates: How it Evolved, why it Occurs and Using Stable Carbon Isotopes to Predict Plume Behavior

EPA Science Inventory

The organisms that degrade MtBE under anaerobic conditions are evolved to acquire energy for growth by using molecular hydrogen and carbonate ion to cleave methyl ether bonds. Methyl ether bonds are common in nature and the bond also occurs in MTBE. MTBE in contaminated ground...

An analytical analysis of the dispersion predictions for effluents from the Saturn 5 and Scout-Algol 3 rocket exhausts

NASA Technical Reports Server (NTRS)

Stephens, J. B.; Susko, M.; Kaufman, J. W.; Hill, C. K.

1973-01-01

Predictions of the spatial concentration mapping of the potentially toxic constituents of the exhaust effluents from a launch of a Saturn 5 and of a Scout-Algol 3 vehicle utilizing the NASA/MSFC Multilayer Diffusion Program are provided. In the case of the Saturn 5, special attention was given to the concentration fields of carbon monoxide with a correlation of carbon dioxide concentrations. The Scout-Algol 3 provided an example of the centerline concentrations of hydrogen chloride, carbon monoxide, and alumina under typical meteorological conditions. While these results define the specific environmental impact of these two launches under the meteorological conditions existing during launches, they also provide a basis for the empirical monitoring of the constituents of the exhaust effluents of these vehicles.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Particle Pollution Estimation Based on Image Analysis

PubMed Central

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. PMID:26828757

Particle Pollution Estimation Based on Image Analysis.

PubMed

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.

The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.

Intelligent sensor-model automated control of PMR-15 autoclave processing

NASA Technical Reports Server (NTRS)

Hart, S.; Kranbuehl, D.; Loos, A.; Hinds, B.; Koury, J.

1992-01-01

An intelligent sensor model system has been built and used for automated control of the PMR-15 cure process in the autoclave. The system uses frequency-dependent FM sensing (FDEMS), the Loos processing model, and the Air Force QPAL intelligent software shell. The Loos model is used to predict and optimize the cure process including the time-temperature dependence of the extent of reaction, flow, and part consolidation. The FDEMS sensing system in turn monitors, in situ, the removal of solvent, changes in the viscosity, reaction advancement and cure completion in the mold continuously throughout the processing cycle. The sensor information is compared with the optimum processing conditions from the model. The QPAL composite cure control system allows comparison of the sensor monitoring with the model predictions to be broken down into a series of discrete steps and provides a language for making decisions on what to do next regarding time-temperature and pressure.

Proactive monitoring of an onshore wind farm through lidar measurements, SCADA data and a data-driven RANS solver

NASA Astrophysics Data System (ADS)

Iungo, Giacomo Valerio; Camarri, Simone; Ciri, Umberto; El-Asha, Said; Leonardi, Stefano; Rotea, Mario A.; Santhanagopalan, Vignesh; Viola, Francesco; Zhan, Lu

2016-11-01

Site conditions, such as topography and local climate, as well as wind farm layout strongly affect performance of a wind power plant. Therefore, predictions of wake interactions and their effects on power production still remain a great challenge in wind energy. For this study, an onshore wind turbine array was monitored through lidar measurements, SCADA and met-tower data. Power losses due to wake interactions were estimated to be approximately 4% and 2% of the total power production under stable and convective conditions, respectively. This dataset was then leveraged for the calibration of a data driven RANS (DDRANS) solver, which is a compelling tool for prediction of wind turbine wakes and power production. DDRANS is characterized by a computational cost as low as that for engineering wake models, and adequate accuracy achieved through data-driven tuning of the turbulence closure model. DDRANS is based on a parabolic formulation, axisymmetry and boundary layer approximations, which allow achieving low computational costs. The turbulence closure model consists in a mixing length model, which is optimally calibrated with the experimental dataset. Assessment of DDRANS is then performed through lidar and SCADA data for different atmospheric conditions. This material is based upon work supported by the National Science Foundation under the I/UCRC WindSTAR, NSF Award IIP 1362033.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions. To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations.We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a differentmore » segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using monte-carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate.We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all environmental and operating conditions, while the latter does not and leverages the fact that environmental and operating conditions vary slowly over time and can be modeled as a Gaussian process.« less

Analysis of the Material Removal Rate in Magnetic Abrasive Finishing of Thin Film Coated Pyrex Glass

NASA Astrophysics Data System (ADS)

Lee, Hee Hwan; Lee, Seoung Hwan

The material removal rate (MRR) during precision finishing/polishing is a key factor, which dictates the process performance. Moreover, the MRR or wear rate is closely related to the material/part reliability. For nanoscale patterning and/or planarization on nano-order thickness coatings, the prediction and in-process monitoring of the MRR is necessary, because the process is not characterizable due to size effects and material property/process condition variations as a result of the coating/substrate interactions. The purpose of this research was to develop a practical methodology for the prediction and in-process monitoring of MRR during nanoscale finishing of coated surfaces. Using a specially designed magnetic abrasive finishing (MAF) and acoustic emission (AE) monitoring setup, experiments were carried out on indium-zinc-oxide (IZO) coated Pyrex glasses. After a given polishing time interval, AFM indentation was conducted for each workpiece sample to measure the adhesion force variations of the coating layers (IZO), which are directly related to the MRR changes. The force variation and AE monitoring data were compared to the MRR calculated form the surface measurement (Nanoview) results. The experimental results demonstrate strong correlations between AFM indentation and MRR measurement data. In addition, the monitored AE signals show sensitivity of the material structure variations of the coating layer, as the polishing progresses.

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate/n-Dodecane/Nitric Acid Solvent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tatiana G. Levitskaia; James M. Peterson; Emily L. Campbell

2013-12-01

In liquid–liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness, and frequent solvent analysis is warranted. Our research explores the feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutylphosphoric acid (HDBP) was assessed. Fourier transform infrared (FTIR)more » spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to high-dose external ?-irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus, demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate /n-Dodecane/Nitric Acid Solvent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.

2013-11-05

In liquid-liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness and frequent solvent analysis is warranted. Our research explores feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutyl phosphoric acid (HDBP) was assessed. Fourier Transform Infrared Spectroscopymore » (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to the high dose external gamma irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

Chemiluminescence as a condition monitoring method for thermal aging and lifetime prediction of an HTPB elastomer.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gillen, Kenneth Todd; Minier, Leanna M. G.; Celina, Mathias C.

Chemiluminescence (CL) has been applied as a condition monitoring technique to assess aging related changes in a hydroxyl-terminated-polybutadiene based polyurethane elastomer. Initial thermal aging of this polymer was conducted between 110 and 50 C. Two CL methods were applied to examine the degradative changes that had occurred in these aged samples: isothermal 'wear-out' experiments under oxygen yielding initial CL intensity and 'wear-out' time data, and temperature ramp experiments under inert conditions as a measure of previously accumulated hydroperoxides or other reactive species. The sensitivities of these CL features to prior aging exposure of the polymer were evaluated on the basismore » of qualifying this method as a quick screening technique for quantification of degradation levels. Both the techniques yielded data representing the aging trends in this material via correlation with mechanical property changes. Initial CL rates from the isothermal experiments are the most sensitive and suitable approach for documenting material changes during the early part of thermal aging.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Judd, C.; Thom, R; Borde, A.

The purpose of this study was to evaluate the ability to enhance distribution of eelgrass (Zostera marina) in the Columbia River Estuary to serve as refuge and feeding habitat for juvenile salmon, Dungeness crab, and other fish and wildlife. We strongly suspected that limited eelgrass seed dispersal has resulted in the present distribution of eelgrass meadows, and that there are other suitable places for eelgrass to survive and form functional meadows. Funded as part of the Bonneville Power Administration's call for Innovative Projects, we initiated a multistage study in 2008 that combined modeling, remote sensing, and field experimentation to: (1)more » Spatially predict habitat quality for eelgrass; (2) Conduct experimental plantings; and (3) Evaluate restoration potential. Baseline in-situ measurements and remote satellite observations were acquired for locations in the Lower Columbia River Estuary (LCRE) to determine ambient habitat conditions. These were used to create a habitat site-selection model, using data on salinity, temperature, current velocity, light availability, wave energy, and desiccation to predict the suitability of nearshore areas for eelgrass. Based on this model and observations in the field, five sites that contained no eelgrass but appeared to have suitable environmental conditions were transplanted with eelgrass in June 2008 to test the appropriateness of these sites for eelgrass growth. We returned one year after the initial planting to monitor the success rate of the transplants. During the year after transplanting, we carried out a concurrent study on crab distribution inside and outside eelgrass meadows to study crab usage of the habitat. One year after the initial transplant, two sites, one in Baker Bay and one in Young's Bay, had good survival or expansion rates with healthy eelgrass. Two sites had poor survival rates, and one site had a total loss of the transplanted eelgrass. For submerged aquatic vegetation (SAV) restoration projects, these are reasonable success results and represent a small net gain in eelgrass in the LCRE. Crabs used both the eelgrass and unvegetated substrate, though in neither were there great abundance of the young-of-the-year crabs. During the field assessment of 12 potential transplant sites, divers discovered one site in southern Young's Bay that contained a previously undocumented eelgrass bed. This integrated project developed the first predictive maps of sites suitable for eelgrass and other SAV in the lower estuary. In addition, techniques developed for this project to assess light levels in existing and potential submerged habitats have great potential to be used in other regions for nearshore and coastal monitoring of SAV. Based on these preliminary results, we conclude that eelgrass distribution could likely be expanded in the estuary, though additional information on current eelgrass locations, usage by species of interest, and monitoring of current conditions would help develop a baseline and verify benefit. Our recommendations for future studies include: (1) Site Monitoring. Continued monitoring of restoration sites along with physical metrics of light, temperature and salinity within beds. Continued monitoring will both assist managers in understanding the longevity and expansion rate of planted sites and inform practical guidance on the minimum planted eelgrass required to develop a resilient meadow. (2) Natural bed documentation and monitoring. Document current eelgrass habitat conditions in the Columbia River by mapping eelgrass and other SAV species and monitoring physical metrics in natural beds. This will assist by better defining the factors that control the annual and spatial variation in eelgrass in the estuary, and thus lead to improved management. Improved information on conditions will help refine a habitat suitability model that can more accurately predict where eelgrass can be restored or areas under duress. (3) Monitor Species Use. Expanded monitoring of Dungeness crab and salmon use and benefit from eelgrass in the estuary to evaluate how feeding and rearing functions of eelgrass benefit the survival and growth of these species. We have two final recommendations. First, if transplanting of eelgrass is to be expanded, donor stocks of plants should also be expanded to reduce the dependence on natural meadows. We recommend that an eelgrass culture facility be considered to supply stocks of eelgrass for planting that are developed from the eelgrass populations now in the estuary. Second, freshwater submerged aquatic vegetation (SAV) occurs in many parts of the estuary, and probably has importance to juvenile salmon (although this also needs verification). Restoration and expansion of freshwater SAV should be considered in a comprehensive effort to restore the submerged vegetation habitats through the Columbia River estuary.« less

Cognitive issues in autonomous spacecraft-control operations: An investigation of software-mediated decision making in a scaled environment

NASA Astrophysics Data System (ADS)

Murphy, Elizabeth Drummond

As advances in technology are applied in complex, semi-automated domains, human controllers are distanced from the controlled process. This physical and psychological distance may both facilitate and degrade human performance. To investigate cognitive issues in spacecraft ground-control operations, the present experimental research was undertaken. The primary issue concerned the ability of operations analysts who do not monitor operations to make timely, accurate decisions when autonomous software calls for human help. Another key issue involved the potential effects of spatial-visualization ability (SVA) in environments that present data in graphical formats. Hypotheses were derived largely from previous findings and predictions in the literature. Undergraduate psychology students were assigned at random to a monitoring condition or an on-call condition in a scaled environment. The experimental task required subjects to decide on the veracity of a problem diagnosis delivered by a software process on-board a simulated spacecraft. To support decision-making, tabular and graphical data displays presented information on system status. A level of software confidence in the problem diagnosis was displayed, and subjects reported their own level of confidence in their decisions. Contrary to expectations, the performance of on-call subjects did not differ significantly from that of continuous monitors. Analysis yielded a significant interaction of sex and condition: Females in the on-call condition had the lowest mean accuracy. Results included a preference for bar charts over line graphs and faster performance with tables than with line graphs. A significant correlation was found between subjective confidence and decision accuracy. SVA was found to be predictive of accuracy but not speed; and SVA was found to be a stronger predictor of performance for males than for females. Low-SVA subjects reported that they relied more on software confidence than did medium- or high-SVA subjects. These and other findings have implications for the design of user interfaces to support human decision-making in on-call situations and to accommodate low-SVA users.

Proactive monitoring of a wind turbine array with lidar measurements, SCADA data and a data-driven RANS solver

NASA Astrophysics Data System (ADS)

Iungo, G.; Said, E. A.; Santhanagopalan, V.; Zhan, L.

2016-12-01

Power production of a wind farm and durability of wind turbines are strongly dependent on non-linear wake interactions occurring within a turbine array. Wake dynamics are highly affected by the specific site conditions, such as topography and local atmospheric conditions. Furthermore, contingencies through the life of a wind farm, such as turbine ageing and off-design operations, make prediction of wake interactions and power performance a great challenge in wind energy. In this work, operations of an onshore wind turbine array were monitored through lidar measurements, SCADA and met-tower data. The atmospheric wind field investing the wind farm was estimated by using synergistically the available data through five different methods, which are characterized by different confidence levels. By combining SCADA data and the lidar measurements, it was possible to estimate power losses connected with wake interactions. For this specific array, power losses were estimated to be 4% and 2% of the total power production for stable and convective atmospheric regimes, respectively. The entire dataset was then leveraged for the calibration of a data-driven RANS (DDRANS) solver for prediction of wind turbine wakes and power production. The DDRANS is based on a parabolic formulation of the Navier-Stokes equations with axisymmetry and boundary layer approximations, which allow achieving very low computational costs. Accuracy in prediction of wind turbine wakes and power production is achieved through an optimal tuning of the turbulence closure model. The latter is based on a mixing length model, which was developed based on previous wind turbine wake studies carried out through large eddy simulations and wind tunnel experiments. Several operative conditions of the wind farm under examination were reproduced through DDRANS for different stability regimes, wind directions and wind velocity. The results show that DDRANS is capable of achieving a good level of accuracy in prediction of power production and wake velocity field associated with the turbine array.

Predictors of severe trunk postures among short-haul truck drivers during non-driving tasks: an exploratory investigation involving video-assessment and driver behavioural self-monitoring.

PubMed

Olson, R; Hahn, D I; Buckert, A

2009-06-01

Short-haul truck (lorry) drivers are particularly vulnerable to back pain and injury due to exposure to whole body vibration, prolonged sitting and demanding material handling tasks. The current project reports the results of video-based assessments (711 stops) and driver behavioural self-monitoring (BSM) (385 stops) of injury hazards during non-driving work. Participants (n = 3) worked in a trailer fitted with a camera system during baseline and BSM phases. Descriptive analyses showed that challenging customer environments and non-standard ingress/egress were prevalent. Statistical modelling of video-assessment results showed that each instance of manual material handling increased the predicted mean for severe trunk postures by 7%, while customer use of a forklift, moving standard pallets and moving non-standard pallets decreased predicted means by 12%, 20% and 22% respectively. Video and BSM comparisons showed that drivers were accurate at self-monitoring frequent environmental conditions, but less accurate at monitoring trunk postures and rare work events. The current study identified four predictors of severe trunk postures that can be modified to reduce risk of injury among truck drivers and showed that workers can produce reliable self-assessment data with BSM methods for frequent and easily discriminated events environmental.

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eden, H.F.; Mooers, C.N.K.

1990-06-01

The goal of COPS is to couple a program of regular observations to numerical models, through techniques of data assimilation, in order to provide a predictive capability for the US coastal ocean including the Great Lakes, estuaries, and the entire Exclusive Economic Zone (EEZ). The objectives of the program include: determining the predictability of the coastal ocean and the processes that govern the predictability; developing efficient prediction systems for the coastal ocean based on the assimilation of real-time observations into numerical models; and coupling the predictive systems for the physical behavior of the coastal ocean to predictive systems for biological,more » chemical, and geological processes to achieve an interdisciplinary capability. COPS will provide the basis for effective monitoring and prediction of coastal ocean conditions by optimizing the use of increased scientific understanding, improved observations, advanced computer models, and computer graphics to make the best possible estimates of sea level, currents, temperatures, salinities, and other properties of entire coastal regions.« less

Environmental auditing: Capabilities and management utility of recreation impact monitoring programs

USGS Publications Warehouse

Marion, J.L.

1995-01-01

A recreation impact monitoring system was developed and applied in 1984?1986 and in 1991 to all backcountry river-accessed campsites within Delaware Water Gap National Recreation Area, Pennsylvania and New Jersey. Results suggest that actions implemented by park managers in response to problems identified by the initial survey were highly effective in reducing resource degradation caused by camping. In particular, the elimination of some designated campsites and installation of anchored firegrates reduced the total area of disturbance by 50%. Firegrate installation provided a focal point that increased the concentration of camping activities, allowing peripheral areas to recover. As suggested by predictive models, additional resource degradation caused by increased camping intensities is more than offset by improvements in the condition of areas where use is eliminated. The capabilities and management utility of recreation impact monitoring programs, illustrated by the Delaware Water Gap monitoring program, are also presented and discussed.

Display/control requirements for automated VTOL aircraft

NASA Technical Reports Server (NTRS)

Hoffman, W. C.; Kleinman, D. L.; Young, L. R.

1976-01-01

A systematic design methodology for pilot displays in advanced commercial VTOL aircraft was developed and refined. The analyst is provided with a step-by-step procedure for conducting conceptual display/control configurations evaluations for simultaneous monitoring and control pilot tasks. The approach consists of three phases: formulation of information requirements, configuration evaluation, and system selection. Both the monitoring and control performance models are based upon the optimal control model of the human operator. Extensions to the conventional optimal control model required in the display design methodology include explicit optimization of control/monitoring attention; simultaneous monitoring and control performance predictions; and indifference threshold effects. The methodology was applied to NASA's experimental CH-47 helicopter in support of the VALT program. The CH-47 application examined the system performance of six flight conditions. Four candidate configurations are suggested for evaluation in pilot-in-the-loop simulations and eventual flight tests.

A new predictive indicator for development of pressure ulcers in bedridden patients based on common laboratory tests results.

PubMed

Hatanaka, N; Yamamoto, Y; Ichihara, K; Mastuo, S; Nakamura, Y; Watanabe, M; Iwatani, Y

2008-04-01

Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction. To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers. Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score. Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone. The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Electrical Resistance of Ceramic Matrix Composites for Damage Detection and Life-Prediction

NASA Technical Reports Server (NTRS)

Smith, Craig; Morscher, Gregory N.; Xia, Zhenhai

2008-01-01

The electric resistance of woven SiC fiber reinforced SiC matrix composites were measured under tensile loading conditions. The results show that the electrical resistance is closely related to damage and that real-time information about the damage state can be obtained through monitoring of the resistance. Such self-sensing capability provides the possibility of on-board/in-situ damage detection or inspection of a component during "down time". The correlation of damage with appropriate failure mechanism can then be applied to accurate life prediction for high-temperature ceramic matrix composites.

Soil-plant interaction monitoring: Small scale example of an apple orchard in Trentino, North-Eastern Italy.

PubMed

Cassiani, Giorgio; Boaga, Jacopo; Rossi, Matteo; Putti, Mario; Fadda, Giuseppe; Majone, Bruno; Bellin, Alberto

2016-02-01

Accurate monitoring and modeling of soil-plant systems are a key unresolved issue that currently limits the development of a comprehensive view of the interactions between soil and atmosphere, with a number of practical consequences including the difficulties in predicting climatic change patterns. This paper presents a case study where time-lapse minimal-invasive 3D micro-electrical tomography (ERT) is used to monitor rhizosphere eco-hydrological processes in an apple orchard in the Trentino region, Northern Italy. In particular we aimed at gaining a better understanding of the soil-vegetation water exchanges in the shallow critical zone, as part of a coordinated effort towards predicting climate-induced changes on the hydrology of Mediterranean basins (EU FP7 CLIMB project). The adopted strategy relied upon the installation of a 3D electrical tomography apparatus consisting of four mini-boreholes carrying 12 electrodes each plus 24 mini-electrodes on the ground surface, arranged in order to image roughly a cubic meter of soil surrounding a single apple tree. The monitoring program was initially tested with repeated measurements over about one year. Subsequently, we performed three controlled irrigation tests under different conditions, in order to evaluate the water redistribution under variable root activities and climatic conditions. Laboratory calibration on soil samples allowed us to translate electrical resistivity variations into moisture content changes, supported also by in-situ TDR measurements. Richards equation modeling was used also to explain the monitoring evidence. The results clearly identified the effect of root water uptake and the corresponding subsoil region where active roots are present, but also marked the need to consider the effects of different water salinity in the water infiltration process. We also gained significant insight about the need to measure quantitatively the plant evapotranspiration in order to close the water balance and separate soil structure effects (primarily, hydraulic conductivity) from water dynamics induced by living plants. Copyright © 2015 Elsevier B.V. All rights reserved.

Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain.

PubMed

Paret, Christian; Zähringer, Jenny; Ruf, Matthias; Gerchen, Martin Fungisai; Mall, Stephanie; Hendler, Talma; Schmahl, Christian; Ende, Gabriele

2018-03-30

Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation. © 2018 Wiley Periodicals, Inc.

Systems identification and application systems development for monitoring the physiological and health status of crewmen in space

NASA Technical Reports Server (NTRS)

Leonard, J. I.; Furukawa, S.; Vannordstrand, P. C.

1975-01-01

The use of automated, analytical techniques to aid medical support teams is suggested. Recommendations are presented for characterizing crew health in terms of: (1) wholebody function including physiological, psychological and performance factors; (2) a combination of critical performance indexes which consist of multiple factors of measurable parameters; (3) specific responses to low noise level stress tests; and (4) probabilities of future performance based on present and periodic examination of past performance. A concept is proposed for a computerized real time biomedical monitoring and health care system that would have the capability to integrate monitored data, detect off-nominal conditions based on current knowledge of spaceflight responses, predict future health status, and assist in diagnosis and alternative therapies. Mathematical models could play an important role in this approach, especially when operating in a real time mode. Recommendations are presented to update the present health monitoring systems in terms of recent advances in computer technology and biomedical monitoring systems.

Using forest inventory and analysis data and the forest vegetation simulator to predict and monitor fisher (Martes pennanti) resting habitat suitability

Treesearch

William J. Zielinski; Andrew N. Gray; Jeffrey R. Dunk; Joseph W. Sherlock; Gary E. Dixon

2010-01-01

New knowledge from wildlife-habitat relationship models is often difficult to implement in a management context. This can occur because researchers do not always consider whether managers have access to information about environmental covariates that permit the models to be applied. Moreover, ecosystem management requires knowledge about the condition of habitats over...

Implementation Considerations for Multisite Clinical Trials with Cognitive Neuroscience Tasks

PubMed Central

Keefe, Richard S. E.; Harvey, Philip D.

2008-01-01

Multisite clinical trials aimed at cognitive enhancement across various neuropsychiatric conditions have employed standard neuropsychological tests as outcome measures. While these tests have enjoyed wide clinical use and have proven reliable and predictive of functional disability, a number of implementation challenges have arisen when these tests are used in clinical trials. These issues are likely to be magnified in future studies when cognitive neuroscience (CN) procedures are explored in these trials, because in their current forms CN procedures are less standardized and more difficult to teach and monitor. For multisite trials, we anticipate that the most challenging issues will include assuring tester competence, monitoring tester performance, specific challenges with complex assessment methods, and having resources available for adequate monitoring of data quality. Suggestions for overcoming these implementation challenges are offered. PMID:18495645

Comparison of eight logger layouts for monitoring animal-level temperature and humidity during commercial feeder cattle transport.

PubMed

Goldhawk, C; Crowe, T; González, L A; Janzen, E; Kastelic, J; Pajor, E; Schwartzkopf-Genswein, K

2014-09-01

Measuring animal-level conditions during transit provides information regarding the true risk of environmental challenges to cattle welfare during transportation. However, due to constraints on placing loggers at the animal level, there is a need to identify appropriate proxy locations. The objective was to evaluate 8 distributions of ceiling-level loggers in the deck and belly compartments of pot-belly trailers for assessing animal-level temperature and humidity during 5 to 18 h commercial transportation of feeder cattle. Ambient conditions during transportation ranged from 3.6 to 45.2°C (20.3 ± 7.61°C, mean ± SD). When considering the entire journey, average differences between ceiling and animal-level temperatures were similar among logger layouts (P > 0.05). The uncertainty in the difference in temperature and humidity between locations was high relative to the magnitude of the difference between animal- and ceiling-level conditions. Single-logger layouts required larger adjustments to predict animal-level conditions within either compartment, during either the entire journey or when the trailer was stationary (P < 0.05). Within certain logger layouts, there were small but significant differences in the ability of regression equations to predict animal-level conditions that were associated with cattle weight and available space relative to body size. Furthermore, evaluation of logger layouts based solely on the entire journey without consideration of stationary periods did not adequately capture variability in layout performance. In conclusion, to adequately monitor animal-level temperature and humidity, 10 loggers distributed throughout the compartment was recommended over single-logger layouts within both the deck and belly compartments of pot-belly trailers transporting feeder cattle in warm weather.

Framework for Structural Online Health Monitoring of Aging and Degradation of Secondary Systems due to some Aspects of Erosion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gribok, Andrei; Patnaik, Sobhan; Williams, Christian

This report describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary components in nuclear power plants. The report also proposes a framework for online health monitoring of aging and degradation of secondary components. The framework consists of an integrated multi-sensor modality system which can be used to monitor different piping configurations under different degradation conditions. The report analyses the currently known degradation mechanisms and available predictive models. Based on this analysis, the structural health monitoring framework is proposed. The Light Water Reactor Sustainability Program began to evaluate technologies that couldmore » be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems (for example, in raw water piping or diesel piping) would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at answering this challenge by combining long-range guided wave technologies with other monitoring techniques, which can significantly increase the inspection length and pinpoint the locations that degraded the most. More widely, the report suggests research efforts aimed at developing, validating, and deploying online corrosion monitoring techniques for complex geometries, which are pervasive in NPPs.« less

A coupled aero-structural model of a HAWT blade for dynamic load and response prediction in time-domain for health monitoring applications

NASA Astrophysics Data System (ADS)

Sauder, Heather Scot

To reach the high standards set for renewable energy production in the US and around the globe, wind turbines with taller towers and longer blades are being designed for onshore and offshore wind developments to capture more energy from higher winds aloft and a larger rotor diameter. However, amongst all the wind turbine components wind turbine blades are still the most prone to damage. Given that wind turbine blades experience dynamic loads from multiple sources, there is a need to be able to predict the real-time load, stress distribution and response of the blade in a given wind environment for damage, flutter and fatigue life predictions. Current methods of wind-induced response analysis for wind turbine blades use approximations that are not suitable for wind turbine blade airfoils which are thick, and therefore lead to inaccurate life predictions. Additionally, a time-domain formulation can prove to be especially advantageous for predicting aerodynamic loads on wind turbine blades since they operate in a turbulent atmospheric boundary layer. This will help to analyze the blades on wind turbines that operate individually or in a farm setting where they experience high turbulence in the wake of another wind turbine. A time-domain formulation is also useful for examining the effects of gusty winds that are transient in nature like in gust fronts, thunderstorms or extreme events such as hurricanes, microbursts, and tornadoes. Time-domain methods present the opportunity for real-time health monitoring strategies that can easily be used with finite element methods for prediction of fatigue life or onset of flutter instability. The purpose of the proposed work is to develop a robust computational model to predict the loads, stresses and response of a wind turbine blade in operating and extreme wind conditions. The model can be used to inform health monitoring strategies for preventative maintenance and provide a realistic number of stress cycles that the blade will experience for fatigue life prediction procedures. To fill in the gaps in the existing knowledge and meet the overall goal of the proposed research, the following objectives were accomplished: (a) improve the existing aeroelastic (motion- and turbulence-induced) load models to predict the response of wind turbine blade airfoils to understand its behavior in turbulent wind, (b) understand, model and predict the response of wind turbine blades in transient or gusty wind, boundary-layer wind and incoherent wind over the span of the blade, (c) understand the effects of aero-structural coupling between the along-wind, cross-wind and torsional vibrations, and finally (d) develop a computational tool using the improved time-domain load model to predict the real-time load, stress distribution and response of a given wind turbine blade during operating and parked conditions subject to a specific wind environment both in a short and long term for damage, flutter and fatigue life predictions.

Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

NASA Technical Reports Server (NTRS)

Hadden, George D.; Mylaraswamy, Dinkar; Schimmel, Craig; Biswas, Gautam; Koutsoukos, Xenofon; Mack, Daniel

2011-01-01

Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two.

Fractional exhaled nitric oxide-measuring devices: technology update

PubMed Central

Maniscalco, Mauro; Vitale, Carolina; Vatrella, Alessandro; Molino, Antonio; Bianco, Andrea; Mazzarella, Gennaro

2016-01-01

The measurement of exhaled nitric oxide (NO) has been employed in the diagnosis of specific types of airway inflammation, guiding treatment monitoring by predicting and assessing response to anti-inflammatory therapy and monitoring for compliance and detecting relapse. Various techniques are currently used to analyze exhaled NO concentrations under a range of conditions for both health and disease. These include chemiluminescence and electrochemical sensor devices. The cost effectiveness and ability to achieve adequate flexibility in sensitivity and selectivity of NO measurement for these methods are evaluated alongside the potential for use of laser-based technology. This review explores the technologies involved in the measurement of exhaled NO. PMID:27382340

Prediction of landslide activation at locations in Beskidy Mountains using standard and real-time monitoring methods

NASA Astrophysics Data System (ADS)

Bednarczyk, Z.

2012-04-01

The paper presents landslide monitoring methods used for prediction of landslide activity at locations in the Carpathian Mountains (SE Poland). Different types of monitoring methods included standard and real-time early warning measurement with use of hourly data transfer to the Internet were used. Project financed from the EU funds was carried out for the purpose of public road reconstruction. Landslides with low displacement rates (varying from few mm to over 5cm/year) had size of 0.4-2.2mln m3. Flysch layers involved in mass movements represented mixture of clayey soils and sandstones of high moisture content and plasticity. Core sampling and GPR scanning were used for recognition of landslide size and depths. Laboratory research included index, IL oedometer, triaxial and direct shear laboratory tests. GPS-RTK mapping was employed for actualization of landslide morphology. Instrumentation consisted of standard inclinometers, piezometers and pore pressure transducers. Measurements were carried 2006-2011, every month. In May 2010 the first in Poland real-time monitoring system was installed at landslide complex over the Szymark-Bystra public road. It included in-place uniaxial sensors and 3D continuous inclinometers installed to the depths of 12-16m with tilt sensors every 0.5m. Vibrating wire pore pressure and groundwater level transducers together with automatic meteorological station analyzed groundwater and weather conditions. Obtained monitoring and field investigations data provided parameters for LEM and FEM slope stability analysis. They enabled prediction and control of landslide behaviour before, during and after stabilization or partly stabilization works. In May 2010 after the maximum precipitation (100mm/3hours) the rates of observed displacements accelerated to over 11cm in a few days and damaged few standard inclinometer installations. However permanent control of the road area was possible by continuous inclinometer installations. Comprehensive monitoring and modelling methods before the landslide counteraction stage could lead to a safer and more economical recognition of landslide remediation possibilities.

Real-time monitoring of high-gravity corn mash fermentation using in situ raman spectroscopy.

PubMed

Gray, Steven R; Peretti, Steven W; Lamb, H Henry

2013-06-01

In situ Raman spectroscopy was employed for real-time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm(-1) C-C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high- and low-concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two-set calibration models for starch (R(2)  = 0.998, percent error = 2.5%) and ethanol (R(2)  = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS-based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman-based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Copyright © 2013 Wiley Periodicals, Inc.

Evaluation of light penetration on Navigation Pools 8 and 13 of the Upper Mississippi River

USGS Publications Warehouse

Giblin, Shawn; Hoff, Kraig; Fischer, Jim; Dukerschein, Terry

2010-01-01

The availability of light can have a dramatic affect on macrophyte and phytoplankton abundance in virtually all aquatic ecosystems. The Long Term Resource Monitoring Program and other monitoring programs often measure factors that affect light extinction (nonvolatile suspended solids, volatile suspended solids, and chlorophyll) and correlates of light extinction (turbidity and Secchi depth), but rarely do they directly measure light extinction. Data on light extinction, Secchi depth, transparency tube, turbidity, total suspended solids, and volatile suspended solids were collected during summer 2003 on Pools 8 and 13 of the Upper Mississippi River. Regressions were developed to predict light extinction based upon Secchi depth, transparency tube, turbidity, and total suspended solids. Transparency tube, Secchi depth, and turbidity all showed strong relations with light extinction and can effectively predict light extinction. Total suspended solids did not show as strong a relation to light extinction. Volatile suspended solids had a greater affect on light extinction than nonvolatile suspended solids. The data were compared to recommended criteria established for light extinction, Secchi depth, total suspended solids, and turbidity by the Upper Mississippi River Conservation Committee to sustain submersed aquatic vegetation in the Upper Mississippi River. During the study period, the average condition in Pool 8 met or exceeded all of the criteria whereas the average condition in Pool 13 failed to meet any of the criteria. This report provides river managers with an effective tool to predict light extinction based upon readily available data.

The impact of warming on greenhouse gas fluxes: an experimental comparison which reveals the varied response of ecosystems to climate change.

NASA Astrophysics Data System (ADS)

Stockdale, James; Ineson, Philip

2016-04-01

Modelled predictions of the response of terrestrial systems to climate change are highly variable, yet the response of net ecosystem exchange (NEE) is a vital ecosystem behaviour to understand due to its inherent feedback to the carbon cycle. The establishment and subsequent monitoring of replicated experimental manipulations are a direct method to reveal these responses, yet are difficult to achieve as they typically resource-heavy and labour intensive. We actively manipulated the temperature at three agricultural grasslands in southern England and deployed novel 'SkyLine' systems, recently developed at the University of York, to continuously monitor GHG fluxes. Each 'SkyLine' is a low-cost and fully autonomous technology yet produces fluxes at a near-continuous temporal frequency and across a wide spatial area. The results produced by 'SkyLine' enable the detail response of each system to increased temperature over diurnal and seasonal timescales. Unexpected differences in NEE are shown between superficially similar ecosystems which, upon investigation, suggest that interactions between a variety of environmental variables are key and that knowledge of pre-existing environmental conditions help to predict a systems response to future climate. For example, the prevailing hydrological conditions at each site appear to affect its response to changing temperature. The high-frequency data shown here, combined with the fully-replicated experimental design reveal complex interactions which must be understood to improve predictions of ecosystem response to a changing climate.

Integration and Assessment of Component Health Prognostics in Supervisory Control Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Bonebrake, Christopher A.; Dib, Gerges

Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize real-time information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant.more » ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation.« less

Environmental Monitoring of Endemic Cholera

NASA Astrophysics Data System (ADS)

ElNemr, W.; Jutla, A. S.; Constantin de Magny, G.; Hasan, N. A.; Islam, M.; Sack, R.; Huq, A.; Hashem, F.; Colwell, R.

2012-12-01

Cholera remains a major public health threat. Since Vibrio cholerae, the causative agent of the disease, is autochthonous to riverine, estuarine, and coastal waters, it is unlikely the bacteria can be eradicated from its natural habitat. Prediction of disease, in conjunction with preventive vaccination can reduce the prevalence rate of a disease. Understanding the influence of environmental parameters on growth and proliferation of bacteria is an essential first step in developing prediction methods for outbreaks. Large scale geophysical variables, such as SST and coastal chlorophyll, are often associated with conditions favoring growth of V. cholerae. However, local environmental factors, meaning biological activity in ponds from where the bulk of populations in endemic regions derive water for daily usage, are either neglected or oversimplified. Using data collected from several sites in two geographically distinct locations in South Asia, we have identified critical local environmental factors associated with cholera outbreak. Of 18 environmental variables monitored for water sources in Mathbaria (a coastal site near the Bay of Bengal) and Bakergonj (an inland site) of Bangladesh, water depth and chlorophyll were found to be important factors associated with initiation of cholera outbreaks. Cholera in coastal regions appears to be related to intrusion. However, monsoonal flooding creates conditions for cholera epidemics in inland regions. This may be one of the first attempts to relate in-situ environmental observations with cholera. We anticipate that it will be useful for further development of prediction models in the resource constrained regions.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Peat conditions mapping using MODIS time series

NASA Astrophysics Data System (ADS)

Poggio, Laura; Gimona, Alessandro; Bruneau, Patricia; Johnson, Sally; McBride, Andrew; Artz, Rebekka

2016-04-01

Large areas of Scotland are covered in peatlands, providing an important sink of carbon in their near natural state but act as a potential source of gaseous and dissolved carbon emission if not in good conditions. Data on the condition of most peatlands in Scotland are, however, scarce and largely confined to sites under nature protection designations, often biased towards sites in better condition. The best information available at present is derived from labour intensive field-based monitoring of relatively few designated sites (Common Standard Monitoring Dataset). In order to provide a national dataset of peat conditions, the available point information from the CSM data was modelled with morphological features and information derived from MODIS sensor. In particular we used time series of indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity (Gross Primary productivity). A scorpan-kriging approach was used, in particular using Generalised Additive Models for the description of the trend. The model provided the probability of a site to be in favourable conditions and the uncertainty of the predictions was taken into account. The internal validation (leave-one-out) provided a mis-classification error of around 0.25. The derived dataset was then used, among others, in the decision making process for the selection of sites for restoration.

The importance of measuring growth in response to intervention models: Testing a core assumption✩

PubMed Central

Schatschneider, Christopher; Wagner, Richard K.; Crawford, Elizabeth C.

2011-01-01

A core assumption of response to instruction or intervention (RTI) models is the importance of measuring growth in achievement over time in response to effective instruction or intervention. Many RTI models actively monitor growth for identifying individuals who need different levels of intervention. A large-scale (N=23,438), two-year longitudinal study of first grade children was carried out to compare the predictive validity of measures of achievement status, growth in achievement, and their combination for predicting future reading achievement. The results indicate that under typical conditions, measures of growth do not make a contribution to prediction that is independent of measures of achievement status. These results question the validity of a core assumption of RTI models. PMID:22224065

Regional Cerebral Oximetry During Cardiopulmonary Resuscitation: Useful or Useless?

PubMed

Genbrugge, Cornelia; Dens, Jo; Meex, Ingrid; Boer, Willem; Eertmans, Ward; Sabbe, Marc; Jans, Frank; De Deyne, Cathy

2016-01-01

Approximately 375,000 people annually experience sudden cardiac arrest (CA) in Europe. Most patients who survive the initial hours and days after CA die of postanoxic brain damage. Current monitors, such as electrocardiography and end-tidal capnography, provide only indirect information about the condition of the brain during cardiopulmonary resuscitation (CPR). In contrast, cerebral near-infrared spectroscopy provides continuous, noninvasive, real-time information about brain oxygenation without the need for a pulsatile blood flow. It measures transcutaneous cerebral tissue oxygen saturation (rSO2). This information could supplement currently used monitors. Moreover, an evolution in rSO2 monitoring technology has made it easier to assess rSO2 in CA conditions. We give an overview of the literature regarding rSO2 measurements during CPR and the current commercially available devices. We highlight the feasibility of cerebral saturation measurement during CPR, its role as a quality parameter of CPR, predictor of return of spontaneous circulation (ROSC) and neurologic outcome, and its monitoring function during transport. rSO2 is feasible in the setting of CA and has the potential to measure the quality of CPR, predict ROSC and neurologic outcome, and monitor post-CA patients during transport. The literature shows that rSO2 has the potential to serve multiple roles as a neuromonitoring tool during CPR and also to guide neuroprotective therapeutic strategies. Copyright © 2016 Elsevier Inc. All rights reserved.

Establishment of a standard operating procedure for predicting the time of calving in cattle

PubMed Central

Sauter-Louis, Carola; Braunert, Anna; Lange, Dorothee; Weber, Frank; Zerbe, Holm

2011-01-01

Precise calving monitoring is essential for minimizing the effects of dystocia in cows and calves. We conducted two studies in healthy cows that compared seven clinical signs (broad pelvic ligaments relaxation, vaginal secretion, udder hyperplasia, udder edema, teat filling, tail relaxation, and vulva edema) alone and in combination in order to predict the time of parturition. The relaxation of the broad pelvic ligaments combined with teat filling gave the best values for predicting either calving or no calving within 12 h. For the proposed parturition score (PS), a threshold of 4 PS points was identified below which calving within the next 12 h could be ruled out with a probability of 99.3% in cows (95.5% in heifers). Above this threshold, intermitted calving monitoring every 3 h and a progesterone rapid blood test (PRBT) would be recommended. By combining the PS and PRBT (if PS ≥ 4), the prediction of calving within the next 12 h improved from 14.9% to 53.1%, and the probability of ruling out calving was 96.8%. The PRBT was compared to the results of an enzyme immunoassay (sensitivity, 90.2%; specificity, 74.9%). The standard operating procedure developed in this study that combines the PS and PRBT will enable veterinarians to rule out or predict calving within a 12 h period in cows with high accuracy under field conditions. PMID:21586878

Methods and on-farm devices to predict calving time in cattle.

PubMed

Saint-Dizier, Marie; Chastant-Maillard, Sylvie

2015-09-01

In livestock farming, accurate prediction of calving time is a key factor for profitability and animal welfare. The most accurate and sensitive methods to date for prediction of calving within 24 h are the measurement of pelvic ligament relaxation and assays for circulating progesterone and oestradiol-17β. Conversely, the absence of calving within the next 12-24 h can be accurately predicted by the measurement of incremental daily decrease in vaginal temperature and by the combination of pelvic ligament relaxation and teat filling estimates. Continuous monitoring systems can detect behavioural changes occurring on the actual day of calving, some of them being accentuated in the last few hours before delivery; standing/lying transitions, tail raising, feeding time, and dry matter and water intakes differ between cows with dystocia and those with eutocia. Use of these behavioural changes has the potential to improve the management of calving. Currently, four types of devices for calving detection are on the market: inclinometers and accelerometers detecting tail raising and overactivity, abdominal belts monitoring uterine contractions, vaginal probes detecting a decrease in vaginal temperature and expulsion of the allantochorion, and devices placed in the vagina or on the vulvar lips that detect calf expulsion. The performance of these devices under field conditions and their capacity to predict dystocia require further investigation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal.

PubMed

Silva, Carmen; Cabral, João Alexandre; Hughes, Samantha Jane; Santos, Mário

2017-03-01

Worldwide ecological impact assessments of wind farms have gathered relevant information on bat activity patterns. Since conventional bat study methods require intensive field work, the prediction of bat activity might prove useful by anticipating activity patterns and estimating attractiveness concomitant with the wind farm location. A novel framework was developed, based on the stochastic dynamic methodology (StDM) principles, to predict bat activity on mountain ridges with wind farms. We illustrate the framework application using regional data from North Portugal by merging information from several environmental monitoring programmes associated with diverse wind energy facilities that enable integrating the multifactorial influences of meteorological conditions, land cover and geographical variables on bat activity patterns. Output from this innovative methodology can anticipate episodes of exceptional bat activity, which, if correlated with collision probability, can be used to guide wind farm management strategy such as halting wind turbines during hazardous periods. If properly calibrated with regional gradients of environmental variables from mountain ridges with windfarms, the proposed methodology can be used as a complementary tool in environmental impact assessments and ecological monitoring, using predicted bat activity to assist decision making concerning the future location of wind farms and the implementation of effective mitigation measures. Copyright © 2016 Elsevier B.V. All rights reserved.

Modelling spoilage of fresh turbot and evaluation of a time-temperature integrator (TTI) label under fluctuating temperature.

PubMed

Nuin, Maider; Alfaro, Begoña; Cruz, Ziortza; Argarate, Nerea; George, Susie; Le Marc, Yvan; Olley, June; Pin, Carmen

2008-10-31

Kinetic models were developed to predict the microbial spoilage and the sensory quality of fresh fish and to evaluate the efficiency of a commercial time-temperature integrator (TTI) label, Fresh Check(R), to monitor shelf life. Farmed turbot (Psetta maxima) samples were packaged in PVC film and stored at 0, 5, 10 and 15 degrees C. Microbial growth and sensory attributes were monitored at regular time intervals. The response of the Fresh Check device was measured at the same temperatures during the storage period. The sensory perception was quantified according to a global sensory indicator obtained by principal component analysis as well as to the Quality Index Method, QIM, as described by Rahman and Olley [Rahman, H.A., Olley, J., 1984. Assessment of sensory techniques for quality assessment of Australian fish. CSIRO Tasmanian Regional Laboratory. Occasional paper n. 8. Available from the Australian Maritime College library. Newnham. Tasmania]. Both methods were found equally valid to monitor the loss of sensory quality. The maximum specific growth rate of spoilage bacteria, the rate of change of the sensory indicators and the rate of change of the colour measurements of the TTI label were modelled as a function of temperature. The temperature had a similar effect on the bacteria, sensory and Fresh Check kinetics. At the time of sensory rejection, the bacterial load was ca. 10(5)-10(6) cfu/g. The end of shelf life indicated by the Fresh Check label was close to the sensory rejection time. The performance of the models was validated under fluctuating temperature conditions by comparing the predicted and measured values for all microbial, sensory and TTI responses. The models have been implemented in a Visual Basic add-in for Excel called "Fish Shelf Life Prediction (FSLP)". This program predicts sensory acceptability and growth of spoilage bacteria in fish and the response of the TTI at constant and fluctuating temperature conditions. The program is freely available at http://www.azti.es/muestracontenido.asp?idcontenido=980&content=15&nodo1=30&nodo2=0.

Wearable, Flexible, and Multifunctional Healthcare Device with an ISFET Chemical Sensor for Simultaneous Sweat pH and Skin Temperature Monitoring.

PubMed

Nakata, Shogo; Arie, Takayuki; Akita, Seiji; Takei, Kuniharu

2017-03-24

Real-time daily healthcare monitoring may increase the chances of predicting and diagnosing diseases in their early stages which, currently, occurs most frequently during medical check-ups. Next-generation noninvasive healthcare devices, such as flexible multifunctional sensor sheets designed to be worn on skin, are considered to be highly suitable candidates for continuous real-time health monitoring. For healthcare applications, acquiring data on the chemical state of the body, alongside physical characteristics such as body temperature and activity, are extremely important for predicting and identifying potential health conditions. To record these data, in this study, we developed a wearable, flexible sweat chemical sensor sheet for pH measurement, consisting of an ion-sensitive field-effect transistor (ISFET) integrated with a flexible temperature sensor: we intend to use this device as the foundation of a fully integrated, wearable healthcare patch in the future. After characterizing the performance, mechanical flexibility, and stability of the sensor, real-time measurements of sweat pH and skin temperature are successfully conducted through skin contact. This flexible integrated device has the potential to be developed into a chemical sensor for sweat for applications in healthcare and sports.

Brain tissue partial pressure of oxygen predicts the outcome of severe traumatic brain injury under mild hypothermia treatment.

PubMed

Sun, Hongtao; Zheng, Maohua; Wang, Yanmin; Diao, Yunfeng; Zhao, Wanyong; Wei, Zhengjun

2016-01-01

The aim of this study was to investigate the clinical significance and changes of brain tissue partial pressure of oxygen (PbtO2) in the course of mild hypothermia treatment (MHT) for treating severe traumatic brain injury (sTBI). There were 68 cases with sTBI undergoing MHT. PbtO2, intracranial pressure (ICP), jugular venous oxygen saturation (SjvO2), and cerebral perfusion pressure (CPP) were continuously monitored, and clinical outcomes were evaluated using the Glasgow Outcome Scale score. Of 68 patients with sTBI, PbtO2, SjvO2, and CPP were obviously increased, but decreased ICP level was observed throughout the MHT. PbtO2 and ICP were negatively linearly correlated, while there was a positive linear correlation between PbtO2 and SjvO2. Monitoring CPP and SjvO2 was performed under normal circumstances, and a large proportion of patients were detected with low PbtO2. Decreased PbtO2 was also found after MHT. Continuous PbtO2 monitoring could be introduced to evaluate the condition of regional cerebral oxygen metabolism, thereby guiding the clinical treatment and predicting the outcome.

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

NASA Technical Reports Server (NTRS)

Dong, Jiarui; Ek, Mike; Hall, Dorothy K.; Peters-Lidard, Christa; Cosgrove, Brian; Miller, Jeff; Riggs, George A.; Xia, Youlong

2013-01-01

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy.

PubMed

Caduff, A; Dewarrat, F; Talary, M; Stalder, G; Heinemann, L; Feldman, Yu

2006-12-15

The aim of this work was to evaluate the performance of a novel non-invasive continuous glucose-monitoring system based on impedance spectroscopy (IS) in patients with diabetes. Ten patients with type 1 diabetes (mean+/-S.D., age 28+/-8 years, BMI 24.2+/-3.2 kg/m(2) and HbA(1C) 7.3+/-1.6%) and five with type 2 diabetes (age 61+/-8 years, BMI 27.5+/-3.2 kg/m(2) and HbA(1C) 8.3+/-1.8%) took part in this study, which comprised a glucose clamp experiment followed by a 7-day outpatient evaluation. The measurements obtained by the NI-CGMD and the reference blood glucose-measuring techniques were evaluated using retrospective data evaluation procedures. Under less controlled outpatient conditions a correlation coefficient of r=0.640 and a standard error of prediction (SEP) of 45 mg dl(-1) with a total of 590 paired glucose measurements was found (versus r=0.926 and a SEP of 26 mg dl(-1) under controlled conditions). Clark error grid analyses (EGA) showed 56% of all values in zone A, 37% in B and 7% in C-E. In conclusion, these results indicate that IS in the used technical setting allows retrospective, continuous and truly non-invasive glucose monitoring under defined conditions for patients with diabetes. Technical advances and developments are needed to expand on this concept to bring the results from the outpatient study closer to those in the experimental section of the study. Further studies will not only help to evaluate the performance and limitations of using such a technique for non non-invasive glucose monitoring but also help to verify technical extensions towards a IS-based concept that offers improved performance under real life operating conditions.

Space-Time Urban Air Pollution Forecasts

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.; Soares, A.

2012-04-01

Air pollution, like other natural phenomena, may be considered a space-time process. However, the simultaneous integration of time and space is not an easy task to perform, due to the existence of different uncertainties levels and data characteristics. In this work we propose a hybrid method that combines geostatistical and neural models to analyze PM10 time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period and to produce forecasts. Geostatistical models have been widely used to characterize air pollution in urban areas, where the pollutant sources are considered diffuse, and also to industrial areas with localized emission sources. It should be stressed however that most geostatistical models correspond basically to an interpolation methodology (estimation, simulation) of a set of variables in a spatial or space-time domain. The temporal prediction of a pollutant usually requires knowledge of the main trends and complex patterns of physical dispersion phenomenon. To deal with low resolution problems and to enhance reliability of predictions, an approach based on neural network short term predictions in the monitoring stations which behave as a local conditioner to a fine grid stochastic simulation model is presented here. After the pollutant concentration is predicted for a given time period at the monitoring stations, we can use the local conditional distributions of observed values, given the predicted value for that period, to perform the spatial simulations for the entire area and consequently evaluate the spatial uncertainty of pollutant concentration. To attain this objective, we propose the use of direct sequential simulations with local distributions. With this approach one succeed to predict the space-time distribution of pollutant concentration that accounts for the time prediction uncertainty (reflecting the neural networks efficiency at each local monitoring station) and the spatial uncertainty as revealed by the spatial variograms. The dataset used consists of PM10 concentrations recorded hourly by 12 monitoring stations within the Lisbon's area, for the period 2002-2006. In addition, meteorological data recorded at 3 monitoring stations and boundary layer height (BLH) daily values from the ECMWF (European Centre for Medium Weather Forecast), ERA Interim, were also used. Based on the large-scale standard pressure fields from the ERA40/ECMWF, prevailing circulation patterns at regional scale where determined and used on the construction of the models. After the daily forecasts were produced, the difference between the average maps based on real observations and predicted values were determined and the model's performance was assessed. Based on the analysis of the results, we conclude that the proposed approach shows to be a very promising alternative for urban air quality characterization because of its good results and simplicity of application.

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents

PubMed Central

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-01-01

CONTEXT Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. METHODS Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13–17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. RESULTS For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. CONCLUSION Results highlight the important influence of peer risk involvement on youths’ sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior. PMID:26308261

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents.

PubMed

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-06-01

Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13-17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. Results highlight the important influence of peer risk involvement on youths' sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior.

Physics-of-Failure Approach to Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Forecasting system predicts presence of sea nettles in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Brown, Christopher W.; Hood, Raleigh R.; Li, Zhen; Decker, Mary Beth; Gross, Thomas F.; Purcell, Jennifer E.; Wang, Harry V.

Outbreaks of noxious biota, which occur in both aquatic and terrestrial systems, can have considerable negative economic impacts. For example, an increasing frequency of harmful algal blooms worldwide has negatively affected the tourism industry in many regions. Such impacts could be mitigated if the conditions that give rise to these outbreaks were known and could be monitored. Recent advances in technology and communications allow us to continuously measure and model many environmental factors that are responsible for outbreaks of certain noxious organisms. A new prototype ecological forecasting system predicts the likelihood of occurrence of the sea nettle (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay.

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

NASA Technical Reports Server (NTRS)

Funk, Chris; Peterson, Pete; Shukla, Shraddhanand; Husak, Gregory; Landsfeld, Marty; Hoell, Andrew; Pedreros, Diego; Roberts, J. B.; Robertson, F. R.; Tadesse, Tsegae;

Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban

2017-01-01

Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice.

Estimating natural monthly streamflows in California and the likelihood of anthropogenic modification

USGS Publications Warehouse

Carlisle, Daren M.; Wolock, David M.; Howard, Jeannette K.; Grantham, Theodore E.; Fesenmyer, Kurt; Wieczorek, Michael

2016-12-12

Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be experiencing anthropogenic flow modification.

Impact of Initial Condition Errors and Precipitation Forecast Bias on Drought Simulation and Prediction in the Huaihe River Basin

NASA Astrophysics Data System (ADS)

Xu, H.; Luo, L.; Wu, Z.

2016-12-01

Drought, regarded as one of the major disasters all over the world, is not always easy to detect and forecast. Hydrological models coupled with Numerical Weather Prediction (NWP) has become a relatively effective method for drought monitoring and prediction. The accuracy of hydrological initial condition (IC) and the skill of NWP precipitation forecast can both heavily affect the quality and skill of hydrological forecast. In the study, the Variable Infiltration Capacity (VIC) model and Global Environmental Multi-scale (GEM) model were used to investigate the roles of IC and NWP forecast accuracy on hydrological predictions. A rev-ESP type experiment was conducted for a number of drought events in the Huaihe river basin. The experiment suggests that errors in ICs indeed affect the drought simulations by VIC and thus the drought monitoring. Although errors introduced in the ICs diminish gradually, the influence sometimes can last beyond 12 months. Using the soil moisture anomaly percentage index (SMAPI) as the metric to measure drought severity for the study region, we are able to quantify that time scale of influence from IC ranges. The analysis shows that the time scale is directly related to the magnitude of the introduced IC range and the average precipitation intensity. In order to explore how systematic bias correction in GEM forecasted precipitation can affect precipitation and hydrological forecast, we then both used station and gridded observations to eliminate biases of forecasted data. Meanwhile, different precipitation inputs with corrected data during drought process were conducted by VIC to investigate the changes of drought simulations, thus demonstrated short-term rolling drought prediction using a better performed corrected precipitation forecast. There is a word limit on the length of the abstract. So make sure your abstract fits the requirement. If this version is too long, try to shorten it as much as you can.

Real-time emissions from construction equipment compared with model predictions.

PubMed

Heidari, Bardia; Marr, Linsey C

2015-02-01

The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO), nitrogen oxides (NOx), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases.

A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

PubMed

Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim

2017-02-01

In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Energy index decomposition methodology at the plant level

NASA Astrophysics Data System (ADS)

Kumphai, Wisit

Scope and method of study. The dissertation explores the use of a high level energy intensity index as a facility-level energy performance monitoring indicator with a goal of developing a methodology for an economically based energy performance monitoring system that incorporates production information. The performance measure closely monitors energy usage, production quantity, and product mix and determines the production efficiency as a part of an ongoing process that would enable facility managers to keep track of and, in the future, be able to predict when to perform a recommissioning process. The study focuses on the use of the index decomposition methodology and explored several high level (industry, sector, and country levels) energy utilization indexes, namely, Additive Log Mean Divisia, Multiplicative Log Mean Divisia, and Additive Refined Laspeyres. One level of index decomposition is performed. The indexes are decomposed into Intensity and Product mix effects. These indexes are tested on a flow shop brick manufacturing plant model in three different climates in the United States. The indexes obtained are analyzed by fitting an ARIMA model and testing for dependency between the two decomposed indexes. Findings and conclusions. The results concluded that the Additive Refined Laspeyres index decomposition methodology is suitable to use on a flow shop, non air conditioned production environment as an energy performance monitoring indicator. It is likely that this research can be further expanded in to predicting when to perform a recommissioning process.

Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition.

PubMed

Lu, Wei; Teng, Jun; Zhou, Qiushi; Peng, Qiexin

2018-02-01

The stress in structural steel members is the most useful and directly measurable physical quantity to evaluate the structural safety in structural health monitoring, which is also an important index to evaluate the stress distribution and force condition of structures during structural construction and service phases. Thus, it is common to set stress as a measure in steel structural monitoring. Considering the economy and the importance of the structural members, there are only a limited number of sensors that can be placed, which means that it is impossible to obtain the stresses of all members directly using sensors. This study aims to develop a stress response prediction method for locations where there are insufficent sensors, using measurements from a limited number of sensors and pattern recognition. The detailed improved aspects are: (1) a distributed computing process is proposed, where the same pattern is recognized by several subsets of measurements; and (2) the pattern recognition using the subset of measurements is carried out by considering the optimal number of sensors and number of fusion patterns. The validity and feasibility of the proposed method are verified using two examples: the finite-element simulation of a single-layer shell-like steel structure, and the structural health monitoring of the space steel roof of Shenzhen Bay Stadium; for the latter, the anti-noise performance of this method is verified by the stress measurements from a real-world project.

The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying.

PubMed

Onwude, Daniel I; Hashim, Norhashila; Abdan, Khalina; Janius, Rimfiel; Chen, Guangnan

2018-03-01

Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding

NASA Astrophysics Data System (ADS)

Wan, Xiaodong; Wang, Yuanxun; Zhao, Dawei; Huang, YongAn

2017-09-01

Our study aims at developing an effective quality monitoring system in small scale resistance spot welding of titanium alloy. The measured electrical signals were interpreted in combination with the nugget development. Features were extracted from the dynamic resistance and electrode voltage curve. A higher welding current generally indicated a lower overall dynamic resistance level. A larger electrode voltage peak and higher change rate of electrode voltage could be detected under a smaller electrode force or higher welding current condition. Variation of the extracted features and weld quality was found more sensitive to the change of welding current than electrode force. Different neural network model were proposed for weld quality prediction. The back propagation neural network was more proper in failure load estimation. The probabilistic neural network model was more appropriate to be applied in quality level classification. A real-time and on-line weld quality monitoring system may be developed by taking advantages of both methods.

An ensemble-based algorithm for optimizing the configuration of an in situ soil moisture monitoring network

NASA Astrophysics Data System (ADS)

De Vleeschouwer, Niels; Verhoest, Niko E. C.; Gobeyn, Sacha; De Baets, Bernard; Verwaeren, Jan; Pauwels, Valentijn R. N.

2015-04-01

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological modeling. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the smaller temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typically small integration volume of in situ measurements, and the often large spacing between monitoring locations. This causes only a small part of the modeling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically non-dynamic in time. Generally, e.g. when applying data assimilation, maximizing the observed information under given circumstances will lead to a better qualitative and quantitative insight of the hydrological system. It is therefore advisable to perform a prior analysis in order to select those monitoring locations which are most predictive for the unobserved modeling domain. This research focuses on optimizing the configuration of a soil moisture monitoring network in the catchment of the Bellebeek, situated in Belgium. A recursive algorithm, strongly linked to the equations of the Ensemble Kalman Filter, has been developed to select the most predictive locations in the catchment. The basic idea behind the algorithm is twofold. On the one hand a minimization of the modeled soil moisture ensemble error covariance between the different monitoring locations is intended. This causes the monitoring locations to be as independent as possible regarding the modeled soil moisture dynamics. On the other hand, the modeled soil moisture ensemble error covariance between the monitoring locations and the unobserved modeling domain is maximized. The latter causes a selection of monitoring locations which are more predictive towards unobserved locations. The main factors that will influence the outcome of the algorithm are the following: the choice of the hydrological model, the uncertainty model applied for ensemble generation, the general wetness of the catchment during which the error covariance is computed, etc. In this research the influence of the latter two is examined more in-depth. Furthermore, the optimal network configuration resulting from the newly developed algorithm is compared to network configurations obtained by two other algorithms. The first algorithm is based on a temporal stability analysis of the modeled soil moisture in order to identify catchment representative monitoring locations with regard to average conditions. The second algorithm involves the clustering of available spatially distributed data (e.g. land cover and soil maps) that is not obtained by hydrological modeling.

Research frontiers for improving our understanding of drought‐induced tree and forest mortality

USGS Publications Warehouse

Hartmann, Henrik; Moura, Catarina; Anderegg, William R. L.; Ruehr, Nadine; Salmon, Yann; Allen, Craig D.; Arndt, Stefan K.; Breshears, David D.; Davi, Hendrik; Galbraith, David; Ruthrof, Katinka X.; Wunder, Jan; Adams, Henry D.; Bloemen, Jasper; Cailleret, Maxime; Cobb, Richard; Gessler, Arthur; Grams, Thorsten E. E.; Jansen, Steven; Kautz, Markus; Lloret, Francisco; O’Brien, Michael

2018-01-01

Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die‐off events have severe consequences for ecosystem services, biophysical and biogeochemical land–atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die‐off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought‐induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.

Slip-mediated dewetting of polymer microdroplets

PubMed Central

McGraw, Joshua D.; Chan, Tak Shing; Maurer, Simon; Salez, Thomas; Benzaquen, Michael; Raphaël, Elie; Brinkmann, Martin; Jacobs, Karin

2016-01-01

Classical hydrodynamic models predict that infinite work is required to move a three-phase contact line, defined here as the line where a liquid/vapor interface intersects a solid surface. Assuming a slip boundary condition, in which the liquid slides against the solid, such an unphysical prediction is avoided. In this article, we present the results of experiments in which a contact line moves and where slip is a dominating and controllable factor. Spherical cap-shaped polystyrene microdroplets, with nonequilibrium contact angle, are placed on solid self-assembled monolayer coatings from which they dewet. The relaxation is monitored using in situ atomic force microscopy. We find that slip has a strong influence on the droplet evolutions, both on the transient nonspherical shapes and contact line dynamics. The observations are in agreement with scaling analysis and boundary element numerical integration of the governing Stokes equations, including a Navier slip boundary condition. PMID:26787903

Development of self-powered wireless high temperature electrochemical sensor for in situ corrosion monitoring of coal-fired power plant.

PubMed

Aung, Naing Naing; Crowe, Edward; Liu, Xingbo

2015-03-01

Reliable wireless high temperature electrochemical sensor technology is needed to provide in situ corrosion information for optimal predictive maintenance to ensure a high level of operational effectiveness under the harsh conditions present in coal-fired power generation systems. This research highlights the effectiveness of our novel high temperature electrochemical sensor for in situ coal ash hot corrosion monitoring in combination with the application of wireless communication and an energy harvesting thermoelectric generator (TEG). This self-powered sensor demonstrates the successful wireless transmission of both corrosion potential and corrosion current signals to a simulated control room environment. Copyright © 2014 ISA. All rights reserved.

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Coble, Jamie B.; Shumaker, Brent

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or moremore » sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

Physical condition and stress levels during early development reflect feeding rates and predict pre- and post-fledging survival in a nearshore seabird

USGS Publications Warehouse

Lamb, Juliet S.; O'Reilly, Kathleen M.; Jodice, Patrick G.R.

2016-01-01

The effects of acute environmental stressors on reproduction in wildlife are often difficult to measure because of the labour and disturbance involved in collecting accurate reproductive data. Stress hormones represent a promising option for assessing the effects of environmental perturbations on altricial young; however, it is necessary first to establish how stress levels are affected by environmental conditions during development and whether elevated stress results in reduced survival and recruitment rates. In birds, the stress hormone corticosterone is deposited in feathers during the entire period of feather growth, making it an integrated measure of background stress levels during development. We tested the utility of feather corticosterone levels in 3- to 4-week-old nestling brown pelicans (Pelecanus occidentalis) for predicting survival rates at both the individual and colony levels. We also assessed the relationship of feather corticosterone to nestling body condition and rates of energy delivery to nestlings. Chicks with higher body condition and lower corticosterone levels were more likely to fledge and to be resighted after fledging, whereas those with lower body condition and higher corticosterone levels were less likely to fledge or be resighted after fledging. Feather corticosterone was also associated with intracolony differences in survival between ground and elevated nest sites. Colony-wide, mean feather corticosterone predicted nest productivity, chick survival and post-fledging dispersal more effectively than did body condition, although these relationships were strongest before fledglings dispersed away from the colony. Both reproductive success and nestling corticosterone were strongly related to nutritional conditions, particularly meal delivery rates. We conclude that feather corticosterone is a powerful predictor of reproductive success and could provide a useful metric for rapidly assessing the effects of changes in environmental conditions, provided pre-existing baseline variation is monitored and understood.

Physical condition and stress levels during early development reflect feeding rates and predict pre- and post-fledging survival in a nearshore seabird.

PubMed

Lamb, Juliet S; O'Reilly, Kathleen M; Jodice, Patrick G R

2016-01-01

The effects of acute environmental stressors on reproduction in wildlife are often difficult to measure because of the labour and disturbance involved in collecting accurate reproductive data. Stress hormones represent a promising option for assessing the effects of environmental perturbations on altricial young; however, it is necessary first to establish how stress levels are affected by environmental conditions during development and whether elevated stress results in reduced survival and recruitment rates. In birds, the stress hormone corticosterone is deposited in feathers during the entire period of feather growth, making it an integrated measure of background stress levels during development. We tested the utility of feather corticosterone levels in 3- to 4-week-old nestling brown pelicans ( Pelecanus occidentalis ) for predicting survival rates at both the individual and colony levels. We also assessed the relationship of feather corticosterone to nestling body condition and rates of energy delivery to nestlings. Chicks with higher body condition and lower corticosterone levels were more likely to fledge and to be resighted after fledging, whereas those with lower body condition and higher corticosterone levels were less likely to fledge or be resighted after fledging. Feather corticosterone was also associated with intracolony differences in survival between ground and elevated nest sites. Colony-wide, mean feather corticosterone predicted nest productivity, chick survival and post-fledging dispersal more effectively than did body condition, although these relationships were strongest before fledglings dispersed away from the colony. Both reproductive success and nestling corticosterone were strongly related to nutritional conditions, particularly meal delivery rates. We conclude that feather corticosterone is a powerful predictor of reproductive success and could provide a useful metric for rapidly assessing the effects of changes in environmental conditions, provided pre-existing baseline variation is monitored and understood.

A Drought Cyberinfrastructure System for Improving Water Resource Management and Policy Making

NASA Astrophysics Data System (ADS)

AghaKouchak, Amir

2015-04-01

Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness, management, and response decision making. This presentation provides an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using both remote sensing observations and model simulations. Designed as a cyberinfrastructure system, GIDMaPS provides drought information based on a wide range of model simulations and satellite observations from different space agencies. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts, and better management and distribution of water resources among and across different users. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is designed to provide drought information for water resource management, and short-term decision making. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The presentation will highlight how this drought cyberinfrastructure system can be used to improve water resource management in California. Furthermore, the presentation provides an overview of the information farmers need for better decision making and how GIDMaPS can be used to improve decision making and reducing drought impacts. Further Reading Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1:140001, 1-10, doi: 10.1038/sdata.2014.1. Momtaz F., Nakhjiri N., AghaKouchak A., 2014, Toward a Drought Cyberinfrastructure System, Eos, Transactions American Geophysical Union, 95(22), 182-183, doi:10.1002/2014EO220002. AghaKouchak A., 2014, A Baseline Probabilistic Drought Forecasting Framework Using Standardized Soil Moisture Index: Application to the 2012 United States Drought, Hydrology and Earth System Sciences, 18, 2485-2492, doi: 10.5194/hess-18-2485-2014.

Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

PubMed

Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

2012-03-01

Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Olatinwo, Rabiu O.; Prabha, Thara V.; Paz, Joel O.; Hoogenboom, Gerrit

2012-03-01

Early leaf spot of peanut ( Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

The Utility of the Real-Time NASA Land Information System Data for Drought Monitoring Applications

NASA Technical Reports Server (NTRS)

White, Kristopher D.; Case, Jonathan L.

2013-01-01

Measurements of soil moisture are a crucial component for the proper monitoring of drought conditions. The large spatial variability of soil moisture complicates the problem. Unfortunately, in situ soil moisture observing networks typically consist of sparse point observations, and conventional numerical model analyses of soil moisture used to diagnose drought are of coarse spatial resolution. Decision support systems such as the U.S. Drought Monitor contain drought impact resolution on sub-county scales, which may not be supported by the existing soil moisture networks or analyses. The NASA Land Information System, which is run with 3 km grid spacing over the eastern United States, has demonstrated utility for monitoring soil moisture. Some of the more useful output fields from the Land Information System are volumetric soil moisture in the 0-10 cm and 40-100 cm layers, column-integrated relative soil moisture, and the real-time green vegetation fraction derived from MODIS (Moderate Resolution Imaging Spectroradiometer) swath data that are run within the Land Information System in place of the monthly climatological vegetation fraction. While these and other variables have primarily been used in local weather models and other operational forecasting applications at National Weather Service offices, the use of the Land Information System for drought monitoring has demonstrated utility for feedback to the Drought Monitor. Output from the Land Information System is currently being used at NWS Huntsville to assess soil moisture, and to provide input to the Drought Monitor. Since feedback to the Drought Monitor takes place on a weekly basis, weekly difference plots of column-integrated relative soil moisture are being produced by the NASA Short-term Prediction Research and Transition Center and analyzed to facilitate the process. In addition to the Drought Monitor, these data are used to assess drought conditions for monthly feedback to the Alabama Drought Monitoring and Impact Group and the Tennessee Drought Task Force, which are comprised of federal, state, and local agencies and other water resources professionals.

Concept for Determining the Life of Ceramic Matrix Composites Using Nondestructive Characterization Techniques

NASA Technical Reports Server (NTRS)

Effinger, Michael; Ellingson, Bill; Spohnholtz, Todd; Koenig, John

2000-01-01

Damping measurements have been taken on ceramic matrix composite (CMC) turbopump blisks in the as fabricated, post proof testing, and post turbopump testing conditions. These results indicate that damping is able to quantify fatigue of the CMC blisk. This gives hope for the potential of determining the actual and residual life of CMC materials using a combination of nondestructive techniques. If successful, then this new paradigm for life prediction of CMCs could revolutionize the approach for designing and servicing CMC components, thereby significantly reducing costs for design, development, health monitoring, and maintenance of CMC components and systems. The Nondestructive Characterization (NDC) life prediction approach would complement life prediction using micromechanics and continuum finite element models. This paper reports on the initial concept of NDC life prediction and how changes in damping and ultrasonic elastic modulus data have established the concept as a possibility.

Test-bed for the remote health monitoring system for bridge structures using FBG sensors

NASA Astrophysics Data System (ADS)

Lee, Chin-Hyung; Park, Ki-Tae; Joo, Bong-Chul; Hwang, Yoon-Koog

2009-05-01

This paper reports on test-bed for the long-term health monitoring system for bridge structures employing fiber Bragg grating (FBG) sensors, which is remotely accessible via the web, to provide real-time quantitative information on a bridge's response to live loading and environmental changes, and fast prediction of the structure's integrity. The sensors are attached on several locations of the structure and connected to a data acquisition system permanently installed onsite. The system can be accessed through remote communication using an optical cable network, through which the evaluation of the bridge behavior under live loading can be allowed at place far away from the field. Live structural data are transmitted continuously to the server computer at the central office. The server computer is connected securely to the internet, where data can be retrieved, processed and stored for the remote web-based health monitoring. Test-bed revealed that the remote health monitoring technology will enable practical, cost-effective, and reliable condition assessment and maintenance of bridge structures.

What must be the accuracy and target of optical sensor systems for patient monitoring?

NASA Astrophysics Data System (ADS)

Frank, Klaus H.; Kessler, Manfred D.

2002-06-01

Although the treatment in the intensive care unit has improved in recent years enabling greater surgical engagements and improving patients survival rate, no adequate monitoring is available in imminent severe pathological cases. Otherwise such kind of monitoring is necessary for early or prophylactic treatment in order to avoid or reduce the severity of the disease and protect the patient from sepsis or multiple organ failure. In these cases the common monitoring is limited, because clinical physiological and laboratory parameters indicate either the situation of macro-circulation or late disturbances of microcirculation, which arise previously on sub-cellular level. Optical sensor systems enable to reveal early variations in local capillary flow. The correlation between clinical parameters and changes in condition of oxygenation as a function of capillary flow disturbances is meaningful for the further treatment. The target should be to develop a predictive parameter, which is useful for detection and follow-up of changes in circulation.

Analysis of hyperspectral field radiometric data for monitoring nitrogen concentration in rice crops

NASA Astrophysics Data System (ADS)

Stroppiana, D.; Boschetti, M.; Confalonieri, R.; Bocchi, S.; Brivio, P. A.

2005-10-01

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R2=0.55 [p<0.01] RRMSE=29.4; modelling efficiency close to the optimum value).

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

NASA Astrophysics Data System (ADS)

Abhilash, S.; Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; De, S.; Goswami, B. N.; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Design and implementation of PAVEMON: A GIS web-based pavement monitoring system based on large amounts of heterogeneous sensors data

NASA Astrophysics Data System (ADS)

Shahini Shamsabadi, Salar

A web-based PAVEment MONitoring system, PAVEMON, is a GIS oriented platform for accommodating, representing, and leveraging data from a multi-modal mobile sensor system. Stated sensor system consists of acoustic, optical, electromagnetic, and GPS sensors and is capable of producing as much as 1 Terabyte of data per day. Multi-channel raw sensor data (microphone, accelerometer, tire pressure sensor, video) and processed results (road profile, crack density, international roughness index, micro texture depth, etc.) are outputs of this sensor system. By correlating the sensor measurements and positioning data collected in tight time synchronization, PAVEMON attaches a spatial component to all the datasets. These spatially indexed outputs are placed into an Oracle database which integrates seamlessly with PAVEMON's web-based system. The web-based system of PAVEMON consists of two major modules: 1) a GIS module for visualizing and spatial analysis of pavement condition information layers, and 2) a decision-support module for managing maintenance and repair (Mℝ) activities and predicting future budget needs. PAVEMON weaves together sensor data with third-party climate and traffic information from the National Oceanic and Atmospheric Administration (NOAA) and Long Term Pavement Performance (LTPP) databases for an organized data driven approach to conduct pavement management activities. PAVEMON deals with heterogeneous and redundant observations by fusing them for jointly-derived higher-confidence results. A prominent example of the fusion algorithms developed within PAVEMON is a data fusion algorithm used for estimating the overall pavement conditions in terms of ASTM's Pavement Condition Index (PCI). PAVEMON predicts PCI by undertaking a statistical fusion approach and selecting a subset of all the sensor measurements. Other fusion algorithms include noise-removal algorithms to remove false negatives in the sensor data in addition to fusion algorithms developed for identifying features on the road. PAVEMON offers an ideal research and monitoring platform for rapid, intelligent and comprehensive evaluation of tomorrow's transportation infrastructure based on up-to-date data from heterogeneous sensor systems.

[Demonstrating that monitoring and punishing increase non-cooperative behavior in a social dilemma game].

PubMed

Kitakaji, Yoko; Ohnuma, Susumu

2014-04-01

This research demonstrated the negative influence of monitoring and punishing during a social dilemma game, taking the illegal dumping of industrial waste as an example. The first study manipulated three conditions: a producing-industries monitoring condition (PIM), an administrative monitoring condition (ADM), and a control condition (no monitoring). The results showed that non-cooperative behavior was more frequent in the PIM condition than in the control condition. The second study had three conditions: a punishing condition (PC), a monitoring condition (MC), and a control condition (no monitoring, no punishing). The results indicated that non-cooperative behavior was observed the most in the PC, and the least in the control condition. Furthermore, information regarding other players' costs and benefits was shared the most in the control conditions in both studies. The results suggest that sanctions prevent people from sharing information, which decreases expectations of mutual cooperation.

Monitoring protocols: Options, approaches, implementation, benefits

USGS Publications Warehouse

Karl, Jason W.; Herrick, Jeffrey E.; Pyke, David A.

2017-01-01

Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses—typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today’s monitoring needs are more complex (or multidimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, conceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management’s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study

PubMed Central

Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data. PMID:28255331

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study.

PubMed

Proescholdt, Martin A; Faltermeier, Rupert; Bele, Sylvia; Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

Offshore wind turbine foundation monitoring, extrapolating fatigue measurements from fleet leaders to the entire wind farm

NASA Astrophysics Data System (ADS)

Weijtens, Wout; Noppe, Nymfa; Verbelen, Tim; Iliopoulos, Alexandros; Devriendt, Christof

2016-09-01

The present contribution is part of the ongoing development of a fatigue assessment strategy driven purely on in-situ measurements on operational wind turbines. The primary objective is to estimate the remaining life time of existing wind farms and individual turbines by instrumenting part of the farm with a load monitoring setup. This load monitoring setup allows to measure interface loads and local stress histories. This contribution will briefly discuss how these load measurements can be translated into fatigue assessment of the instrumented turbine. However, due to different conditions at the wind farm, such as turbulence, differences in water depth and foundation design this turbine will not be fully representable for all turbines in the farm. In this paper we will use the load measurements on two offshore wind turbines in the Northwind offshore wind farm to discuss fatigue progression in an operational wind farm. By calculating the damage equivalent loads on the two turbines the fatigue progression is quantified for every 10 minute interval and can be analyzed against turbulence and site conditions. In future work these results will be used to predict the fatigue life progression in the entire farm.

Advanced Signal Processing for High Temperatures Health Monitoring of Condensed Water Height in Steam Pipes

NASA Technical Reports Server (NTRS)

Lih, Shyh-Shiuh; Bar-Cohen, Yoseph; Lee, Hyeong Jae; Takano, Nobuyuki; Bao, Xiaoqi

2013-01-01

An advanced signal processing methodology is being developed to monitor the height of condensed water thru the wall of a steel pipe while operating at temperatures as high as 250deg. Using existing techniques, previous study indicated that, when the water height is low or there is disturbance in the environment, the predicted water height may not be accurate. In recent years, the use of the autocorrelation and envelope techniques in the signal processing has been demonstrated to be a very useful tool for practical applications. In this paper, various signal processing techniques including the auto correlation, Hilbert transform, and the Shannon Energy Envelope methods were studied and implemented to determine the water height in the steam pipe. The results have shown that the developed method provides a good capability for monitoring the height in the regular conditions. An alternative solution for shallow water or no water conditions based on a developed hybrid method based on Hilbert transform (HT) with a high pass filter and using the optimized windowing technique is suggested. Further development of the reported methods would provide a powerful tool for the identification of the disturbances of water height inside the pipe.

Hydrologic response across a snow persistence gradient on the west and east slopes of the Rocky Mountains in Colorado

NASA Astrophysics Data System (ADS)

Richard, G. A.; Hammond, J. C.; Kampf, S. K.; Moore, C. D.; Eurich, A.

2017-12-01

Snowpack trend analyses and modeling studies suggest that lower elevation snowpacks in mountain regions are most sensitive to drought and warming temperatures, however, in Colorado, most snow monitoring occurs in the high elevations where snow lasts throughout the winter and most streamflow monitoring occurs at lower elevations. The lack of combined snow and streamflow monitoring in watersheds along the transition from intermittent to persistent snow creates a gap in our understanding of snowmelt and runoff within the intermittent-persistent snow transition. Expanded hydrologic monitoring that spans the gradient of snow conditions in Colorado can help improve streamflow prediction and inform land and water managers. This study established hydrologic monitoring watersheds in intermittent, transitional, and persistent snow zones on the east slope and west slope of the Rocky Mountains in Colorado, and uses this monitoring network to improve understanding of how snow accumulation and melt affect soil moisture and streamflow generation under different snow conditions. We monitored six small watersheds (three west slope, three east slope) (0.8 to 3.9 km2) that drain intermittent, transitional, and persistent snow zones. At each site, we measured: streamflow, snow depth, soil moisture, precipitation, air temperature, and snow water equivalent (SWE). In our first season of monitoring, the west slope persistent and transitional sites had more mid-winter melt and infiltration, shorter snowpack duration, and lower peak SWE than the east slope sites. Snow cover remained at the east slope persistent site into June, whereas much of the snow at the persistent site on the west slope had already melted by early June. The difference in soil water input likely has consequences for streamflow response that we will continue to examine in future years. At the west slope intermittent site, the stream did not flow during the entire first year of monitoring, while at the east slope intermittent site, the streams flowed intermittently during winter and spring, likely a result of different subsurface geology. With our ongoing watershed monitoring across a broad range of snow conditions in Colorado, we continue to learn about the factors that increase or decrease streamflow in the headwater streams that supply the state's major rivers.

Monitoring and regulation of learning in medical education: the need for predictive cues.

PubMed

de Bruin, Anique B H; Dunlosky, John; Cavalcanti, Rodrigo B

2017-06-01

Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is applicable to learning in several areas, including the development of clinical reasoning. © 2017 The Authors Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

2014-05-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

NASA Astrophysics Data System (ADS)

Borah, N.; Abhilash, S.; Sahai, A. K.; Chattopadhyay, R.; Joseph, S.; Sharmila, S.; de, S.; Goswami, B.; Kumar, A.

2013-12-01

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISOs) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Locust displacing winds in eastern Australia reassessed with observations from an insect monitoring radar

NASA Astrophysics Data System (ADS)

Hao, Zhenhua; Drake, V. Alistair; Sidhu, Leesa; Taylor, John R.

2017-12-01

Based on previous investigations, adult Australian plague locusts are believed to migrate on warm nights (with evening temperatures >25 °C), provided daytime flight is suppressed by surface winds greater than the locusts' flight speed, which has been shown to be 3.1 m s-1. Moreover, adult locusts are believed to undertake briefer `dispersal' flights on nights with evening temperature >20 °C. To reassess the utility of these conditions for forecasting locust flight, contingency tests were conducted comparing the nights selected on these bases (predicted nights) for the months of November, January, and March and the nights when locust migration were detected with an insect monitoring radar (actual nights) over a 7-year period. In addition, the wind direction distributions and mean wind directions on all predicted nights and actual nights were compared. Observations at around 395 m above ground level (AGL), the height at which radar observations have shown that the greatest number of locusts fly, were used to determine the actual nights. Tests and comparisons were also made for a second height, 990 m AGL, as this was used in the previous investigation. Our analysis shows that the proposed criteria are successful from predicting migratory flight only in March, when the surface temperature is effective as a predicting factor. Surface wind speed has no predicting power. It is suggested that a strong daytime surface wind speed requirement should not be considered and other meteorological variables need to be added to the requirement of a warm surface temperature around dusk for the predictions to have much utility.

Development of the monitoring system to detect the piping thickness reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, N. Y.; Ryu, K. H.; Oh, Y. J.

2006-07-01

As nuclear piping becomes aging, secondary piping which was considered safe, undergo thickness reduction problem these days. After some accidents caused by Flow Accelerated Corrosion (FAC), guidelines and recommendations for the thinned pipe management were issued, and thus need for monitoring increases. Through thinned pipe management program, monitoring activities based on the various analyses and the case study of other plants also increases. As the monitoring points increase, time needs to cover the recommended inspection area becomes increasing, while the time given to inspect the piping during overhaul becomes shortened. Existing Ultrasonic Technique (UT) can cover small area in amore » given time. Moreover, it cannot be applied to a complex geometry piping or a certain location like welded part. In this paper, we suggested Switching Direct Current Potential Drop (S-DCPD) method by which we can narrow down the FAC-susceptible area. To apply DCPD, we developed both resistance model and Finite Element Method (FEM) model to predict the DCPD feasibility. We tested elbow specimen to compare DCPD monitoring results with UT results to identify consistency. For the validation test, we designed simulation loop. To determine the text condition, we analyzed environmental parameters and introduced applicable wearing rate model. To obtain the model parameters, we developed electrodes and analyzed velocity profile in the test loop using CFX code. Based on the prediction model and prototype testing results, we are planning to perform validation test to identify applicability of S-DCPD in the NPP environment. Validation text plan will be described as a future work. (authors)« less

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Circulating microRNAs in breast cancer: novel diagnostic and prognostic biomarkers

PubMed Central

Hamam, Rimi; Hamam, Dana; Alsaleh, Khalid A; Kassem, Moustapha; Zaher, Waleed; Alfayez, Musaad; Aldahmash, Abdullah; Alajez, Nehad M

2017-01-01

Effective management of breast cancer depends on early diagnosis and proper monitoring of patients’ response to therapy. However, these goals are difficult to achieve because of the lack of sensitive and specific biomarkers for early detection and for disease monitoring. Accumulating evidence in the past several years has highlighted the potential use of peripheral blood circulating nucleic acids such as DNA, mRNA and micro (mi)RNA in breast cancer diagnosis, prognosis and for monitoring response to anticancer therapy. Among these, circulating miRNA is increasingly recognized as a promising biomarker, given the ease with which miRNAs can be isolated and their structural stability under different conditions of sample processing and isolation. In this review, we provide current state-of-the-art of miRNA biogenesis, function and discuss the advantages, limitations, as well as pitfalls of using circulating miRNAs as diagnostic, prognostic or predictive biomarkers in breast cancer management. PMID:28880270

Circulating microRNAs in breast cancer: novel diagnostic and prognostic biomarkers.

PubMed

Hamam, Rimi; Hamam, Dana; Alsaleh, Khalid A; Kassem, Moustapha; Zaher, Waleed; Alfayez, Musaad; Aldahmash, Abdullah; Alajez, Nehad M

2017-09-07

Effective management of breast cancer depends on early diagnosis and proper monitoring of patients' response to therapy. However, these goals are difficult to achieve because of the lack of sensitive and specific biomarkers for early detection and for disease monitoring. Accumulating evidence in the past several years has highlighted the potential use of peripheral blood circulating nucleic acids such as DNA, mRNA and micro (mi)RNA in breast cancer diagnosis, prognosis and for monitoring response to anticancer therapy. Among these, circulating miRNA is increasingly recognized as a promising biomarker, given the ease with which miRNAs can be isolated and their structural stability under different conditions of sample processing and isolation. In this review, we provide current state-of-the-art of miRNA biogenesis, function and discuss the advantages, limitations, as well as pitfalls of using circulating miRNAs as diagnostic, prognostic or predictive biomarkers in breast cancer management.

Correlation between average frequency and RA value (rise time/amplitude) for crack classification of reinforced concrete beam using acoustic emission technique

NASA Astrophysics Data System (ADS)

Noorsuhada, M. N.; Abdul Hakeem, Z.; Soffian Noor, M. S.; Noor Syafeekha, M. S.; Azmi, I.

2017-12-01

Health monitoring of structures during their service life become a vital thing as it provides crucial information regarding the performance and condition of the structures. Acoustic emission (AE) is one of the non-destructive techniques (NDTs) that could be used to monitor the performance of the structures. Reinforced concrete (RC) beam associated with AE monitoring was monotonically loaded to failure under three-point loading. Correlation between average frequency and RA value (rise time / amplitude) was computed. The relationship was established to classify the crack types that propagated in the RC beam. The crack was classified as tensile crack and shear crack. It was found that the relationship is well matched with the actual crack pattern that appeared on the beam surface. Hence, this relationship is useful for prediction of the crack occurrence in the beam and its performance can be determined.

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

NASA Astrophysics Data System (ADS)

Sévellec, Florian; Dijkstra, Henk A.; Drijfhout, Sybren S.; Germe, Agathe

2017-11-01

In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, ˜ 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1-5 years lead times.

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

An Investigation of Soft Proof to Print Agreement under Bright Surround

NASA Astrophysics Data System (ADS)

Zunjarrao, Vickrant J.

Color quality is a vital concern in the printing industry. The ability of an LCD monitor to accurately and consistently predict the color of a printed work is often in doubt. According to Chung (2005), color reproduction technology is different for soft proofing and hard proofing which could lead a layman to believe that the two technologies may not produce the same result. Nevertheless, it is still possible for both reproduction technologies to achieve a metameric match which gives the same perceived color sensation between display and print. ISO/CD 14681 provides guidelines for creating the conditions required to perform soft proofing. This standard builds on ISO 12646 requirements for monitors and introduces a new softproofing environment (lightbooth with integrated monitor) to better meet the needs of industrial users. The ISO 14681 integrated viewing environment removes one important obstacle to achieving print to softproof match, i.e., the problem of simultaneous color contrast inherent in using a dim monitor surround with a bright paper viewing condition for soft proofing. Thus, the first objective of this research was to assess print to softproof visual match in the ISO 14681 integrated viewing environment. Nevertheless, even in this environment, inconsistency between paper white and monitor white remains as the next major obstacle to achieving consistent print to softproof match. Thus, a second objective of this research is to develop a methodology for matching the monitor's white point to the white point of the paper viewed in an ISO 14681 integrated viewing environment. The methodology for fulfilling these objectives began with the creation of the hardware/software environment required to support experimentation. This environment consisted of a 24-inch EIZO CG242W display conforming to ISO 12646 and an integrated viewing environment conforming to the P2 specification in ISO 3664:2009. Two ISO 12647-2 conformed press sheets were prepared and became the reference for the experiment. The researcher next developed a methodology for matching the monitor white point to the white point of the paper under the P2 viewing condition. Finally, a panel of observers was used to compare print to softproof match for four display conditions in a paired comparison experiment. The results of the experiment were highly encouraging. The mismatch between monitor and paper white points, as measured by the sum of the differences in R, G, and B counts between the monitor and the paper, was reduced by nearly 90%. In addition, the paired comparison experiment demonstrated that the use of a custom monitor white point and optimized monitor gamma outperformed the use of standard D65 and D50 white points with the same optimized gamma at a .05 level of significance.

Innovative Strategy For Long Term Monitoring Of Metal And Radionuclide Plumes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eddy-Dilek, Carol; Millings, Margaret R.; Looney, Brian B.

2014-01-08

Many government and private industry sites that were once contaminated with radioactive and chemical wastes cannot be cleaned up enough to permit unrestricted human access. The sites will require long term management, in some cases indefinitely, leaving site owners with the challenge of protecting human health and environmental quality at these "legacy" sites. Long-term monitoring of groundwater contamination is one of the largest projected costs in the life cycle of environmental management at the Savannah River Site, the larger DOE complex, and many large federal and private sites. There is a need to optimize the performance and manage the costmore » of long term surveillance and monitoring at their sites. Currently, SRNL is initiating a pilot field test using alternative protocols for long term monitoring of metals and radionuclides. A key component of the approach is that monitoring efforts are focused on measurement of low cost metrics related to hydrologic and chemical conditions that control contaminant migration. The strategy combines careful monitoring of hydrologic boundary conditions with measurement of master variables such as chemical surrogates along with a smaller number of standard well analyses. In plumes contaminated with metals, master variables control the chemistry of the groundwater system, and include redox variables (ORP, DO, chemicals), pH, specific conductivity, biological community (breakdown/decay products), and temperature. Significant changes in these variables will result in conditions whereby the plume may not be stable and therefore can be used to predict possible plume migration. Conversely, concentration measurements for all types of contaminants in groundwater are a lagging indicator plume movement - major changes contaminant concentrations indicate that contamination has migrated. An approach based on measurement of master variables and explicit monitoring of hydrologic boundary conditions combined with traditional metrics should lead to improved monitoring while simultaneously reducing costs. This paradigm is being tested at the SRS F-Area where an innovative passive remedial system is being monitored and evaluated over the long term prior to traditional regulatory closure. Contaminants being addressed at this site are uranium, strontium-90, iodine-129, and tritium. We believe that the proposed strategies will be more effective in early identification of potential risks; these strategies will also be cost effective because controlling variables are relatively simple to measure. These variables also directly reflect the evolution of the plume through time, so that the monitoring strategy can be modified as the plume 'ages'. This transformational long-term monitoring paradigm will generate large cost savings to DOE, other federal agencies and industry and will provide improved performance and leading indicators of environmental management performance.« less

Infrared thermography applied to the study of heated and solar pavement: from numerical modeling to small scale laboratory experiments

NASA Astrophysics Data System (ADS)

Le Touz, N.; Toullier, T.; Dumoulin, J.

2017-05-01

The present study addresses the thermal behaviour of a modified pavement structure to prevent icing at its surface in adverse winter time conditions or overheating in hot summer conditions. First a multi-physic model based on infinite elements method was built to predict the evolution of the surface temperature. In a second time, laboratory experiments on small specimen were carried out and the surface temperature was monitored by infrared thermography. Results obtained are analyzed and performances of the numerical model for real scale outdoor application are discussed. Finally conclusion and perspectives are proposed.

Spatial and temporal analyses for multiscale monitoring of landslides: Examples from Northern Ireland

NASA Astrophysics Data System (ADS)

Bell, Andrew; McKinley, Jennifer; Hughes, David

2013-04-01

Landslides in the form of debris flows, large scale rotational features and composite mudflows impact transport corridors cutting off local communities and in some instances result in loss of life. This study presents landslide monitoring methods used for predicting and characterising landslide activity along transport corridors. A variety of approaches are discussed: desk based risk assessment of slopes using Geographical Information Systems (GIS); Aerial LiDAR surveys and Terrestrial LiDAR monitoring and field instrumentation of selected sites. A GIS based case study is discussed which provides risk assessment for the potential of slope stability issues. Layers incorporated within the system include Digital Elevation Model (DEM), slope, aspect, solid and drift geology and groundwater conditions. Additional datasets include consequence of failure. These are combined within a risk model, presented as likelihoods of failure. This integrated spatial approach for slope risk assessment provides the user with a preliminary risk assessment of sites. An innovative "Flexviewer" web-based server interface allows users to view data without needing advanced GIS techniques to gather information about selected areas. On a macro landscape scale, Aerial LiDAR (ALS) surveys are used for the characterisation of landslides from the surrounding terrain. DEMs are generated along with terrain derivatives: slope, curvature and various measures of terrain roughness. Spatial analysis of terrain morphological parameters allow characterisation of slope stability issues and are used to predict areas of potential failure or recently failure terrain. On a local scale ground monitoring approaches are employed for the monitoring of changes in selected slopes using ALS and risk assessment approaches. Results are shown from on-going bimonthly Terrestrial LiDAR (TLS) monitoring of the slope within a site specific geodectically referenced network. This has allowed a classification of changes in the slopes with DEMs of difference showing areas of recent movement, erosion and deposition. In addition, changes in the structure of the slope characterised by DEM of difference and morphological parameters in the form of roughness, slope and curvature measures are progressively linked to failures indicated from temporal DEM monitoring. Preliminary results are presented for a case site at Straidkilly Point, Glenarm, Co. Antrim, Northern Ireland, illustrating multiple approaches to the spatial and temporal monitoring of landslides. These indicate how spatial morphological approaches and risk assessment frameworks coupled with TLS monitoring and field instrumentation enable characterisation and prediction of potential areas of slope stability issues. On site weather instrumentation and piezometers document changes in pore water pressures resulting in site-specific information with geotechnical observations parameterised within the temporal LiDAR monitoring. This provides a multifaceted approach to the characterisation and analysis of slope stability issues. The presented methodology of multiscale datasets and surveying approaches utilising spatial parameters and risk index mapping enables a more comprehensive and effective prediction of landslides resulting in effective characterisation and remediation strategies.

Prediction of meningococcal meningitis epidemics in western Africa by using climate information

NASA Astrophysics Data System (ADS)

YAKA, D. P.; Sultan, B.; Tarbangdo, F.; Thiaw, W. M.

2013-12-01

The variations of certain climatic parameters and the degradation of ecosystems, can affect human's health by influencing the transmission, the spatiotemporal repartition and the intensity of infectious diseases. It is mainly the case of meningococcal meningitis (MCM) whose epidemics occur particularly in Sahelo-Soudanian climatic area of Western Africa under quite particular climatic conditions. Meningococcal Meningitis (MCM) is a contagious infection disease due to the bacteria Neisseria meningitis. MCM epidemics occur worldwide but the highest incidence is observed in the "meningitis belt" of sub-Saharan Africa, stretching from Senegal to Ethiopia. In spite of standards, strategies of prevention and control of MCS epidemic from World Health Organization (WHO) and States, African Sahelo-Soudanian countries remain frequently afflicted by disastrous epidemics. In fact, each year, during the dry season (February-April), 25 to 250 thousands of cases are observed. Children under 15 are particularly affected. Among favourable conditions for the resurgence and dispersion of the disease, climatic conditions may be important inducing seasonal fluctuations in disease incidence and contributing to explain the spatial pattern of the disease roughly circumscribed to the ecological Sahelo-Sudanian band. In this study, we tried to analyse the relationships between climatic factors, ecosystems degradation and MCM for a better understanding of MCM epidemic dynamic and their prediction. We have shown that MCM epidemics, whether at the regional, national or local level, occur in a specific period of the year, mainly from January to May characterised by a dry, hot and sandy weather. We have identified both in situ (meteorological synoptic stations) and satellitales climatic variables (NCEP reanalysis dataset) whose seasonal variability is dominating in MCM seasonal transmission. Statistical analysis have measured the links between seasonal variation of certain climatic parameters (particularly the meridional wind components) and seasonal recrudescence of MCM cases in order to elaborate seasonal occurrence of MCM epidemics prediction models on different spatiotemporal scales. These predictions have been experienced and evaluated by Burkina Meteorological Authority and Health Protection General Direction since 2009. The encouraging results from the monitoring and evaluation of these predictions given by such simple models enable the development of a monitoring and an early warning integrated system of MCM epidemics in Burkina Faso. This experience could be implemented in others African Sahelo-Soudanian countries.

Monitoring Damage Accumulation in Ceramic Matrix Composites Using Electrical Resistivity

NASA Technical Reports Server (NTRS)

Smith, Craig E.; Morscher, Gregory N.; Xia, Zhenhai H.

2008-01-01

The electric resistance of woven SiC fiber reinforced SiC matrix composites were measured under tensile loading conditions. The results show that the electrical resistance is closely related to damage and that real-time information about the damage state can be obtained through monitoring of the resistance. Such self-sensing capability provides the possibility of on-board/in-situ damage detection and accurate life prediction for high-temperature ceramic matrix composites. Woven silicon carbide fiber-reinforced silicon carbide (SiC/SiC) ceramic matrix composites (CMC) possess unique properties such as high thermal conductivity, excellent creep resistance, improved toughness, and good environmental stability (oxidation resistance), making them particularly suitable for hot structure applications. In specific, CMCs could be applied to hot section components of gas turbines [1], aerojet engines [2], thermal protection systems [3], and hot control surfaces [4]. The benefits of implementing these materials include reduced cooling air requirements, lower weight, simpler component design, longer service life, and higher thrust [5]. It has been identified in NASA High Speed Research (HSR) program that the SiC/SiC CMC has the most promise for high temperature, high oxidation applications [6]. One of the critical issues in the successful application of CMCs is on-board or insitu assessment of the damage state and an accurate prediction of the remaining service life of a particular component. This is of great concern, since most CMC components envisioned for aerospace applications will be exposed to harsh environments and play a key role in the vehicle s safety. On-line health monitoring can enable prediction of remaining life; thus resulting in improved safety and reliability of structural components. Monitoring can also allow for appropriate corrections to be made in real time, therefore leading to the prevention of catastrophic failures. Most conventional nondestructive evaluation (NDE) techniques such as ultrasonic C-scan, x-ray, thermography, and eddy current are limited since they require structural components of complex geometry to be taken out of service for a substantial length of time for post-damage inspection and assessment. Furthermore, the typical NDE techniques are useful for identifying large interlaminar flaws, but insensitive to CMC materials flaws developed perpendicular to the surface under tensile creep conditions. There are techniques such as piezoelectric sensor [7,8], and optical fiber [9,10] that could be used for on-line health monitoring of CMC structures. However, these systems involve attaching an external sensor or putting special fibers in CMC composites, which would be problematic at high temperature applications.

Shelf Life of Food Products: From Open Labeling to Real-Time Measurements.

PubMed

Corradini, Maria G

2018-03-25

The labels currently used on food and beverage products only provide consumers with a rough guide to their expected shelf lives because they assume that a product only experiences a limited range of predefined handling and storage conditions. These static labels do not take into consideration conditions that might shorten a product's shelf life (such as temperature abuse), which can lead to problems associated with food safety and waste. Advances in shelf-life estimation have the potential to improve the safety, reliability, and sustainability of the food supply. Selection of appropriate kinetic models and data-analysis techniques is essential to predict shelf life, to account for variability in environmental conditions, and to allow real-time monitoring. Novel analytical tools to determine safety and quality attributes in situ coupled with modern tracking technologies and appropriate predictive tools have the potential to provide accurate estimations of the remaining shelf life of a food product in real time. This review summarizes the necessary steps to attain a transition from open labeling to real-time shelf-life measurements.

Monitoring Streambed Scour/Deposition Under Nonideal Temperature Signal and Flood Conditions

NASA Astrophysics Data System (ADS)

DeWeese, Timothy; Tonina, Daniele; Luce, Charles

2017-12-01

Streambed erosion and deposition are fundamental geomorphic processes in riverbeds, and monitoring their evolution is important for ecological system management and in-stream infrastructure stability. Previous research showed proof of concept that analysis of paired temperature signals of stream and pore waters can simultaneously provide monitoring scour and deposition, stream sediment thermal regime, and seepage velocity information. However, it did not address challenges often associated with natural systems, including nonideal temperature variations (low-amplitude, nonsinusoidal signal, and vertical thermal gradients) and natural flooding conditions on monitoring scour and deposition processes over time. Here we addressed this knowledge gap by testing the proposed thermal scour-deposition chain (TSDC) methodology, with laboratory experiments to test the impact of nonideal temperature signals under a range of seepage velocities and with a field application during a pulse flood. Both analyses showed excellent match between surveyed and temperature-derived bed elevation changes even under very low temperature signal amplitudes (less than 1°C), nonideal signal shape (sawtooth shape), and strong and changing vertical thermal gradients (4°C/m). Root-mean-square errors on predicting the change in streambed elevations were comparable with the median grain size of the streambed sediment. Future research should focus on improved techniques for temperature signal phase and amplitude extractions, as well as TSDC applications over long periods spanning entire hydrographs.

A review on prognostic techniques for non-stationary and non-linear rotating systems

NASA Astrophysics Data System (ADS)

Kan, Man Shan; Tan, Andy C. C.; Mathew, Joseph

2015-10-01

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Condition monitoring of a wind turbine doubly-fed induction generator through current signature analysis

NASA Astrophysics Data System (ADS)

Artigao, Estefania; Honrubia-Escribano, Andres; Gomez-Lazaro, Emilio

2017-11-01

Operation and maintenance (O&M) of wind turbines is recently becoming the spotlight in the wind energy sector. While wind turbine power capacities continue to increase and new offshore developments are being installed, O&M costs keep raising. With the objective of reducing such costs, the new trends are moving from corrective and preventive maintenance toward predictive actions. In this scenario, condition monitoring (CM) has been identified as the key to achieve this goal. The induction generator of a wind turbine is a major contributor to failure rates and downtime where doubly-fed induction generators (DFIG) are the dominant technology employed in variable speed wind turbines. The current work presents the analysis of an in-service DFIG. A one-year measurement campaign has been used to perform the study. Several signal processing techniques have been applied and the optimal method for CM has been identified. A diagnosis has been reached, the DFIG under study shows potential gearbox damage.

Remote Imaging Applied to Schistosomiasis Control: The Anning River Project

NASA Technical Reports Server (NTRS)

Seto, Edmund Y. W.; Maszle, Don R.; Spear, Robert C.; Gong, Peng

1997-01-01

The use of satellite imaging to remotely detect areas of high risk for transmission of infectious disease is an appealing prospect for large-scale monitoring of these diseases. The detection of large-scale environmental determinants of disease risk, often called landscape epidemiology, has been motivated by several authors (Pavlovsky 1966; Meade et al. 1988). The basic notion is that large-scale factors such as population density, air temperature, hydrological conditions, soil type, and vegetation can determine in a coarse fashion the local conditions contributing to disease vector abundance and human contact with disease agents. These large-scale factors can often be remotely detected by sensors or cameras mounted on satellite or aircraft platforms and can thus be used in a predictive model to mark high risk areas of transmission and to target control or monitoring efforts. A review of satellite technologies for this purpose was recently presented by Washino and Wood (1994) and Hay (1997) and Hay et al. (1997).

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

NASA Astrophysics Data System (ADS)

Zhang, Bin; Deng, Congying; Zhang, Yi

2018-03-01

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

NASA Astrophysics Data System (ADS)

Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

2016-10-01

In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

A Waveform Detector that Targets Template-Decorrelated Signals and Achieves its Predicted Performance: Demonstration with IMS Data

NASA Astrophysics Data System (ADS)

Carmichael, J.

2016-12-01

Waveform correlation detectors used in seismic monitoring scan multichannel data to test two competing hypotheses: that data contain (1) a noisy, amplitude-scaled version of a template waveform, or, (2) only noise. In reality, seismic wavefields include signals triggered by non-target sources (background seismicity) and target signals that are only partially correlated with the waveform template. We reform the waveform correlation detector hypothesis test to accommodate deterministic uncertainty in template/target waveform similarity and thereby derive a new detector from convex set projections (the "cone detector") for use in explosion monitoring. Our analyses give probability density functions that quantify the detectors' degraded performance with decreasing waveform similarity. We then apply our results to three announced North Korean nuclear tests and use International Monitoring System (IMS) arrays to determine the probability that low magnitude, off-site explosions can be reliably detected with a given waveform template. We demonstrate that cone detectors provide (1) an improved predictive capability over correlation detectors to identify such spatially separated explosive sources, (2) competitive detection rates, and (3) reduced false alarms on background seismicity. Figure Caption: Observed and predicted receiver operating characteristic curves for correlation statistic r(x) (left) and cone statistic s(x) (right) versus semi-empirical explosion magnitude. a: Shaded region shows range of ROC curves for r(x) that give the predicted detection performance in noise conditions recorded over 24 hrs on 8 October 2006. Superimposed stair plot shows the empirical detection performance (recorded detections/total events) averaged over 24 hr of data. Error bars indicate the demeaned range in observed detection probability over the day; means are removed to avoid risk of misinterpreting range to indicate probabilities can exceed one. b: Shaded region shows range of ROC curves for s(x) that give the predicted detection performance for the cone detector. Superimposed stair plot show observed detection performance averaged over 24 hr of data analogous to that shown in a.

Past, Present, and Future: A Science Program for the Arctic Ocean Linking Ancient and Contemporary Observations of Change Through Modeling

NASA Astrophysics Data System (ADS)

Coakley, Bernard; Edmonds, Henrietta N.; Frey, Karen; Gascard, Jean-Claude; Grebmeier, Jacqueline M.; Kassens, Heidemarie; Thiede, Jörn; Wegner, Carolyn

2007-07-01

A follow-up to the 2nd International Conference on Arctic Research Planning, 19-21 November 2007, Potsdam, Germany The Arctic Ocean is the missing piece for any global model. Records of processes at both long and short timescales will be necessary to predict the future evolution of the Arctic Ocean through what appears to be a period of rapid climate change. Ocean monitoring is impoverished without the long-timescale records available from paleoceanography and the boundary conditions that can be obtained from marine geology and geophysics. The past and the present are the key to our ability to predict the future.

Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions

PubMed Central

Cai, Xiaonan; Wang, Chen; Chen, Shengdi; Lu, Jian

2016-01-01

Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers’ subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers’ perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors) using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions. PMID:26894434

How many days of accelerometer monitoring predict weekly physical activity behaviour in obese youth?

PubMed

Vanhelst, Jérémy; Fardy, Paul S; Duhamel, Alain; Béghin, Laurent

2014-09-01

The aim of this study was to determine the type and the number of accelerometer monitoring days needed to predict weekly sedentary behaviour and physical activity in obese youth. Fifty-three obese youth wore a triaxial accelerometer for 7 days to measure physical activity in free-living conditions. Analyses of variance for repeated measures, Intraclass coefficient (ICC) and regression linear analyses were used. Obese youth spent significantly less time in physical activity on weekends or free days compared with school days. ICC analyses indicated a minimum of 2 days is needed to estimate physical activity behaviour. ICC were 0·80 between weekly physical activity and weekdays and 0·92 between physical activity and weekend days. The model has to include a weekday and a weekend day. Using any combination of one weekday and one weekend day, the percentage of variance explained is >90%. Results indicate that 2 days of monitoring are needed to estimate the weekly physical activity behaviour in obese youth with an accelerometer. Our results also showed the importance of taking into consideration school day versus free day and weekday versus weekend day in assessing physical activity in obese youth. © 2013 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Flight test of takeoff performance monitoring system

NASA Technical Reports Server (NTRS)

Middleton, David B.; Srivatsan, Raghavachari; Person, Lee H., Jr.

1994-01-01

The Takeoff Performance Monitoring System (TOPMS) is a computer software and hardware graphics system that visually displays current runway position, acceleration performance, engine status, and other situation advisory information to aid pilots in their decision to continue or to abort a takeoff. The system was developed at the Langley Research Center using the fixed-base Transport Systems Research Vehicle (TSRV) simulator. (The TSRV is a highly modified Boeing 737-100 research airplane.) Several versions of the TOPMS displays were evaluated on the TSRV B-737 simulator by more than 40 research, United States Air Force, airline and industry and pilots who rated the system satisfactory and recommended further development and testing. In this study, the TOPMS was flight tested on the TSRV. A total of 55 takeoff and 30 abort situations were investigated at 5 airfields. TOPMS displays were observed on the navigation display screen in the TSRV research flight deck during various nominal and off-nominal situations, including normal takeoffs; reduced-throttle takeoffs; induced-acceleration deficiencies; simulated-engine failures; and several gross-weight, runway-geometry, runway-surface, and ambient conditions. All tests were performed on dry runways. The TOPMS software executed accurately during the flight tests and the displays correctly depicted the various test conditions. Evaluation pilots found the displays easy to monitor and understand. The algorithm provides pretakeoff predictions of the nominal distances that are needed to accelerate the airplane to takeoff speed and to brake it to a stop; these predictions agreed reasonably well with corresponding values measured during several fully executed and aborted takeoffs. The TOPMS is operational and has been retained on the TSRV for general use and demonstration.

IUTAM Symposium on Inelastic Deformation of Composite Materials Held in Troy, New York on 29 May - 1 June 1990

DTIC Science & Technology

1991-01-01

bimodal theory . 1. Introduction Numerous analytical models have been proposed for prediction of the inelastic response of fibrous composites, an...necessity - especially at a higher c1 - to use the local-field theory . The shear creep strain of the composite is slightly larger in the transverse... gauge surface were also monitored. Theoretical Consideration Failure theories for anisotropic materials in plane stress conditions are in general

Meta-analysis of Magnetic Marker Monitoring Data to Characterize the Movement of Single Unit Dosage Forms Though the Gastrointestinal Tract Under Fed and Fasting Conditions.

PubMed

Hénin, Emilie; Bergstrand, Martin; Weitschies, Werner; Karlsson, Mats O

2016-03-01

To develop a model predicting movement of non-disintegrating single unit dosage forms (or "tablet") through the gastrointestinal tract and characterizing the effect of food intake, based on Magnetic Marker Monitoring data, allowing real-time location of a magnetically labeled formulation. Five studies including 30 individuals in 94 occasions under 3 food status were considered. The mean residence time (MRT) of the tablet and the effect of food intake in proximal (PS) and distal stomach (DS), small intestine (SI), ascending (AC), transverse (TC) and descending colon (DC) were estimated using a Markov model for probabilities of movement. Under fasting conditions, tablet MRTs were 9.4 min in PS, 10.4 in DS, 246 in SI, 545 in AC, 135 in TC, and 286 in DC. A meal taken simultaneous to tablet intake prolonged tablet MRT to 99 min in PS and to 232 in DS; probability of gastric emptying increased of 89% each hour from 2.25 h after meal. The effect of a gastroileac reflex, caused by a secondary meal, accelerated the transit from terminal SI to AC. This model-based knowledge can be used as a part of mechanism-based models for drug absorption, applied for bottom-up predictions and/or top-down estimation.

Characterizing fate and transport properties in karst aquifers under different hydrologic conditions

NASA Astrophysics Data System (ADS)

Rodriguez, E.; Padilla, I. Y.

2017-12-01

Karst landscapes contain very productive aquifers. The hydraulic and hydrogeological characteristics of karst aquifers make these systems capable of storing and transporting large amount of water, but also highly vulnerable to contamination. Their extremely heterogeneous nature prevents accurate prediction in contaminant fate and transport. Even more challenging is to understand the impact of hydrologic conditions changes on fate and transport processes. This studies aims at characterizing fate and transport processes in the karst groundwater system of northern Puerto Rico under different hydrologic conditions. The study involves injecting rhodamine and uranine dyes into a sinkhole, and monitoring concentrations at a spring. Results show incomplete recovery of tracers, but breaking curves can be used to estimate advective, dispersive and mass transfer characteristic of the karst system. Preliminary results suggest significant differences in fate and transport characteristics under different hydrologic conditions.

Acceptance lowers stress reactivity: Dismantling mindfulness training in a randomized controlled trial.

PubMed

Lindsay, Emily K; Young, Shinzen; Smyth, Joshua M; Brown, Kirk Warren; Creswell, J David

2018-01-01

Mindfulness interventions, which train practitioners to monitor their present-moment experience with a lens of acceptance, are known to buffer stress reactivity. Little is known about the active mechanisms driving these effects. We theorize that acceptance is a critical emotion regulation mechanism underlying mindfulness stress reduction effects. In this three-arm parallel trial, mindfulness components were dismantled into three structurally equivalent 15-lesson smartphone-based interventions: (1) training in both monitoring and acceptance (Monitor+Accept), (2) training in monitoring only (Monitor Only), or (3) active control training (Coping control). 153 stressed adults (mean age=32years; 67% female; 53% white, 21.5% black, 21.5% Asian, 4% other race) were randomly assigned to complete one of three interventions. After the intervention, cortisol, blood pressure, and subjective stress reactivity were assessed using a modified Trier Social Stress Test. As predicted, Monitor+Accept training reduced cortisol and systolic blood pressure reactivity compared to Monitor Only and control trainings. Participants in all three conditions reported moderate levels of subjective stress. This study provides the first experimental evidence that brief smartphone mindfulness training can impact stress biology, and that acceptance training drives these effects. We discuss implications for basic and applied research in contemplative science, emotion regulation, stress and coping, health, and clinical interventions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

USGS Publications Warehouse

Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

2018-01-01

Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate risks posed by mobile disease hosts. More broadly, we demonstrate how mechanistic movement models can provide predictions of ecological conditions that are consistent with climate change but may be more extreme than has been observed historically.

Development and application of Geobacillus stearothermophilus growth model for predicting spoilage of evaporated milk.

PubMed

Kakagianni, Myrsini; Gougouli, Maria; Koutsoumanis, Konstantinos P

2016-08-01

The presence of Geobacillus stearothermophilus spores in evaporated milk constitutes an important quality problem for the milk industry. This study was undertaken to provide an approach in modelling the effect of temperature on G. stearothermophilus ATCC 7953 growth and in predicting spoilage of evaporated milk. The growth of G. stearothermophilus was monitored in tryptone soy broth at isothermal conditions (35-67 °C). The data derived were used to model the effect of temperature on G. stearothermophilus growth with a cardinal type model. The cardinal values of the model for the maximum specific growth rate were Tmin = 33.76 °C, Tmax = 68.14 °C, Topt = 61.82 °C and μopt = 2.068/h. The growth of G. stearothermophilus was assessed in evaporated milk at Topt in order to adjust the model to milk. The efficiency of the model in predicting G. stearothermophilus growth at non-isothermal conditions was evaluated by comparing predictions with observed growth under dynamic conditions and the results showed a good performance of the model. The model was further used to predict the time-to-spoilage (tts) of evaporated milk. The spoilage of this product caused by acid coagulation when the pH approached a level around 5.2, eight generations after G. stearothermophilus reached the maximum population density (Nmax). Based on the above, the tts was predicted from the growth model as the sum of the time required for the microorganism to multiply from the initial to the maximum level ( [Formula: see text] ), plus the time required after the [Formula: see text] to complete eight generations. The observed tts was very close to the predicted one indicating that the model is able to describe satisfactorily the growth of G. stearothermophilus and to provide realistic predictions for evaporated milk spoilage. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bosnian and American students' attitudes toward electronic monitoring: is it about what we know or where we come from?

PubMed

Muftić, Lisa R; Payne, Brian K; Maljević, Almir

2015-06-01

The use of community corrections continues to grow across the globe as alternatives to incarceration are sought. Little research attention, however, has been directed at correctional alternatives from a global orientation. The purpose of this research study is to compare the way that a sample of criminal justice students from the United States (n = 118) and Bosnia and Herzegovina (n = 133) perceive electronic monitoring. Because electronic monitoring is a newer sentencing alternative and it is used differently in Bosnia and Herzegovina than it is in the United States, it is predicted that Bosnian students will view electronic monitoring differently than will students from the United States. This study finds that while students are largely supportive of electronic monitoring sentences, support is affected by offender type and student nationality. For example, Bosnian students are more supportive of electronic monitoring sentences for drug offenders while American students are more supportive of electronic monitoring sentences for juvenile offenders. Differences were also found across student groups when attitudes toward electronic monitoring and the costs and pains associated with electronic monitoring were assessed. Specifically, American students were less likely to view electronic monitoring as meeting the goals of rehabilitation and more likely to view the conditions and restrictions associated with electronic monitoring as being punitive than Bosnian students were. Implications from these findings, as well as limitations and suggestions for further research are discussed. © The Author(s) 2013.

Climate Prediction Center - Monitoring & Data: Seasonal ENSO Impacts on

Science.gov Websites

page National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center , state and local government Web resources and services. HOME > Monitoring and Data > U.S. Climate and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland

Long-term post-fire effects on spatial ecology and reproductive output of female Agassiz’s desert tortoises (Gopherus agassizii) at a wind energy facility near Palm Springs, California, USA

USGS Publications Warehouse

Lovich, Jeffrey E.; Ennen, Joshua R.; Madrak, Sheila V.; Loughran, Caleb L.; Meyer, Katherin P.; Arundel, Terence R.; Bjurlin, Curtis D.

2011-01-01

We studied the long-term response of a cohort of eight female Agassiz’s desert tortoises (Gopherus agassizii) during the first 15 years following a large fire at a wind energy generation facility near Palm Springs, California, USA. The fire burned a significant portion of the study site in 1995. Tortoise activity areas were mapped using minimum convex polygons for a proximate post-fire interval from 1997 to 2000, and a long-term post-fire interval from 2009 to 2010. In addition, we measured the annual reproductive output of eggs each year and monitored the body condition of tortoises over time. One adult female tortoise was killed by the fire and five tortoises bore exposure scars that were not fatal. Despite predictions that tortoises would make the short-distance movements from burned to nearby unburned habitats, most activity areas and their centroids remained in burned areas for the duration of the study. The percentage of activity area burned did not differ significantly between the two monitoring periods. Annual reproductive output and measures of body condition remained statistically similar throughout the monitoring period. Despite changes in plant composition, conditions at this site appeared to be suitable for survival of tortoises following a major fire. High productivity at the site may have buffered tortoises from the adverse impacts of fire if they were not killed outright. Tortoise populations at less productive desert sites may not have adequate resources to sustain normal activity areas, reproductive output, and body conditions following fire.

Epstein-barr virus DNAemia monitoring for the management of post-transplant lymphoproliferative disorder.

PubMed

Kalra, Amit; Roessner, Cameron; Jupp, Jennifer; Williamson, Tyler; Tellier, Raymond; Chaudhry, Ahsan; Khan, Faisal; Taparia, Minakshi; Jimenez-Zepeda, Victor H; Stewart, Douglas A; Daly, Andrew; Storek, Jan

2018-05-01

Post-transplant lymphoproliferative disorder (PTLD) is a potentially fatal complication of allogeneic hematopoietic cell transplantation (HCT). Epstein-Barr virus (EBV) reactivation (detectable DNAemia) predisposes to the development of PTLD. We retrospectively studied 306 patients monitored for EBV DNAemia after Thymoglobulin-conditioned HCT to determine the utility of the monitoring in the management of PTLD. DNAemia was monitored weekly for ≥12 weeks post-transplantation. Reactivation was detected in 82% of patients. PTLD occurred in 14% of the total patients (17% of patients with reactivation). PTLD was treated with rituximab only when and if the diagnosis was established. This allowed us to evaluate potential DNAemia thresholds for pre-emptive therapy. We suggest 100,000-500,000 IU per mL whole blood as this would result in unnecessary rituximab administration to only 4-20% of patients and near zero mortality due to PTLD. After starting rituximab (for diagnosed PTLD), sustained regression of PTLD occurred in 25/25 (100%) patients in whom DNAemia became undetectable. PTLD progressed or relapsed in 12/17 (71%) patients in whom DNAemia was persistently detectable. In conclusion, for pre-emptive therapy of PTLD, we suggest threshold DNAemia of 100,000-500,000 IU/mL. Persistently detectable DNAemia after PTLD treatment with rituximab appears to have 71% positive predictive value and 100% negative predictive value for PTLD progression/relapse. Copyright © 2018 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

PubMed Central

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-01-01

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

PubMed

Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

2016-04-29

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

Compressive strength and hydration processes of concrete with recycled aggregates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koenders, Eduardus A.B., E-mail: e.a.b.koenders@coc.ufrj.br; Microlab, Delft University of Technology; Pepe, Marco, E-mail: mapepe@unisa.it

2014-02-15

This paper deals with the correlation between the time evolution of the degree of hydration and the compressive strength of Recycled Aggregate Concrete (RAC) for different water to cement ratios and initial moisture conditions of the Recycled Concrete Aggregates (RCAs). Particularly, the influence of such moisture conditions is investigated by monitoring the hydration process and determining the compressive strength development of fully dry or fully saturated recycled aggregates in four RAC mixtures. Hydration processes are monitored via temperature measurements in hardening concrete samples and the time evolution of the degree of hydration is determined through a 1D hydration and heatmore » flow model. The effect of the initial moisture condition of RCAs employed in the considered concrete mixtures clearly emerges from this study. In fact, a novel conceptual method is proposed to predict the compressive strength of RAC-systems, from the initial mixture parameters and the hardening conditions. -- Highlights: •The concrete industry is more and more concerned with sustainability issues. •The use of recycled aggregates is a promising solution to enhance sustainability. •Recycled aggregates affect both hydration processes and compressive strength. •A fundamental approach is proposed to unveil the influence of recycled aggregates. •Some experimental comparisons are presented to validate the proposed approach.« less

Predicting biological condition in southern California streams

USGS Publications Warehouse

Brown, Larry R.; May, Jason T.; Rehn, Andrew C.; Ode, Peter R.; Waite, Ian R.; Kennen, Jonathan G.

2012-01-01

As understanding of the complex relations among environmental stressors and biological responses improves, a logical next step is predictive modeling of biological condition at unsampled sites. We developed a boosted regression tree (BRT) model of biological condition, as measured by a benthic macroinvertebrate index of biotic integrity (BIBI), for streams in urbanized Southern Coastal California. We also developed a multiple linear regression (MLR) model as a benchmark for comparison with the BRT model. The BRT model explained 66% of the variance in B-IBI, identifying watershed population density and combined percentage agricultural and urban land cover in the riparian buffer as the most important predictors of B-IBI, but with watershed mean precipitation and watershed density of manmade channels also important. The MLR model explained 48% of the variance in B-IBI and included watershed population density and combined percentage agricultural and urban land cover in the riparian buffer. For a verification data set, the BRT model correctly classified 75% of impaired sites (B-IBI < 40) and 78% of unimpaired sites (B-IBI = 40). For the same verification data set, the MLR model correctly classified 69% of impaired sites and 87% of unimpaired sites. The BRT model should not be used to predict B-IBI for specific sites; however, the model can be useful for general applications such as identifying and prioritizing regions for monitoring, remediation or preservation, stratifying new bioassessments according to anticipated biological condition, or assessing the potential for change in stream biological condition based on anticipated changes in population density and development in stream buffers.

Mapping and predictive variations of soil bacterial richness across France

PubMed Central

Dequietd, Samuel; Saby, Nicolas P. A.; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition. PMID:29059218

Modeling and predicting the biofilm formation of Salmonella Virchow with respect to temperature and pH.

PubMed

Ariafar, M Nima; Buzrul, Sencer; Akçelik, Nefise

2016-03-01

Biofilm formation of Salmonella Virchow was monitored with respect to time at three different temperature (20, 25 and 27.5 °C) and pH (5.2, 5.9 and 6.6) values. As the temperature increased at a constant pH level, biofilm formation decreased while as the pH level increased at a constant temperature, biofilm formation increased. Modified Gompertz equation with high adjusted determination coefficient (Radj(2)) and low mean square error (MSE) values produced reasonable fits for the biofilm formation under all conditions. Parameters of the modified Gompertz equation could be described in terms of temperature and pH by use of a second order polynomial function. In general, as temperature increased maximum biofilm quantity, maximum biofilm formation rate and time of acceleration of biofilm formation decreased; whereas, as pH increased; maximum biofilm quantity, maximum biofilm formation rate and time of acceleration of biofilm formation increased. Two temperature (23 and 26 °C) and pH (5.3 and 6.3) values were used up to 24 h to predict the biofilm formation of S. Virchow. Although the predictions did not perfectly match with the data, reasonable estimates were obtained. In principle, modeling and predicting the biofilm formation of different microorganisms on different surfaces under various conditions could be possible.

Mapping and predictive variations of soil bacterial richness across France.

PubMed

Terrat, Sébastien; Horrigue, Walid; Dequiedt, Samuel; Saby, Nicolas P A; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

2017-01-01

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

Analysis of three-phase equilibrium conditions for methane hydrate by isometric-isothermal molecular dynamics simulations.

PubMed

Yuhara, Daisuke; Brumby, Paul E; Wu, David T; Sum, Amadeu K; Yasuoka, Kenji

2018-05-14

To develop prediction methods of three-phase equilibrium (coexistence) conditions of methane hydrate by molecular simulations, we examined the use of NVT (isometric-isothermal) molecular dynamics (MD) simulations. NVT MD simulations of coexisting solid hydrate, liquid water, and vapor methane phases were performed at four different temperatures, namely, 285, 290, 295, and 300 K. NVT simulations do not require complex pressure control schemes in multi-phase systems, and the growth or dissociation of the hydrate phase can lead to significant pressure changes in the approach toward equilibrium conditions. We found that the calculated equilibrium pressures tended to be higher than those reported by previous NPT (isobaric-isothermal) simulation studies using the same water model. The deviations of equilibrium conditions from previous simulation studies are mainly attributable to the employed calculation methods of pressure and Lennard-Jones interactions. We monitored the pressure in the methane phase, far from the interfaces with other phases, and confirmed that it was higher than the total pressure of the system calculated by previous studies. This fact clearly highlights the difficulties associated with the pressure calculation and control for multi-phase systems. The treatment of Lennard-Jones interactions without tail corrections in MD simulations also contributes to the overestimation of equilibrium pressure. Although improvements are still required to obtain accurate equilibrium conditions, NVT MD simulations exhibit potential for the prediction of equilibrium conditions of multi-phase systems.

Analysis of three-phase equilibrium conditions for methane hydrate by isometric-isothermal molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Yuhara, Daisuke; Brumby, Paul E.; Wu, David T.; Sum, Amadeu K.; Yasuoka, Kenji

2018-05-01

To develop prediction methods of three-phase equilibrium (coexistence) conditions of methane hydrate by molecular simulations, we examined the use of NVT (isometric-isothermal) molecular dynamics (MD) simulations. NVT MD simulations of coexisting solid hydrate, liquid water, and vapor methane phases were performed at four different temperatures, namely, 285, 290, 295, and 300 K. NVT simulations do not require complex pressure control schemes in multi-phase systems, and the growth or dissociation of the hydrate phase can lead to significant pressure changes in the approach toward equilibrium conditions. We found that the calculated equilibrium pressures tended to be higher than those reported by previous NPT (isobaric-isothermal) simulation studies using the same water model. The deviations of equilibrium conditions from previous simulation studies are mainly attributable to the employed calculation methods of pressure and Lennard-Jones interactions. We monitored the pressure in the methane phase, far from the interfaces with other phases, and confirmed that it was higher than the total pressure of the system calculated by previous studies. This fact clearly highlights the difficulties associated with the pressure calculation and control for multi-phase systems. The treatment of Lennard-Jones interactions without tail corrections in MD simulations also contributes to the overestimation of equilibrium pressure. Although improvements are still required to obtain accurate equilibrium conditions, NVT MD simulations exhibit potential for the prediction of equilibrium conditions of multi-phase systems.

Instantaneous angular speed monitoring of gearboxes under non-cyclic stationary load conditions

NASA Astrophysics Data System (ADS)

Stander, C. J.; Heyns, P. S.

2005-07-01

Recent developments in the condition monitoring and asset management market have led to the commercialisation of online vibration-monitoring systems. These systems are primarily utilised to monitor large mineral mining equipment such as draglines, continuous miners and hydraulic shovels. Online monitoring systems make diagnostic information continuously available for asset management, production outsourcing and maintenance alliances with equipment manufacturers. However, most online vibration-monitoring systems are based on conventional vibration-monitoring technologies, which are prone to giving false equipment deterioration warnings on gears that operate under fluctuating load conditions. A simplified mathematical model of a gear system was developed to illustrate the feasibility of monitoring the instantaneous angular speed (IAS) as a means of monitoring the condition of gears that are subjected to fluctuating load conditions. A distinction is made between cyclic stationary load modulation and non-cyclic stationary load modulation. It is shown that rotation domain averaging will suppress the modulation caused by non-cyclic stationary load conditions but will not suppress the modulation caused by cyclic stationary load conditions. An experimental investigation on a test rig indicated that the IAS of a gear shaft could be monitored with a conventional shaft encoder to indicate a deteriorating gear fault condition.

Using models for the optimization of hydrologic monitoring

USGS Publications Warehouse

Fienen, Michael N.; Hunt, Randall J.; Doherty, John E.; Reeves, Howard W.

2011-01-01

Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interest (regardless of how well-suited that model is for the prediction). However, modeling tools are available that can inform which locations and types of data provide the most 'bang for the buck' for a specified prediction. Put another way, the hydrologist can determine which observation data most reduce the model uncertainty around a specified prediction. An advantage of such an approach is the maximization of limited monitoring resources because it focuses on the difference in prediction uncertainty with or without additional collection of field data. Data worth can be calculated either through the addition of new data or subtraction of existing information by reducing monitoring efforts (Beven, 1993). The latter generally is not widely requested as there is explicit recognition that the worth calculated is fundamentally dependent on the prediction specified. If a water manager needs a new prediction, the benefits of reducing the scope of a monitoring effort, based on an old prediction, may be erased by the loss of information important for the new prediction. This fact sheet focuses on the worth or value of new data collection by quantifying the reduction in prediction uncertainty achieved be adding a monitoring observation. This calculation of worth can be performed for multiple potential locations (and types) of observations, which then can be ranked for their effectiveness for reducing uncertainty around the specified prediction. This is implemented using a Bayesian approach with the PREDUNC utility in the parameter estimation software suite PEST (Doherty, 2010). The techniques briefly described earlier are described in detail in a U.S. Geological Survey Scientific Investigations Report available on the Internet (Fienen and others, 2010; http://pubs.usgs.gov/sir/2010/5159/). This fact sheet presents a synopsis of the techniques as applied to a synthetic model based on a model constructed using properties from the Lake Michigan Basin (Hoard, 2010).

The effects of simulated hypogravity on murine bone marrow cells

NASA Technical Reports Server (NTRS)

Lawless, Desales

1989-01-01

Mouse bone marrow cells grown in complete medium at unit gravity were compared with a similar population cultured in conditions that mimic some aspects of microgravity. After the cells adjusted to the conditions that simulated microgravity, they proliferated as fetal or oncogenic populations; their numbers doubled in twelve hour periods. Differentiated subpopulations were depleted from the heterogeneous mixture with time and the undifferentiated hematopoietic stem cells increased in numbers. The cells in the control groups in unit gravity and those in the bioreactors in conditions of microgravity were monitored under a number of parameters. Each were phenotyped as to cell surface antigens using a panel of monoclonal antibodies and flow cytometry. Other parameters compared included: pH, glucose uptake, oxygen consumption and carbon-dioxide production. Nuclear DNA was monitored by flow cytometry. Functional responses were studied by mitogenic stimulation by various lectins. The importance of these findings should have relevance to the space program. Cells should behave predictably in zero gravity; specific populations can be eliminated from diverse populations and other populations isolated. The availability of stem cell populations will enhance both bone marrow and gene transplant programs. Stem cells will permit developmental biologists study the paths of hematopoiesis.

Heat stress assessment in artistic glass units

PubMed Central

d’AMBROSIO ALFANO, Francesca Romana; PALELLA, Boris Igor; RICCIO, Giuseppe; BARTALINI, Massimo; STRAMBI, Fabio; MALCHAIRE, Jacques

2017-01-01

Heat stress in glass industry is mainly studied in large and highly mechanized manufacturing Units. To the contrary, few studies were carried out in small factories specialized in hand-made products. To stress the need of combined objective and medical surveys in these environments, this paper deals with a simultaneous climatic and physiological investigation of working conditions in artistic crystal glass factories in Tuscany (Italy). The microclimatic monitoring, through a continuous survey has been carried out in early spring. The main physiological parameters (metabolic rate, heart rate, tympanic temperature and water loss) were measured over the whole shifts. The results show that, despite the arduousness of the working conditions, the heat stress levels are physiologically tolerable. The predictions made using the PHS model at the Analysis level described in ISO 15265 agree closely to the observed values, validating the use of PHS model in these conditions. This model was then used to analyse what is likely to be the situation during the summer. It is concluded that the heat constraint will be very high and that some steps must be taken from the spring to monitor closely the exposed workers in the summer and take measures to prevent any heat accident. PMID:29109359

Heat stress assessment in artistic glass units.

PubMed

d'AMBROSIO Alfano, Francesca Romana; Palella, Boris Igor; Riccio, Giuseppe; Bartalini, Massimo; Strambi, Fabio; Malchaire, Jacques

2018-04-07

Heat stress in glass industry is mainly studied in large and highly mechanized manufacturing Units. To the contrary, few studies were carried out in small factories specialized in hand-made products. To stress the need of combined objective and medical surveys in these environments, this paper deals with a simultaneous climatic and physiological investigation of working conditions in artistic crystal glass factories in Tuscany (Italy). The microclimatic monitoring, through a continuous survey has been carried out in early spring. The main physiological parameters (metabolic rate, heart rate, tympanic temperature and water loss) were measured over the whole shifts. The results show that, despite the arduousness of the working conditions, the heat stress levels are physiologically tolerable. The predictions made using the PHS model at the Analysis level described in ISO 15265 agree closely to the observed values, validating the use of PHS model in these conditions. This model was then used to analyse what is likely to be the situation during the summer. It is concluded that the heat constraint will be very high and that some steps must be taken from the spring to monitor closely the exposed workers in the summer and take measures to prevent any heat accident.

Mood congruent tuning of reward expectation in positive mood: evidence from FRN and theta modulations

PubMed Central

Pourtois, Gilles

2017-01-01

Abstract Positive mood broadens attention and builds additional mental resources. However, its effect on performance monitoring and reward prediction errors remain unclear. To examine this issue, we used a standard mood induction procedure (based on guided imagery) and asked 45 participants to complete a gambling task suited to study reward prediction errors by means of the feedback-related negativity (FRN) and mid-frontal theta band power. Results showed a larger FRN for negative feedback as well as a lack of reward expectation modulation for positive feedback at the theta level with positive mood, relative to a neutral mood condition. A control analysis showed that this latter result could not be explained by the mere superposition of the event-related brain potential component on the theta oscillations. Moreover, these neurophysiological effects were evidenced in the absence of impairments at the behavioral level or increase in autonomic arousal with positive mood, suggesting that this mood state reliably altered brain mechanisms of reward prediction errors during performance monitoring. We interpret these new results as reflecting a genuine mood congruency effect, whereby reward is anticipated as the default outcome with positive mood and therefore processed as unsurprising (even when it is unlikely), while negative feedback is perceived as unexpected. PMID:28199707

4D offline PET-based treatment verification in scanned ion beam therapy: a phantom study

NASA Astrophysics Data System (ADS)

Kurz, Christopher; Bauer, Julia; Unholtz, Daniel; Richter, Daniel; Stützer, Kristin; Bert, Christoph; Parodi, Katia

2015-08-01

At the Heidelberg Ion-Beam Therapy Center, patient irradiation with scanned proton and carbon ion beams is verified by offline positron emission tomography (PET) imaging: the {β+} -activity measured within the patient is compared to a prediction calculated on the basis of the treatment planning data in order to identify potential delivery errors. Currently, this monitoring technique is limited to the treatment of static target structures. However, intra-fractional organ motion imposes considerable additional challenges to scanned ion beam radiotherapy. In this work, the feasibility and potential of time-resolved (4D) offline PET-based treatment verification with a commercial full-ring PET/CT (x-ray computed tomography) device are investigated for the first time, based on an experimental campaign with moving phantoms. Motion was monitored during the gated beam delivery as well as the subsequent PET acquisition and was taken into account in the corresponding 4D Monte-Carlo simulations and data evaluation. Under the given experimental conditions, millimeter agreement between the prediction and measurement was found. Dosimetric consequences due to the phantom motion could be reliably identified. The agreement between PET measurement and prediction in the presence of motion was found to be similar as in static reference measurements, thus demonstrating the potential of 4D PET-based treatment verification for future clinical applications.

Monitoring Changes of Tropical Extreme Rainfall Events Using Differential Absorption Barometric Radar (DiBAR)

NASA Technical Reports Server (NTRS)

Lin, Bing; Harrah, Steven; Lawrence, R. Wes; Hu, Yongxiang; Min, Qilong

2015-01-01

This work studies the potential of monitoring changes in tropical extreme rainfall events such as tropical storms from space using a Differential-absorption BArometric Radar (DiBAR) operating at 50-55 gigahertz O2 absorption band to remotely measure sea surface air pressure. Air pressure is among the most important variables that affect atmospheric dynamics, and currently can only be measured by limited in-situ observations over oceans. Analyses show that with the proposed radar the errors in instantaneous (averaged) pressure estimates can be as low as approximately 5 millibars (approximately 1 millibar) under all weather conditions. With these sea level pressure measurements, the forecasts, analyses and understanding of these extreme events in both short and long time scales can be improved. Severe weathers, especially hurricanes, are listed as one of core areas that need improved observations and predictions in WCRP (World Climate Research Program) and NASA Decadal Survey (DS) and have major impacts on public safety and national security through disaster mitigation. Since the development of the DiBAR concept about a decade ago, our team has made substantial progress in advancing the concept. Our feasibility assessment clearly shows the potential of sea surface barometry using existing radar technologies. We have developed a DiBAR system design, fabricated a Prototype-DiBAR (P-DiBAR) for proof-of-concept, conducted lab, ground and airborne P-DiBAR tests. The flight test results are consistent with our instrumentation goals. Observational system simulation experiments for space DiBAR performance show substantial improvements in tropical storm predictions, not only for the hurricane track and position but also for the hurricane intensity. DiBAR measurements will lead us to an unprecedented level of the prediction and knowledge on tropical extreme rainfall weather and climate conditions.

A novel method for patient exit and entrance dose prediction based on water equivalent path length measured with an amorphous silicon electronic portal imaging device.

PubMed

Kavuma, Awusi; Glegg, Martin; Metwaly, Mohamed; Currie, Garry; Elliott, Alex

2010-01-21

In vivo dosimetry is one of the quality assurance tools used in radiotherapy to monitor the dose delivered to the patient. Electronic portal imaging device (EPID) images for a set of solid water phantoms of varying thicknesses were acquired and the data fitted onto a quadratic equation, which relates the reduction in photon beam intensity to the attenuation coefficient and material thickness at a reference condition. The quadratic model is used to convert the measured grey scale value into water equivalent path length (EPL) at each pixel for any material imaged by the detector. For any other non-reference conditions, scatter, field size and MU variation effects on the image were corrected by relative measurements using an ionization chamber and an EPID. The 2D EPL is linked to the percentage exit dose table, for different thicknesses and field sizes, thereby converting the plane pixel values at each point into a 2D dose map. The off-axis ratio is corrected using envelope and boundary profiles generated from the treatment planning system (TPS). The method requires field size, monitor unit and source-to-surface distance (SSD) as clinical input parameters to predict the exit dose, which is then used to determine the entrance dose. The measured pixel dose maps were compared with calculated doses from TPS for both entrance and exit depth of phantom. The gamma index at 3% dose difference (DD) and 3 mm distance to agreement (DTA) resulted in an average of 97% passing for the square fields of 5, 10, 15 and 20 cm. The exit dose EPID dose distributions predicted by the algorithm were in better agreement with TPS-calculated doses than phantom entrance dose distributions.

A novel method for patient exit and entrance dose prediction based on water equivalent path length measured with an amorphous silicon electronic portal imaging device

NASA Astrophysics Data System (ADS)

Kavuma, Awusi; Glegg, Martin; Metwaly, Mohamed; Currie, Garry; Elliott, Alex

2010-01-01

In vivo dosimetry is one of the quality assurance tools used in radiotherapy to monitor the dose delivered to the patient. Electronic portal imaging device (EPID) images for a set of solid water phantoms of varying thicknesses were acquired and the data fitted onto a quadratic equation, which relates the reduction in photon beam intensity to the attenuation coefficient and material thickness at a reference condition. The quadratic model is used to convert the measured grey scale value into water equivalent path length (EPL) at each pixel for any material imaged by the detector. For any other non-reference conditions, scatter, field size and MU variation effects on the image were corrected by relative measurements using an ionization chamber and an EPID. The 2D EPL is linked to the percentage exit dose table, for different thicknesses and field sizes, thereby converting the plane pixel values at each point into a 2D dose map. The off-axis ratio is corrected using envelope and boundary profiles generated from the treatment planning system (TPS). The method requires field size, monitor unit and source-to-surface distance (SSD) as clinical input parameters to predict the exit dose, which is then used to determine the entrance dose. The measured pixel dose maps were compared with calculated doses from TPS for both entrance and exit depth of phantom. The gamma index at 3% dose difference (DD) and 3 mm distance to agreement (DTA) resulted in an average of 97% passing for the square fields of 5, 10, 15 and 20 cm. The exit dose EPID dose distributions predicted by the algorithm were in better agreement with TPS-calculated doses than phantom entrance dose distributions.

Hot dogs: High ambient temperatures impact reproductive success in a tropical carnivore.

PubMed

Woodroffe, Rosie; Groom, Rosemary; McNutt, J Weldon

2017-10-01

Climate change imposes an urgent need to recognise and conserve the species likely to be worst affected. However, while ecologists have mostly explored indirect effects of rising ambient temperatures on temperate and polar species, physiologists have predicted direct impacts on tropical species. The African wild dog (Lycaon pictus), a tropical species, exhibits few of the traits typically used to predict climate change vulnerability. Nevertheless, we predicted that wild dog populations might be sensitive to weather conditions, because the species shows strongly seasonal reproduction across most of its geographical range. We explored associations between weather conditions, reproductive costs, and reproductive success, drawing on long-term wild dog monitoring data from sites in Botswana (20°S, 24 years), Kenya (0°N, 12 years), and Zimbabwe (20°S, 6 years). High ambient temperatures were associated with reduced foraging time, especially during the energetically costly pup-rearing period. Across all three sites, packs which reared pups at high ambient temperatures produced fewer recruits than did those rearing pups in cooler weather; at the non-seasonal Kenya site such packs also had longer inter-birth intervals. Over time, rising ambient temperatures at the (longest-monitored) Botswana site coincided with falling wild dog recruitment. Our findings suggest a direct impact of high ambient temperatures on African wild dog demography, indicating that this species, which is already globally endangered, may be highly vulnerable to climate change. This vulnerability would have been missed by simplistic trait-based assessments, highlighting the limitations of such assessments. Seasonal reproduction, which is less common at low latitudes than at higher latitudes, might be a useful indicator of climate change vulnerability among tropical species. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

The role of answer fluency and perceptual fluency as metacognitive cues for initiating analytic thinking.

PubMed

Thompson, Valerie A; Turner, Jamie A Prowse; Pennycook, Gordon; Ball, Linden J; Brack, Hannah; Ophir, Yael; Ackerman, Rakefet

2013-08-01

Although widely studied in other domains, relatively little is known about the metacognitive processes that monitor and control behaviour during reasoning and decision-making. In this paper, we examined the conditions under which two fluency cues are used to monitor initial reasoning: answer fluency, or the speed with which the initial, intuitive answer is produced (Thompson, Prowse Turner, & Pennycook, 2011), and perceptual fluency, or the ease with which problems can be read (Alter, Oppenheimer, Epley, & Eyre, 2007). The first two experiments demonstrated that answer fluency reliably predicted Feeling of Rightness (FOR) judgments to conditional inferences and base rate problems, which subsequently predicted the amount of deliberate processing as measured by thinking time and answer changes; answer fluency also predicted retrospective confidence judgments (Experiment 3b). Moreover, the effect of answer fluency on reasoning was independent from the effect of perceptual fluency, establishing that these are empirically independent constructs. In five experiments with a variety of reasoning problems similar to those of Alter et al. (2007), we found no effect of perceptual fluency on FOR, retrospective confidence or accuracy; however, we did observe that participants spent more time thinking about hard to read stimuli, although this additional time did not result in answer changes. In our final two experiments, we found that perceptual disfluency increased accuracy on the CRT (Frederick, 2005), but only amongst participants of high cognitive ability. As Alter et al.'s samples were gathered from prestigious universities, collectively, the data to this point suggest that perceptual fluency prompts additional processing in general, but this processing may results in higher accuracy only for the most cognitively able. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Modeling Particle Exposure in US Trucking Terminals

PubMed Central

Davis, ME; Smith, TJ; Laden, F; Hart, JE; Ryan, LM; Garshick, E

2007-01-01

Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the US trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor “background” location. Using the SEM method, we predict: 1) personal exposures as a function of work related exposure and smoking status; 2) work related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and 3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling. PMID:16856739

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni

PubMed Central

Yang, Ya; Cheng, Wanting; Wu, Xiaoying; Huang, Shaoyu; Deng, Zhuohui; Zeng, Xin; Yang, Yu; Wu, Zhongdao; Chen, Yue; Zhou, Yibiao; Jiang, Qingwu

2018-01-01

Background Schistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control. Methods and findings A comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia. Conclusions Our results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based on habitat suitability, which would help for prioritizing monitoring and management efforts for B. straminea control in the context of ongoing climate change and human disturbances. PMID:29813073

Integrating scales of seagrass monitoring to meet conservation needs

USGS Publications Warehouse

Neckles, Hilary A.; Kopp, Blaine S.; Peterson, Bradley J.; Pooler, Penelope S.

2012-01-01

We evaluated a hierarchical framework for seagrass monitoring in two estuaries in the northeastern USA: Little Pleasant Bay, Massachusetts, and Great South Bay/Moriches Bay, New York. This approach includes three tiers of monitoring that are integrated across spatial scales and sampling intensities. We identified monitoring attributes for determining attainment of conservation objectives to protect seagrass ecosystems from estuarine nutrient enrichment. Existing mapping programs provided large-scale information on seagrass distribution and bed sizes (tier 1 monitoring). We supplemented this with bay-wide, quadrat-based assessments of seagrass percent cover and canopy height at permanent sampling stations following a spatially distributed random design (tier 2 monitoring). Resampling simulations showed that four observations per station were sufficient to minimize bias in estimating mean percent cover on a bay-wide scale, and sample sizes of 55 stations in a 624-ha system and 198 stations in a 9,220-ha system were sufficient to detect absolute temporal increases in seagrass abundance from 25% to 49% cover and from 4% to 12% cover, respectively. We made high-resolution measurements of seagrass condition (percent cover, canopy height, total and reproductive shoot density, biomass, and seagrass depth limit) at a representative index site in each system (tier 3 monitoring). Tier 3 data helped explain system-wide changes. Our results suggest tiered monitoring as an efficient and feasible way to detect and predict changes in seagrass systems relative to multi-scale conservation objectives.

How consumer physical activity monitors could transform human physiology research.

PubMed

Wright, Stephen P; Hall Brown, Tyish S; Collier, Scott R; Sandberg, Kathryn

2017-03-01

A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O 2 , and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing. Copyright © 2017 the American Physiological Society.

How consumer physical activity monitors could transform human physiology research

PubMed Central

Hall Brown, Tyish S.; Collier, Scott R.; Sandberg, Kathryn

2017-01-01

A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O2, and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing. PMID:28052867

Non-invasive Monitoring of Intracranial Pressure Using Transcranial Doppler Ultrasonography: Is It Possible?

PubMed

Cardim, Danilo; Robba, C; Bohdanowicz, M; Donnelly, J; Cabella, B; Liu, X; Cabeleira, M; Smielewski, P; Schmidt, B; Czosnyka, M

2016-12-01

Although intracranial pressure (ICP) is essential to guide management of patients suffering from acute brain diseases, this signal is often neglected outside the neurocritical care environment. This is mainly attributed to the intrinsic risks of the available invasive techniques, which have prevented ICP monitoring in many conditions affecting the intracranial homeostasis, from mild traumatic brain injury to liver encephalopathy. In such scenario, methods for non-invasive monitoring of ICP (nICP) could improve clinical management of these conditions. A review of the literature was performed on PUBMED using the search keywords 'Transcranial Doppler non-invasive intracranial pressure.' Transcranial Doppler (TCD) is a technique primarily aimed at assessing the cerebrovascular dynamics through the cerebral blood flow velocity (FV). Its applicability for nICP assessment emerged from observation that some TCD-derived parameters change during increase of ICP, such as the shape of FV pulse waveform or pulsatility index. Methods were grouped as: based on TCD pulsatility index; aimed at non-invasive estimation of cerebral perfusion pressure and model-based methods. Published studies present with different accuracies, with prediction abilities (AUCs) for detection of ICP ≥20 mmHg ranging from 0.62 to 0.92. This discrepancy could result from inconsistent assessment measures and application in different conditions, from traumatic brain injury to hydrocephalus and stroke. Most of the reports stress a potential advantage of TCD as it provides the possibility to monitor changes of ICP in time. Overall accuracy for TCD-based methods ranges around ±12 mmHg, with a great potential of tracing dynamical changes of ICP in time, particularly those of vasogenic nature.

Treatment Satisfaction in a Randomized Clinical Trial of mHealth Smoking Abstinence Reinforcement.

PubMed

Alessi, Sheila M; Rash, Carla J

2017-01-01

The importance of patient satisfaction in modern healthcare is widely recognized, but research on satisfaction in the context of smoking cessation has not kept pace. The purpose of this study was to explore treatment satisfaction in a sample of smokers (N=84) randomized to one of two smoking cessation treatment interventions (mHealth reinforcement and mHealth monitoring) that used cell phone-based procedures to monitor smoking status in individuals' natural environments for 4 weeks. Starting on the target quit date, participants received usual care smoking cessation treatment consisting of 8 weeks of transdermal nicotine and 4 weeks of twice-weekly telephone counseling were also prompted 1 to 3 times daily (with exact number and timing not disclosed beforehand) to use a study cell phone and CO monitor to complete a CO self-test, video-record the process, and submit videos using multimedia messaging within 2 hours. mHealth reinforcement participants could earn prizes for smoking-negative on-time CO tests. A treatment satisfaction survey was completed at the end of the 4-week monitoring/reinforcement phase. Results indicate that participants overwhelmingly endorsed high levels of overall satisfaction in both conditions. Treatment adherence did not differ between conditions, but was positively associated with endorsing the highest satisfaction with help quitting with the intervention (p<.01 to .03). mHealth reinforcement was associated with increased longest duration of abstinence (p<.01). Controlling for relevant participant characteristics and treatment adherence, longest duration of abstinence robustly predicted highest satisfaction with help quitting and mediated the effect of treatment condition on that satisfaction. Further research on treatment satisfaction may aid the development of effective abstinence reinforcement and other smoking cessation interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coble, Jamie; Orton, Christopher; Schwantes, Jon

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Usefulness of simultaneous and sequential monitoring of glucose level and electrocardiogram in monkeys treated with gatifloxacin under conscious and nonrestricted conditions.

PubMed

Yoshimatsu, Yu; Ishizaka, Tomomichi; Chiba, Katsuyoshi; Mori, Kazuhiko

2018-05-10

Drug-induced cardiac electrophysiological abnormalities accompanied by hypoglycemia or hyperglycemia increase the risk for life-threatening arrhythmia. To assess the drug-induced cardiotoxic potential associated with extraordinary blood glucose (GLU) levels, the effect of gatifloxacin (GFLX) which was frequently associated with GLU abnormality and QT/QTc prolongations in the clinic on blood GLU and electrocardiogram (ECG) parameters was investigated in cynomolgus monkeys (n=4) given GFLX orally in an ascending dose regimen (10, 30, 60 and 100 mg/kg). Simultaneous and sequential GLU and ECG monitoring with a continuous GLU monitoring system and Holter ECG, respectively, were conducted for 24 h under free-moving conditions. Consequently, GFLX at 30 and 60 mg/kg dose-dependently induced a transient decrease in GLU without any ECG abnormality 2-4 h postdose. Highest dose of 100 mg/kg caused severe hypoglycemia with a mean GLU of <30 mg/dL, accompanied by remarkable QT/QTc prolongations by 20-30% in all animals. In contrast, hyperglycemia without QT/QTc prolongations was noted 24 h after dosing in one animal. A close correlation between GLU and QTc values was observed in animals treated with 100 mg/kg, suggesting that GFLX-induced hypoglycemia enhanced QT/QTc prolongations. Furthermore, the 24-h sequential GLU monitoring data clearly distinguished between GFLX-induced GLU abnormality and physiological GLU changes influenced by feeding throughout the day. In conclusion, the combined assessment of continuous GLU and ECG monitoring is valuable in predicting the drug-induced cardio-electrophysiological risk associated with both GLU and ECG abnormalities.

Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

Tadesse, T.; Zaitchik, B. F.; Habib, S.; Funk, C. C.; Senay, G. B.; Dinku, T.; Policelli, F. S.; Block, P.; Baigorria, G. A.; Beyene, S.; Wardlow, B.; Hayes, M. J.

2014-12-01

The development of effective strategies to adapt to changes in the character of droughts and floods in Africa will rely on improved seasonal prediction systems that are robust to an evolving climate baseline and can be integrated into disaster preparedness and response. Many efforts have been made to build models to improve seasonal forecasts in the Greater Horn of Africa region (GHA) using satellite and climate data, but these efforts and models must be improved and translated into future conditions under evolving climate conditions. This has considerable social significance, but is challenged by the nature of climate predictability and the adaptability of coupled natural and human systems facing exposure to climate extremes. To address these issues, work is in progress under a project funded by NASA. The objectives of the project include: 1) Characterize and explain large-scale drivers in the ocean-atmosphere-land system associated with years of extreme flood or drought in the GHA. 2) Evaluate the performance of state-of-the-art seasonal forecast methods for prediction of decision-relevant metrics of hydrologic extremes. 3) Apply seasonal forecast systems to prediction of socially relevant impacts on crops, flood risk, and economic outcomes, and assess the value of these predictions to decision makers. 4) Evaluate the robustness of seasonal prediction systems to evolving climate conditions. The National Drought Mitigation Center (University of Nebraska-Lincoln, USA) is leading this project in collaboration with the USGS, Johns Hopkins University, University of Wisconsin-Madison, the International Research Institute for Climate and Society, NASA, and GHA local experts. The project is also designed to have active engagement of end users in various sectors, university researchers, and extension agents in GHA through workshops and/or webinars. This project is expected improve and implement new and existing climate- and remote sensing-based agricultural, meteorological, and hydrologic drought and flood monitoring products (or indicators) that can enhance the preparedness for extreme climate events and climate change adaptation and mitigation strategies in the GHA. Even though this project is in its first year, the preliminary results and future plans to carry out the objectives will be presented.

Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

USGS Publications Warehouse

Gonzalez, Raul; Conn, Kathleen E.; Crosswell, Joey; Noble, Rachel

2012-01-01

Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18–24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.

Third National Aeronautics and Space Administration Weather and climate program science review

NASA Technical Reports Server (NTRS)

Kreins, E. R. (Editor)

1977-01-01

Research results of developing experimental and prototype operational systems, sensors, and space facilities for monitoring, and understanding the atmosphere are reported. Major aspects include: (1) detection, monitoring, and prediction of severe storms; (2) improvement of global forecasting; and (3) monitoring and prediction of climate change.

Climate Prediction Center - Monitoring and Data Index

Science.gov Websites

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News ; Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface Temperature Indices

Design of a real-time and continua-based framework for care guideline recommendations.

PubMed

Lin, Yu-Feng; Shie, Hsin-Han; Yang, Yi-Ching; Tseng, Vincent S

2014-04-16

Telehealth is an important issue in the medical and healthcare domains. Although a number of systems have been developed to meet the demands of emerging telehealth services, the following problems still remain to be addressed: (1) most systems do not monitor/predict the vital signs states so that they are able to send alarms to caregivers in real-time; (2) most systems do not focus on reducing the amount of work that caregivers need to do, and provide patients with remote care; and (3) most systems do not recommend guidelines for caregivers. This study thus proposes a framework for a real-time and Continua-based Care Guideline Recommendation System (Cagurs) which utilizes mobile device platforms to provide caregivers of chronic patients with real-time care guideline recommendations, and that enables vital signs data to be transmitted between different devices automatically, using the Continua standard. Moreover, the proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients, and then offers related recommended care guidelines to caregivers so that they can offer preventive care in a timely manner.

Modelling of aircrew radiation exposure from galactic cosmic rays and solar particle events.

PubMed

Takada, M; Lewis, B J; Boudreau, M; Al Anid, H; Bennett, L G I

2007-01-01

Correlations have been developed for implementation into the semi-empirical Predictive Code for Aircrew Radiation Exposure (PCAIRE) to account for effects of extremum conditions of solar modulation and low altitude based on transport code calculations. An improved solar modulation model, as proposed by NASA, has been further adopted to interpolate between the bounding correlations for solar modulation. The conversion ratio of effective dose to ambient dose equivalent, as applied to the PCAIRE calculation (based on measurements) for the legal regulation of aircrew exposure, was re-evaluated in this work to take into consideration new ICRP-92 radiation-weighting factors and different possible irradiation geometries of the source cosmic-radiation field. A computational analysis with Monte Carlo N-Particle eXtended Code was further used to estimate additional aircrew exposure that may result from sporadic solar energetic particle events considering real-time monitoring by the Geosynchronous Operational Environmental Satellite. These predictions were compared with the ambient dose equivalent rates measured on-board an aircraft and to count rate data observed at various ground-level neutron monitors.

Fluorescence Spectroscopy and Chemometric Modeling for Bioprocess Monitoring

PubMed Central

Faassen, Saskia M.; Hitzmann, Bernd

2015-01-01

On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables. PMID:25942644

Design of a Real-Time and Continua-Based Framework for Care Guideline Recommendations

PubMed Central

Lin, Yu-Feng; Shie, Hsin-Han; Yang, Yi-Ching; Tseng, Vincent S.

2014-01-01

Telehealth is an important issue in the medical and healthcare domains. Although a number of systems have been developed to meet the demands of emerging telehealth services, the following problems still remain to be addressed: (1) most systems do not monitor/predict the vital signs states so that they are able to send alarms to caregivers in real-time; (2) most systems do not focus on reducing the amount of work that caregivers need to do, and provide patients with remote care; and (3) most systems do not recommend guidelines for caregivers. This study thus proposes a framework for a real-time and Continua-based Care Guideline Recommendation System (Cagurs) which utilizes mobile device platforms to provide caregivers of chronic patients with real-time care guideline recommendations, and that enables vital signs data to be transmitted between different devices automatically, using the Continua standard. Moreover, the proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients, and then offers related recommended care guidelines to caregivers so that they can offer preventive care in a timely manner. PMID:24743843

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

NASA Astrophysics Data System (ADS)

Padhee, S. K.; Nikam, B. R.; Aggarwal, S. P.; Garg, V.

2014-11-01

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October-April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct-Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

A depolarization and attenuation experiment using the CTS satellite. Volume 1: Experiment description

NASA Technical Reports Server (NTRS)

Bostian, C. W.; Holt, S. B., Jr.; Kauffman, S. R.; Manus, E. A.; Marshall, R. E.; Stutzman, W. L.; Wiley, P. H.

1976-01-01

An experiment for measuring precipitation attenuation and depolarization on the Communications Technology Satellite (CTS) 11.7 GHz downlink is described. Attenuation and depolarization of the signal received from the spacecraft is monitored on a 24 hour basis. Data is correlated with ground weather conditions. Theoretical models for millimeter wave propagation through rain are refined for maximum agreement with observed data. Techniques are developed for predicting and mimimizing the effects of rain scatter and depolarization on future satellite communication systems.

Wave Breaking Influence in a Coupled Model of the Atmosphere-Ocean Wave Boundary Layers under Very High Wind Conditions

DTIC Science & Technology

2006-09-30

PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of New South Wales,School of Mathematics,Sydney 2052, Australia, 8. PERFORMING... ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S...published, refereed] Leslie, LM, MS Speer and L. Qi, (2003): Prediction of Extreme Rainfall for the Coffs Harbour Catchment. Aust. Meteor. Mag., 52, 95

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy

PubMed Central

Diago, Maria P.; Fernández-Novales, Juan; Gutiérrez, Salvador; Marañón, Miguel; Tardaguila, Javier

2018-01-01

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψs) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction (RP2) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture. PMID:29441086

Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy.

PubMed

Diago, Maria P; Fernández-Novales, Juan; Gutiérrez, Salvador; Marañón, Miguel; Tardaguila, Javier

2018-01-01

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψ s ) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction ([Formula: see text]) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture.

Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure.

PubMed

Dhurandhar, E J; Kaiser, K A; Dawson, J A; Alcorn, A S; Keating, K D; Allison, D B

2015-08-01

Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs). We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. subjects per treatment arm ≥5; ≥1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ≥80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation. Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12-44% and 55-64% less weight loss than expected, respectively, under an assumption of no behavioral compensation. Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.

Atmospheric monitoring and model applications at the Pierre Auger Observatory

NASA Astrophysics Data System (ADS)

Keilhauer, Bianca

2015-03-01

The Pierre Auger Observatory detects high-energy cosmic rays with energies above ˜1017 eV. It is built as a multi-hybrid detector measuring extensive air showers with different techniques. For the reconstruction of extensive air showers, the atmospheric conditions at the site of the Observatory have to be known quite well. This is particularly true for reconstructions based on data obtained by the fluorescence technique. For these data, not only the weather conditions near ground are relevant, most important are altitude-dependent atmospheric profiles. The Pierre Auger Observatory has set up a dedicated atmospheric monitoring programme at the site in the Mendoza province, Argentina. Beyond this, exploratory studies were performed in Colorado, USA, for possible installations in the northern hemisphere. In recent years, the atmospheric monitoring programme at the Pierre Auger Observatory was supplemented by applying data from atmospheric models. Both GDAS and HYSPLIT are developments by the US weather department NOAA and the data are freely available. GDAS is a global model of the atmospheric state parameters on a 1 degree geographical grid, based on real-time measurements and numeric weather predictions, providing a full altitude-dependent data set every 3 hours. HYSPLIT is a powerful tool to track the movement of air masses at various heights, and with it the aerosols. Combining local measurements of the atmospheric state variables and aerosol scattering with the given model data, advanced studies about atmospheric conditions can be performed and high precision air shower reconstructions are achieved.

Review and Future Research Directions about Major Monitoring Method of Soil Erosion

NASA Astrophysics Data System (ADS)

LI, Yue; Bai, Xiaoyong; Tian, Yichao; Luo, Guangjie

2017-05-01

Soil erosion is a highly serious ecological problem that occurs worldwide. Hence,scientific methods for accurate monitoring are needed to obtain soil erosion data. At present,numerous methods on soil erosion monitoring are being used internationally. In this paper, wepresent a systematic classification of these methods based on the date of establishment andtype of approach. This classification comprises five categories: runoff plot method, erosion pinmethod, radionuclide tracer method, model estimation, and 3S technology combined method.The backgrounds of their establishment are briefly introduced, the history of their developmentis reviewed, and the conditions for their application are enumerated. Their respectiveadvantages and disadvantages are compared and analysed, and future prospects regarding theirdevelopment are discussed. We conclude that the methods of soil erosion monitoring in the past 100 years of their development constantly considered the needs of the time. According to the progress of soil erosion monitoring technology throughout its history, we predict that the future trend in this field would move toward the development of quantitative, precise, and composite methods. This report serves as a valuable reference for scientific and technological workers globally, especially those engaged in soil erosion research.

Golden Eagle Monitoring Plan for the Desert Renewable Energy Conservation Plan

USGS Publications Warehouse

Wiens, David; Kolar, Patrick; Katzner, Todd

2018-01-01

This report describes options for monitoring the status and population trends of the golden eagle (Aquila chrysaetos) within the Desert Renewable Energy Conservation Plan (DRECP) area of Southern California in maintaining stable or increasing population in the planning area. The report profiles the ecology of golden eagles in the region and provides a range of potential sampling options to address monitoring needs and objectives. This approach also focused on links between changes in human land-use, golden eagle nesting and foraging habitat conditions, and population dynamics. The report outlines how monitoring data from demographic, prey, and habitat studies were used to develop a predictive demographic model for golden eagles in the DRECP area. Results from the model simulations suggest increases in renewable energy development could have negative consequences for population trajectories. Results also suggest site-specific conservation actions could reduce the magnitude of negative impacts to the local population of eagles. A monitoring framework is proposed including: (1) annual assessments of site-occupancy and reproduction by territorial pairs of golden eagles (including rates at which sites become colonized or vacated over time); (2) estimates of survival, movements, and intensity of use of landscapes by breeding and non-breeding golden eagles; (3) periodic (conducted every two to four years) assessments of nesting and foraging habitats, prey populations, and associations with land-use and management activities; and (4) updating the predictive demographic model with new information obtained on eagles and associated population stressors. The results of this research were published in the Journal of Rapture Research, Wiens, David,Inman, Rich D., Esque, Todd C., Longshore, Kathleen M. and Nussear, Kenneth (2017). Spatial Demographic Models to Inform Conservation Planning of Golden Eagles in Renewable Energy Landscapes. 51(3):234-257.

Effects of Interactive Voice Response Self-Monitoring on Natural Resolution of Drinking Problems: Utilization and Behavioral Economic Factors

PubMed Central

Tucker, Jalie A.; Roth, David L.; Huang, Jin; Scott Crawford, M.; Simpson, Cathy A.

2012-01-01

Objective: Most problem drinkers do not seek help, and many recover on their own. A randomized controlled trial evaluated whether supportive interactive voice response (IVR) self-monitoring facilitated such “natural” resolutions. Based on behavioral economics, effects on drinking outcomes were hypothesized to vary with drinkers’ baseline “time horizons,” reflecting preferences among commodities of different value available over different delays and with their IVR utilization. Method: Recently resolved untreated problem drinkers were randomized to a 24-week IVR self-monitoring program (n = 87) or an assessment-only control condition (n = 98). Baseline interviews assessed outcome predictors including behavioral economic measures of reward preferences (delay discounting, pre-resolution monetary allocation to alcohol vs. savings). Six-month outcomes were categorized as resolved abstinent, resolved nonabstinent, unresolved, or missing. Complier average causal effect (CACE) models examined IVR self-monitoring effects. Results: IVR self-monitoring compliers (≥70% scheduled calls completed) were older and had greater pre-resolution drinking control and lower discounting than noncompliers (<70%). A CACE model interaction showed that observed compliers in the IVR group with shorter time horizons (expressed by greater pre-resolution spending on alcohol than savings) were more likely to attain moderation than abstinent resolutions compared with predicted compliers in the control group with shorter time horizons and with all noncompliers. Intention-to-treat analytical models revealed no IVR-related effects. More balanced spending on savings versus alcohol predicted moderation in both approaches. Conclusions: IVR interventions should consider factors affecting IVR utilization and drinking outcomes, including person-specific behavioral economic variables. CACE models provide tools to evaluate interventions involving extended participation. PMID:22630807

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

The application of a piezo-resistive cardiorespiratory sensor system in an automobile safety belt.

PubMed

Hamdani, Syed Talha Ali; Fernando, Anura

2015-03-30

Respiratory and heart failure are conditions that can occur with little warning and may also be difficult to predict. Therefore continuous monitoring of these bio-signals is advantageous for ensuring human health. The car safety belt is mainly designed to secure the occupants of the vehicle in the event of an accident. In the current research a prototype safety belt is developed, which is used to acquire respiratory and heart signals, under laboratory conditions. The current safety belt is constructed using a copper ink based nonwoven material, which works based on the piezo-resistive effect due to the pressure exerted on the sensor as a result of expansion of the thorax/abdomen area of the body for respiration and due to the principle of ballistocardiography (BCG) in heart signal sensing. In this research, the development of a theoretical model to qualitatively describe the piezo-resistive material is also presented in order to predict the relative change in the resistance of the piezo-resistive material due to the pressure applied.

The effect of pathophysiology on pharmacokinetics in the critically ill patient--concepts appraised by the example of antimicrobial agents.

PubMed

Blot, Stijn I; Pea, Federico; Lipman, Jeffrey

2014-11-20

Critically ill patients are at high risk for development of life-threatening infection leading to sepsis and multiple organ failure. Adequate antimicrobial therapy is pivotal for optimizing the chances of survival. However, efficient dosing is problematic because pathophysiological changes associated with critical illness impact on pharmacokinetics of mainly hydrophilic antimicrobials. Concentrations of hydrophilic antimicrobials may be increased because of decreased renal clearance due to acute kidney injury. Alternatively, antimicrobial concentrations may be decreased because of increased volume of distribution and augmented renal clearance provoked by systemic inflammatory response syndrome, capillary leak, decreased protein binding and administration of intravenous fluids and inotropes. Often multiple conditions that may influence pharmacokinetics are present at the same time thereby excessively complicating the prediction of adequate concentrations. In general, conditions leading to underdosing are predominant. Yet, since prediction of serum concentrations remains difficult, therapeutic drug monitoring for individual fine-tuning of antimicrobial therapy seems the way forward. Copyright © 2014. Published by Elsevier B.V.

Neural Correlates of the Lombard Effect in Primate Auditory Cortex

PubMed Central

Eliades, Steven J.

2012-01-01

Speaking is a sensory-motor process that involves constant self-monitoring to ensure accurate vocal production. Self-monitoring of vocal feedback allows rapid adjustment to correct perceived differences between intended and produced vocalizations. One important behavior in vocal feedback control is a compensatory increase in vocal intensity in response to noise masking during vocal production, commonly referred to as the Lombard effect. This behavior requires mechanisms for continuously monitoring auditory feedback during speaking. However, the underlying neural mechanisms are poorly understood. Here we show that when marmoset monkeys vocalize in the presence of masking noise that disrupts vocal feedback, the compensatory increase in vocal intensity is accompanied by a shift in auditory cortex activity toward neural response patterns seen during vocalizations under normal feedback condition. Furthermore, we show that neural activity in auditory cortex during a vocalization phrase predicts vocal intensity compensation in subsequent phrases. These observations demonstrate that the auditory cortex participates in self-monitoring during the Lombard effect, and may play a role in the compensation of noise masking during feedback-mediated vocal control. PMID:22855821

Are phonological influences on lexical (mis)selection the result of a monitoring bias?

PubMed Central

Ratinckx, Elie; Ferreira, Victor S.; Hartsuiker, Robert J.

2009-01-01

A monitoring bias account is often used to explain speech error patterns that seem to be the result of an interactive language production system, like phonological influences on lexical selection errors. A biased monitor is suggested to detect and covertly correct certain errors more often than others. For instance, this account predicts that errors which are phonologically similar to intended words are harder to detect than ones that are phonologically dissimilar. To test this, we tried to elicit phonological errors under the same conditions that show other kinds of lexical selection errors. In five experiments, we presented participants with high cloze probability sentence fragments followed by a picture that was either semantically related, a homophone of a semantically related word, or phonologically related to the (implicit) last word of the sentence. All experiments elicited semantic completions or homophones of semantic completions, but none elicited phonological completions. This finding is hard to reconcile with a monitoring bias account and is better explained with an interactive production system. Additionally, this finding constrains the amount of bottom-up information flow in interactive models. PMID:18942035

Estimation of groundwater flow from temperature monitoring in a borehole heat exchanger during a thermal response test

NASA Astrophysics Data System (ADS)

Yoshioka, Mayumi; Takakura, Shinichi; Uchida, Youhei

2018-05-01

To estimate the groundwater flow around a borehole heat exchanger (BHE), thermal properties of geological core samples were measured and a thermal response test (TRT) was performed in the Tsukuba upland, Japan. The thermal properties were measured at 57 points along a 50-m-long geological core, consisting predominantly of sand, silt, and clay, drilled near the BHE. In this TRT, the vertical temperature in the BHE was also monitored during and after the test. Results for the thermal properties of the core samples and from the monitoring indicated that groundwater flow enhanced thermal transfers, especially at shallow depths. The groundwater velocities around the BHE were estimated using a two-dimensional numerical model with monitoring data on temperature changes. According to the results, the estimated groundwater velocity was generally consistent with hydrogeological data from previous studies, except for the data collected at shallow depths consisting of a clay layer. The reasons for this discrepancy at shallow depths were predicted to be preferential flow and the occurrence of vertical flow through the BHE grout, induced by the hydrogeological conditions.

Corrosion monitoring using high-frequency guided waves

NASA Astrophysics Data System (ADS)

Fromme, P.

2016-04-01

Corrosion can develop due to adverse environmental conditions during the life cycle of a range of industrial structures, e.g., offshore oil platforms, ships, and desalination plants. Generalized corrosion leading to wall thickness loss can cause the reduction of the strength and thus degradation of the structural integrity. The monitoring of corrosion damage in difficult to access areas can be achieved using high frequency guided waves propagating along the structure from accessible areas. Using standard ultrasonic wedge transducers with single sided access to the structure, guided wave modes were selectively generated that penetrate through the complete thickness of the structure. The wave propagation and interference of the different guided wave modes depends on the thickness of the structure. Laboratory experiments were conducted for wall thickness reduction due to milling of the steel structure. From the measured signal changes due to the wave mode interference the reduced wall thickness was monitored. Good agreement with theoretical predictions was achieved. The high frequency guided waves have the potential for corrosion damage monitoring at critical and difficult to access locations from a stand-off distance.

Relation between Ocean SST Dipoles and Downwind Continental Croplands Assessed for Early Management Using Satellite-based Photosynthesis Models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2015-04-01

Crop-monitoring systems with the unit of carbon-dioxide sequestration for environmental issues related to climate adaptation to global warming have been improved using satellite-based photosynthesis and meteorological conditions. Early management of crop status is desirable for grain production, stockbreeding, and bio-energy providing that the seasonal climate forecasting is sufficiently accurate. Incorrect seasonal forecasting of crop production can damage global social activities if the recognized conditions are unsatisfied. One cause of poor forecasting related to the atmospheric dynamics at the Earth surface, which reflect the energy budget through land surface, especially the oceans and atmosphere. Recognition of the relation between SST anomalies (e.g. ENSO, Atlantic Niño, Indian dipoles, and Ningaloo Niño) and crop production, as expressed precisely by photosynthesis or the sequestrated-carbon rate, is necessary to elucidate the mechanisms related to poor production. Solar radiation, surface air temperature, and water stress all directly affect grain vegetation photosynthesis. All affect stomata opening, which is related to the water balance or definition by the ratio of the Penman potential evaporation and actual transpiration. Regarding stomata, present data and reanalysis data give overestimated values of stomata opening because they are extended from wet models in forests rather than semi-arid regions commonly associated with wheat, maize, and soybean. This study applies a complementary model based on energy conservation for semi-arid zones instead of the conventional Penman-Monteith method. Partitioning of the integrated Net PSN enables precise estimation of crop yields by modifying the semi-closed stomata opening. Partitioning predicts production more accurately using the cropland distribution already classified using satellite data. Seasonal crop forecasting should include near-real-time monitoring using satellite-based process crop models to avoid social difficulties that can derive from uncertain seasonal predictions produced from long-term forecasting. Acknowledgement The author appreciates scientific discussions held with the application team of seasonal prediction at the Japan Agency for Marine-Earth Science and Technology. Key words: crop production, monitoring, forecasting, SST anomaly, remote sensing

Road structural elements temperature trends diagnostics using sensory system of own design

NASA Astrophysics Data System (ADS)

Dudak, Juraj; Gaspar, Gabriel; Sedivy, Stefan; Pepucha, Lubomir; Florkova, Zuzana

2017-09-01

A considerable funds is spent for the roads maintenance in large areas during the winter. The road maintenance is significantly affected by the temperature change of the road structure. In remote locations may occur a situation, when it is not clear whether the sanding is actually needed because the lack of information on road conditions. In these cases, the actual road conditions are investigated by a personal inspection or by sending out a gritting vehicle. Here, however, is a risk of unnecessary trip the sanding vehicle. This situation is economically and environmentally unfavorable. The proposed system solves the problem of measuring the temperature profile of the road and the utilization of the predictive model to determine the future development trend of temperature. The system was technically designed as a set of sensors to monitor environmental values such as the temperature of the road, ambient temperature, relative air humidity, solar radiation and atmospheric pressure at the measuring point. An important part of the proposal is prediction model which based on the inputs from sensors and historical measurements can, with some probability, predict temperature trends at the measuring point. The proposed system addresses the economic and environmental aspects of winter road maintenance.

Prognostics for Microgrid Components

NASA Technical Reports Server (NTRS)

Saxena, Abhinav

2012-01-01

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Real-time monitoring of acoustic linear and nonlinear behavior of titanium alloys during low-cycle fatigue and high-cycle fatigue

NASA Astrophysics Data System (ADS)

Frouin, Jerome; Sathish, Shamachary; Na, Jeong K.

2000-05-01

An in-situ technique to measure sound velocity, ultrasonic attenuation and acoustic nonlinear property has been developed for characterization and early detection of fatigue damage in aerospace materials. For this purpose we have developed a computer software and measurement technique including hardware for the automation of the measurement. New transducer holder and special grips are designed. The automation has allowed us to test the long-term stability of the electronics over a period of time and so proof of the linearity of the system. Real-time monitoring of the material nonlinearity has been performed on dog-bone specimens from zero fatigue all the way to the final fracture under low-cycle fatigue test condition (LCF) and high-cycle test condition (HCF). Real-time health monitoring of the material can greatly contribute to the understanding of material behavior under cyclic loading. Interpretation of the results show that correlation exist between the slope of the curve described by the material nonlinearity and the life of the component. This new methodology was developed with an objective to predict the initiation of fatigue microcracks, and to detect, in-situ fatigue crack initiation as well as to quantify early stages of fatigue damage.

Monitoring Wildlife Interactions with Their Environment: An Interdisciplinary Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charles-Smith, Lauren E.; Domnguez, Ignacio X.; Fornaro, Robert J.

In a rapidly changing world, wildlife ecologists strive to correctly model and predict complex relationships between animals and their environment, which facilitates management decisions impacting public policy to conserve and protect delicate ecosystems. Recent advances in monitoring systems span scientific domains, including animal and weather monitoring devices and landscape classification mapping techniques. The current challenge is how to combine and use detailed output from various sources to address questions spanning multiple disciplines. WolfScout wildlife and weather tracking system is a software tool capable of filling this niche. WolfScout automates integration of the latest technological advances in wildlife GPS collars, weathermore » stations, drought conditions, and severe weather reports, and animal demographic information. The WolfScout database stores a variety of classified landscape maps including natural and manmade features. Additionally, WolfScout’s spatial database management system allows users to calculate distances between animals’ location and landscape characteristics, which are linked to the best approximation of environmental conditions at the animal’s location during the interaction. Through a secure website, data are exported in formats compatible with multiple software programs including R and ArcGIS. The WolfScout design promotes interoperability in data, between researchers, and software applications while standardizing analyses of animal interactions with their environment.« less

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

PubMed

Suh, Myung-Kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2012-09-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

PubMed Central

Suh, Myung-kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %. PMID:27617297

Biodestruction of strongly swelling polymer hydrogels and its effect on the water retention capacity of soils

NASA Astrophysics Data System (ADS)

Smagin, A. V.; Sadovnikova, N. B.; Smagina, M. V.

2014-06-01

The biodestruction of strongly swelling polymer hydrogels (water adsorbing soil conditioners of the new generation) has been studied at the quantitative level using original mathematical models. In laboratory experiments, a relationship between the hydrogel degradation rate and the temperature has been obtained, and the effect of the biodestruction on the water retention curve of soil compositions with hydrogels (used as an index of their water retention capacity) has been assessed. From the automatic monitoring data of the temperature regime of soils, the potential biodestruction of hydrogels has been predicted for different climatic conditions. The loss of hydrogels during three months of the vegetation period because of destruction can exceed 30% of their initial content in irrigated agriculture under arid climatic conditions and more than 10% under humid climatic conditions. Thus, the biodestruction of hydrogels is one of the most important factors decreasing their efficiency under actual soil conditions.

Lightweight ZERODUR: Validation of Mirror Performance and Mirror Modeling Predictions

NASA Technical Reports Server (NTRS)

Hull, Tony; Stahl, H. Philip; Westerhoff, Thomas; Valente, Martin; Brooks, Thomas; Eng, Ron

2017-01-01

Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA's XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2 m diameter, f/1.2988% lightweighted SCHOTT lightweighted ZERODUR(TradeMark) mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR(TradeMark). In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response(dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR(TradeMark) mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS). Summarize the outcome of NASA's XRCF tests and model validations

Lightweight ZERODUR®: Validation of mirror performance and mirror modeling predictions

NASA Astrophysics Data System (ADS)

Hull, Anthony B.; Stahl, H. Philip; Westerhoff, Thomas; Valente, Martin; Brooks, Thomas; Eng, Ron

2017-01-01

Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA’s XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2m diameter, f/1.29 88% lightweighted SCHOTT lightweighted ZERODUR® mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR®. In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response (dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR® mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS), and summarize the outcome of NASA’s XRCF tests and model validations.

Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2011-01-01

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R2) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.

Predicting Activity Energy Expenditure Using the Actical[R] Activity Monitor

ERIC Educational Resources Information Center

Heil, Daniel P.

2006-01-01

This study developed algorithms for predicting activity energy expenditure (AEE) in children (n = 24) and adults (n = 24) from the Actical[R] activity monitor. Each participant performed 10 activities (supine resting, three sitting, three house cleaning, and three locomotion) while wearing monitors on the ankle, hip, and wrist; AEE was computed…

Quantifying and modeling soil erosion and sediment export from construction sites in southern California

NASA Astrophysics Data System (ADS)

Wernet, A. K.; Beighley, R. E.

2006-12-01

Soil erosion is a power process that continuously alters the Earth's landscape. Human activities, such as construction and agricultural practices, and natural events, such as forest fires and landslides, disturb the landscape and intensify erosion processes leading to sudden increases in runoff sediment concentrations and degraded stream water quality. Understanding soil erosion and sediment transport processes is of great importance to researchers and practicing engineers, who routinely use models to predict soil erosion and sediment movement for varied land use and climate change scenarios. However, existing erosion models are limited in their applicability to constructions sites which have highly variable soil conditions (density, moisture, surface roughness, and best management practices) that change often in both space and time. The goal of this research is to improve the understanding, predictive capabilities and integration of treatment methodologies for controlling soil erosion and sediment export from construction sites. This research combines modeling with field monitoring and laboratory experiments to quantify: (a) spatial and temporal distribution of soil conditions on construction sites, (b) soil erosion due to event rainfall, and (c) potential offsite discharge of sediment with and without treatment practices. Field sites in southern California were selected to monitor the effects of common construction activities (ex., cut/fill, grading, foundations, roads) on soil conditions and sediment discharge. Laboratory experiments were performed in the Soil Erosion Research Laboratory (SERL), part of the Civil and Environmental Engineering department at San Diego State University, to quantify the impact of individual factors leading to sediment export. SERL experiments utilize a 3-m by 10-m tilting soil bed with soil depths up to 1 m, slopes ranging from 0 to 50 percent, and rainfall rates up to 150 mm/hr (6 in/hr). Preliminary modeling, field and laboratory results are presented.

Heavy rainfall and waterborne disease outbreaks: the Walkerton example.

PubMed

Auld, Heather; MacIver, D; Klaassen, J

Recent research indicates that excessive rainfall has been a significant contributor to historical waterborne disease outbreaks. The Meteorological Service of Canada, Environment Canada, provided an analysis and testimony to the Walkerton Inquiry on the excessive rainfall events, including an assessment of the historical significance and expected return periods of the rainfall amounts. While the onset of the majority of the Walkerton, Ontario, Escherichia coli O157:H7 and Campylobacter outbreak occurred several days after a heavy rainfall on May 12, the accumulated 5-d rainfall amounts from 8-12 May were particularly significant. These 5-d accumulations could, on average, only be expected once every 60 yr or more in Walkerton and once every 100 yr or so in the heaviest rainfall area to the south of Walkerton. The significant link between excess rainfall and waterborne disease outbreaks, in conjunction with other multiple risk factors, indicates that meteorological and climatological conditions need to be considered by water managers, public health officials, and private citizens as a significant risk factor for water contamination. A system to identify and project the impacts of such challenging or extreme weather conditions on water supply systems could be developed using a combination of weather/climate monitoring information and weather prediction or quantitative precipitation forecast information. The use of weather monitoring and forecast information or a "wellhead alert system" could alert water system and water supply managers on the potential response of their systems to challenging weather conditions and additional requirements to protect health. Similar approaches have recently been used by beach managers in parts of the United States to predict day-to-day water quality for beach advisories.

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

PubMed Central

Zheng, Yan-Ling; Zhang, Li-Ping; Zhang, Xue-Liang; Wang, Kai; Zheng, Yu-Jian

2015-01-01

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China. PMID:25760345

[Arterial pressure curve and fluid status].

PubMed

Pestel, G; Fukui, K

2009-04-01

Fluid optimization is a major contributor to improved outcome in patients. Unfortunately, anesthesiologists are often in doubt whether an additional fluid bolus will improve the hemodynamics of the patient or not as excess fluid may even jeopardize the condition. This article discusses physiological concepts of liberal versus restrictive fluid management followed by a discussion on the respective capabilities of various monitors to predict fluid responsiveness. The parameter difference in pulse pressure (dPP), derived from heart-lung interaction in mechanically ventilated patients is discussed in detail. The dPP cutoff value of 13% to predict fluid responsiveness is presented together with several assessment techniques of dPP. Finally, confounding variables on dPP measurements, such as ventilation parameters, pneumoperitoneum and use of norepinephrine are also mentioned.

a Study of the Interferences with the On-Line Radioiodine Measurement Under Nuclear Accident Conditions

NASA Astrophysics Data System (ADS)

Tseng, Tung-Tse

In this research the interferences with the on -line detection of radioiodines, under nuclear accident conditions, were studied. The special tool employed for this research is the developed on-line radioiodine monitor (the Penn State Radioiodine Monitor), which is capable of detecting low levels of radioiodine on-line in air containing orders of magnitude higher levels of radioactive noble gases. Most of the data reported in this thesis were collected during a series of experiments called "Source -Term Experiment Program (STEP)." The experiments were conducted at the Argonne National Laboratory's TREAT reactor located at the Idaho National Engineering Laboratory (INEL). In these tests, fission products were released from the Light Water Reactor (LWR) test fuels as a result of simulating a reactor accident. The Penn State Monitor was then used to sample the fission products accumulated in a large container which simulated the reactor containment building. The test results proved that the Penn State Monitor was not affected significantly by the passage of large amounts of noble gases through the system. Also, it confirmed the predicted results that the operation of conventional on-line radioiodine detectors would, under nuclear accident conditions, be seriously impaired by the passage of high concentrations of radioactive noble gases through such systems. This work also demonstrated that under conditions of high noble gas concentrations and low radioiodine concentrations, the formation of noble-gas-decayed alkali metals can seriously interfere with the on-line detection of radioiodine, especially during the 24 hours immediately after the accident. The decayed alkali metal particulates were also found to be much more penetrating than the ordinary type of particulates, since a large fraction (15%) of the particulates were found to penetrate through the commonly used High Efficiency Particulate Air (HEPA) filter (rated >99.97% for 0.3 (mu)m particulate). Also, a significant fraction ((TURN)40%) of these particles became deposited on silver zeolite iodine filters inside the counting chamber. Finally, the Penn State Monitor proved itself to be a powerful research tool for the on-line source term studies since it can easily produce near noble-gas-free spectra during the real time studies occurring under simulated nuclear accident conditions.

Simulation and Prediction of Groundwater Pollution from Planned Feed Additive Project in Nanning City Based on GMS Model

NASA Astrophysics Data System (ADS)

Liang, Yimin; Lan, Junkang; Wen, Zhixiong

2018-01-01

In order to predict the pollution of underground aquifers and rivers by the proposed project, Specialized hydrogeological investigation was carried out. After hydrogeological surveying and mapping, drilling, and groundwater level monitoring, the scope of the hydrogeological unit and the regional hydrogeological condition were found out. The permeability coefficients of the aquifers were also obtained by borehole water injection tests. In order to predict the impact on groundwater environment by the project, a GMS software was used in numerical simulation. The simulation results show that when unexpected sewage leakage accident happened, the pollutants will be gradually diluted by groundwater, and the diluted contaminants will slowly spread to southeast with groundwater flow, eventually they are discharged into Gantang River. However, the process of the pollutants discharging into the river is very long, the long-term dilution of the river water will keep Gantang River from being polluted.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Synergistic Effects of Frequency and Temperature on Damage Evolution and Life Prediction of Cross-Ply Ceramic Matrix Composites under Tension-Tension Fatigue Loading

NASA Astrophysics Data System (ADS)

Longbiao, Li

2017-10-01

In this paper, the synergistic effects of loading frequency and testing temperature on the fatigue damage evolution and life prediction of cross-ply SiC/MAS ceramic-matrix composite have been investigated. The damage parameters of the fatigue hysteresis modulus, fatigue hysteresis dissipated energy and the interface shear stress were used to monitor the damage evolution inside of SiC/MAS composite. The evolution of fatigue hysteresis dissipated energy, the interface shear stress and broken fibers fraction versus cycle number, and the fatigue life S-N curves of SiC/MAS composite under the loading frequency of 1 and 10 Hz at 566 °C and 1093 °C in air condition have been predicted. The synergistic effects of the loading frequency and testing temperature on the degradation rate of fatigue hysteresis dissipated energy and the interface shear stress have been analyzed.

Role of Groundwater Monitoring for Closure of Underground Nuclear Tests on the Nevada Test Site

NASA Astrophysics Data System (ADS)

Chapman, J. B.; Pohlmann, K.; Pohll, G.; Russell, C.

2009-12-01

Over 800 underground nuclear tests were conducted at the Nevada Test Site in a variety of hydrogeologic environments. As of the 1996 Environmental Impact Statement for the site, more than 100 million curies of radioactivity remained in the subsurface from these tests, much of it near or below the water table. The U.S. Department of Energy Environmental Management program is implementing a closure strategy for these sites that anticipates closure-in-place, natural attenuation, and institutional controls. Groundwater monitoring is a key component of this strategy, but its role is significantly evolved from that of a detection- or compliance-based monitoring concept. Indeed, monitoring is part of the integrated closure process itself, not an activity confined to a static post-closure period. The reasons for this evolution derive from the complex hydrogeologic conditions, the long time-frames of concern, and recognition that a significant degree of uncertainty is irreducible. The hundreds of test locations are grouped into Corrective Action Units that measure over 100 km2 in area and extend to depths in excess of 1000 m. Despite concerted data collection efforts, the technical basis for closure of these large regions relies heavily on complex numerical models of flow and transport. The inherent uncertainties in these models present challenges for reaching regulatory acceptance of closure, and challenges for confidently locating monitoring wells. The solution now being pursued for the NTS is to integrate model evaluation and monitoring. In addition to standard goals of contaminant detection and protection of human health, an explicit monitoring objective is to increase confidence in model results by assessing the reliability of model forecasts. The initial monitoring network is expected to eventually transition to a long-term closure design, with less emphasis on confidence-building as uncertainty in forecasts is reduced. The methodology for this iterative process of monitoring and model refinement will incorporate expert-judgment and Bayesian updating of model input parameters to provide a cost-beneficial monitoring network that is expected to reduce model prediction uncertainty. This approach to monitoring for these large and complex contaminant areas is consistent with the underlying reliance on model predictions and will ensure that water quality samples are collected in a manner and location that is consistent with the current understanding of contaminant flowpaths.

To the National Map and beyond

USGS Publications Warehouse

Kelmelis, J.

2003-01-01

Scientific understanding, technology, and social, economic, and environmental conditions have driven a rapidly changing demand for geographic information, both digital and analog. For more than a decade, the U.S. Geological Survey (USGS) has been developing innovative partnerships with other government agencies and private industry to produce and distribute geographic information efficiently; increase activities in remote sensing to ensure ongoing monitoring of the land surface; and develop new understanding of the causes and consequences of land surface change. These activities are now contributing to a more robust set of geographic information called The National Map (TNM). The National Map is designed to provide an up-to-date, seamless, horizontally and vertically integrated set of basic digital geographic data, a frequent monitoring of changes on the land surface, and an understanding of the condition of the Earth's surface and many of the processes that shape it. The USGS has reorganized its National Mapping Program into three programs to address the continuum of scientific activities-describing (mapping), monitoring, understanding, modeling, and predicting. The Cooperative Topographic Mapping Program focuses primarily on the mapping and revision aspects of TNM. The National Map also includes results from the Land Remote Sensing and Geographic Analysis and Monitoring Programs that provide continual updates, new insights, and analytical tools. The National Map is valuable as a framework for current research, management, and operational activities. It also provides a critical framework for the development of distributed, spatially enabled decision support systems.

Refinement of detecting atrial fibrillation in stroke patients: results from the TRACK-AF Study.

PubMed

Reinke, F; Bettin, M; Ross, L S; Kochhäuser, S; Kleffner, I; Ritter, M; Minnerup, J; Dechering, D; Eckardt, L; Dittrich, R

2018-04-01

Detection of occult atrial fibrillation (AF) is crucial for optimal secondary prevention in stroke patients. The AF detection rate was determined by implantable cardiac monitor (ICM) and compared to the prediction rate of the probability of incident AF by software based analysis of a continuously monitored electrocardiogram at follow-up (stroke risk analysis, SRA); an optimized AF detection algorithm is proposed by combining both tools. In a monocentric prospective study 105 out of 389 patients with cryptogenic stroke despite extensive diagnostic workup were investigated with two additional cardiac monitoring tools: (a) 20 months' monitoring by ICM and (b) SRA during hospitalization at the stroke unit. The detection rate of occult AF was 18% by ICM (n = 19) (range 6-575 days) and 62% (n = 65) had an increased risk for AF predicted by SRA. When comparing the predictive accuracy of SRA to ICM, the sensitivity was 95%, specificity 35%, positive predictive value 27% and negative predictive value 96%. In 18 patients with AF detected by ICM, SRA also showed a medium risk for AF. Only one patient with a very low risk predicted by SRA developed AF revealed by ICM after 417 days. A combination of SRA and ICM is a promising strategy to detect occult AF. SRA is reliable in predicting incident AF with a high negative predictive value. Thus, SRA may serve as a cost-effective pre-selection tool identifying patients at risk for AF who may benefit from further cardiac monitoring by ICM. © 2017 EAN.

Cognitive, sensory and physical factors enabling driving safety in older adults.

PubMed

Anstey, Kaarin J; Wood, Joanne; Lord, Stephen; Walker, Janine G

2005-01-01

We reviewed literature on cognitive, sensory, motor and physical factors associated with safe driving and crash risk in older adults with the goal of developing a model of factors enabling safe driving behaviour. Thirteen empirical studies reporting associations between cognitive, sensory, motor and physical factors and either self-reported crashes, state crash records or on-road driving measures were identified. Measures of attention, reaction time, memory, executive function, mental status, visual function, and physical function variables were associated with driving outcome measures. Self-monitoring was also identified as a factor that may moderate observed effects by influencing driving behavior. We propose that three enabling factors (cognition, sensory function and physical function/medical conditions) predict driving ability, but that accurate self-monitoring of these enabling factors is required for safe driving behaviour.

Biomarkers for pediatric sepsis and septic shock

PubMed Central

Standage, Stephen W; Wong, Hector R

2011-01-01

Sepsis is a clinical syndrome defined by physiologic changes indicative of systemic inflammation, which are likely attributable to documented or suspected infection. Septic shock is the progression of those physiologic changes to the extent that delivery of oxygen and metabolic substrate to tissues is compromised. Biomarkers have the potential to diagnose, monitor, stratify and predict outcome in these syndromes. C-reactive protein is elevated in inflammatory and infectious conditions and has long been used as a biomarker indicating infection. Procalcitonin has more recently been shown to better distinguish infection from inflammation. Newer candidate biomarkers for infection include IL-18 and CD64. Lactate facilitates the diagnosis of septic shock and the monitoring of its progression. Multiple stratification biomarkers based on genome-wide expression profiling are under active investigation and present exciting future possibilities. PMID:21171879

Continuous Metabolic Monitoring Based on Multi-Analyte Biomarkers to Predict Exhaustion

PubMed Central

Kastellorizios, Michail; Burgess, Diane J.

2015-01-01

This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject’s perception. PMID:26028477

Continuous metabolic monitoring based on multi-analyte biomarkers to predict exhaustion.

PubMed

Kastellorizios, Michail; Burgess, Diane J

2015-06-01

This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject's perception.

Individual differences in episodic memory abilities predict successful prospective memory output monitoring.

PubMed

Hunter Ball, B; Pitães, Margarida; Brewer, Gene A

2018-02-07

Output monitoring refers to memory for one's previously completed actions. In the context of prospective memory (PM) (e.g., remembering to take medication), failures of output monitoring can result in repetitions and omissions of planned actions (e.g., over- or under-medication). To be successful in output monitoring paradigms, participants must flexibly control attention to detect PM cues as well as engage controlled retrieval of previous actions whenever a particular cue is encountered. The current study examined individual differences in output monitoring abilities in a group of younger adults differing in attention control (AC) and episodic memory (EM) abilities. The results showed that AC ability uniquely predicted successful cue detection on the first presentation, whereas EM ability uniquely predicted successful output monitoring on the second presentation. The current study highlights the importance of examining external correlates of PM abilities and contributes to the growing body of research on individual differences in PM.

Non-destructive monitoring of viability in an ex vivo organ culture model of osteochondral tissue.

PubMed

Elson, K M; Fox, N; Tipper, J L; Kirkham, J; Hall, R M; Fisher, J; Ingham, E

2015-06-30

Organ culture is an increasingly important tool in research, with advantages over monolayer cell culture due to the inherent natural environment of tissues. Successful organ cultures must retain cell viability. The aim of this study was to produce viable and non-viable osteochondral organ cultures, to assess the accumulation of soluble markers in the conditioned medium for predicting tissue viability. Porcine femoral osteochondral plugs were cultured for 20 days, with the addition of Triton X-100 on day 6 (to induce necrosis), camptothecin (to induce apoptosis) or no toxic additives. Tissue viability was assessed by the tissue destructive XTT (2,3-bis[2-methoxy-4-nitro-5-sulfophenyl]-2H-tetrazolium-5-carboxyanilide tetrazolium salt) assay method and LIVE/DEAD® staining of the cartilage at days 0, 6 and 20. Tissue structure was assessed by histological evaluation using haematoxylin & eosin and safranin O. Conditioned medium was assessed every 3-4 days for glucose depletion, and levels of lactate dehydrogenase (LDH), alkaline phosphatase (AP), glycosaminoglycans (GAGs), and matrix metalloproteinase (MMP)-2 and MMP-9. Necrotic cultures immediately showed a reduction in glucose consumption, and an immediate increase in LDH, GAG, MMP-2 and MMP-9 levels. Apoptotic cultures showed a delayed reduction in glucose consumption and delayed increase in LDH, a small rise in MMP-2 and MMP-9, but no significant effect on GAGs released into the conditioned medium. The data showed that tissue viability could be monitored by assessing the conditioned medium for the aforementioned markers, negating the need for tissue destructive assays. Physiologically relevant whole- or part-joint organ culture models, necessary for research and pre-clinical assessment of therapies, could be monitored this way, reducing the need to sacrifice tissues to determine viability, and hence reducing the sample numbers necessary.

Monitoring apparatus and method for battery power supply

DOEpatents

Martin, Harry L.; Goodson, Raymond E.

1983-01-01

A monitoring apparatus and method are disclosed for monitoring and/or indicating energy that a battery power source has then remaining and/or can deliver for utilization purposes as, for example, to an electric vehicle. A battery mathematical model forms the basis for monitoring with a capacity prediction determined from measurement of the discharge current rate and stored battery parameters. The predicted capacity is used to provide a state-of-charge indication. Self-calibration over the life of the battery power supply is enacted through use of a feedback voltage based upon the difference between predicted and measured voltages to correct the battery mathematical model. Through use of a microprocessor with central information storage of temperature, current and voltage, system behavior is monitored, and system flexibility is enhanced.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

Propulsion health monitoring of a turbine engine disk using spin test data

NASA Astrophysics Data System (ADS)

Abdul-Aziz, Ali; Woike, Mark; Oza, Nikunj; Matthews, Bryan; Baakilini, George

2010-03-01

On line detection techniques to monitor the health of rotating engine components are becoming increasingly attractive options to aircraft engine companies in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenging feature to easily implement, especially, in the presence of scattered loading conditions, crack size, component geometry and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini cracks before any catastrophic event occurs. These techniques go further to evaluate materials' discontinuities and other anomalies that have grown to the level of critical defects which can lead to failure. Generally, health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost and durable products. Therefore, in an effort to address the technical issues concerning health monitoring of a rotor disk, this paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating engine-like-disk-to predict the disk faults and assess its structural integrity. The experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center's Rotordynamics Laboratory will be evaluated using multiple data-driven anomaly detection techniques to identify anomalies in the disk. This study is expected to present a select evaluation of online health monitoring of a rotating disk using these high caliber sensors and test the capability of the in-house spin system.

Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees

NASA Astrophysics Data System (ADS)

Sayegh, Arwa; Tate, James E.; Ropkins, Karl

2016-02-01

Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx, traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 °C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper highlights the importance of either setting up local background continuous monitors or improving the quality and resolution of modelled UK background maps and the need to further investigate the influence of ambient air temperature on NOx emissions and roadside NOx concentrations.

Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

ERIC Educational Resources Information Center

Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

2011-01-01

In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

Individual Differences in Fifth Graders' Literacy and Academic Language Predict Comprehension Monitoring Development: An Eye-Movement Study

ERIC Educational Resources Information Center

Connor, Carol McDonald; Radach, Ralph; Vorstius, Christian; Day, Stephanie L.; McLean, Leigh; Morrison, Frederick J.

2015-01-01

In this study, we investigated fifth graders' (n = 52) fall literacy, academic language, and motivation and how these skills predicted fall and spring comprehension monitoring on an eye movement task. Comprehension monitoring was defined as the identification and repair of misunderstandings when reading text. In the eye movement task, children…

Predicting the solubility of gases in Nitrile Butadiene Rubber in extreme conditions using molecular simulation

NASA Astrophysics Data System (ADS)

Khawaja, Musab; Molinari, Nicola; Sutton, Adrian; Mostofi, Arash

In the oil and gas industry, elastomer seals play an important role in protecting sensitive monitoring equipment from contamination by gases - a problem that is exacerbated by the high pressures and temperatures found down-hole. The ability to predict and prevent such permeative failure has proved elusive to-date. Nitrile butadiene rubber (NBR) is a common choice of elastomer for seals due to its resistance to heat and fuels. In the conditions found in the well it readily absorbs small molecular weight gases. How this behaviour changes quantitatively for different gases as a function of temperature and pressure is not well-understood. In this work a series of fully atomistic simulations are performed to understand the effect of extreme conditions on gas solubility in NBR. Widom particle insertion is used to compute solubilities. The importance of sampling and allowing structural relaxation upon compression are highlighted, and qualitatively reasonable trends reproduced. Finally, while at STP it has previously been shown that the solubility of CO2 is higher than that of He in NBR, we observe that under the right circumstances it is possible to reverse this trend.

Application of the International Water Association activated sludge models to describe aerobic sludge digestion.

PubMed

Ghorbani, M; Eskicioglu, C

2011-12-01

Batch and semi-continuous flow aerobic digesters were used to stabilize thickened waste-activated sludge at different initial conditions and mean solids retention times. Under dynamic conditions, total suspended solids, volatile suspended solids (VSS) and total and particulate chemical oxygen demand (COD and PCOD) were monitored in the batch reactors and effluent from the semi-continuous flow reactors. Activated Sludge Model (ASM) no. 1 and ASM no. 3 were applied to measured data (calibration data set) to evaluate the consistency and performances of models at different flow regimes for digester COD and VSS modelling. The results indicated that both ASM1 and ASM3 predicted digester COD, VSS and PCOD concentrations well (R2, Ra2 > or = 0.93). Parameter estimation concluded that compared to ASM1, ASM3 parameters were more consistent across different batch and semi-continuous flow runs with different operating conditions. Model validation on a data set independent from the calibration data successfully predicted digester COD (R2 = 0.88) and VSS (R2 = 0.94) concentrations by ASM3, while ASM1 overestimated both reactor COD (R2 = 0.74) and VSS concentrations (R2 = 0.79) after 15 days of aerobic batch digestion.

Simulating air temperature in an urban street canyon in all weather conditions using measured data at a reference meteorological station

NASA Astrophysics Data System (ADS)

Erell, E.; Williamson, T.

2006-10-01

A model is proposed that adapts data from a standard meteorological station to provide realistic site-specific air temperature in a city street exposed to the same meso-scale environment. In addition to a rudimentary description of the two sites, the canyon air temperature (CAT) model requires only inputs measured at standard weather stations; yet it is capable of accurately predicting the evolution of air temperature in all weather conditions for extended periods. It simulates the effect of urban geometry on radiant exchange; the effect of moisture availability on latent heat flux; energy stored in the ground and in building surfaces; air flow in the street based on wind above roof height; and the sensible heat flux from individual surfaces and from the street canyon as a whole. The CAT model has been tested on field data measured in a monitoring program carried out in Adelaide, Australia, in 2000-2001. After calibrating the model, predicted air temperature correlated well with measured data in all weather conditions over extended periods. The experimental validation provides additional evidence in support of a number of parameterisation schemes incorporated in the model to account for sensible heat and storage flux.

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring.

PubMed

Kim, Sun-Young; Olives, Casey; Sheppard, Lianne; Sampson, Paul D; Larson, Timothy V; Keller, Joshua P; Kaufman, Joel D

2017-01-01

Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children's Health Study (CHS), and the Inhalable Particulate Network (IPN). In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84-0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00-0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. Citation: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38-46; http://dx.doi.org/10.1289/EHP131.

Characterization of iron carbonate scales developed under carbon dioxide corrosion conditions

NASA Astrophysics Data System (ADS)

de Moraes, Flavio Dias

1999-11-01

Carbon steel CO2 corrosion is a common and very serious problem in the oil industry. It often results in severe damage to pipes and equipment. Besides controlling direct costs associated with loss of production and replacement or repair to the equipment damaged by corrosion, life and environmental safety must be protected with the thorough study of this type of corrosion. For a given type of steel, the CO2 corrosion rates are strongly influenced by many mechanical and environmental factors, such as flow velocity, temperature, gas-liquid ratio, oil-water ratio, CO2 partial pressure, and the chemical composition of the produced water. Under specific conditions, a corrosion product, the iron carbonate (FeCO3), can deposit over the corroding metal as a scale and dramatically reduce the CO2 corrosion rates on carbon steels. The ability to reliably predict the protective characteristics of such scales so that this knowledge may be used to mitigate the CO2 corrosion problem is the main objective of this research. CO2 corrosion tests performed under various CO2 corrosion flowing conditions in a flow loop were used to generate and study FeCO3 scales. In situ Electrochemical Impedance Spectroscopy (EIS) techniques were successfully used to monitor the development of the scales throughout the duration of the tests. The EIS monitoring enabled the identification of the type of scales being formed and the quantification of the protection they give. A procedure using EIS, SEM and X-ray diffraction was developed to electrochemically and morphologically characterize the scales formed. In this work, morphology of the scales was proved to be the most important characteristic related to CO2 corrosion protection, and temperature was found to be the main environmental parameter controlling the morphology of the scales. For the environmental conditions tested, a correlation was developed to predict the type of iron carbonate scales that would be formed and the amount of CO2 corrosion protection these scales would provide to carbon steels.

Integrating local urban climate modelling and mobile sensor data for personal exposure assessments in the context of urban heat island effect

NASA Astrophysics Data System (ADS)

Ueberham, Maximilian; Hertel, Daniel; Schlink, Uwe

2017-04-01

Deeper knowledge about urban climate conditions is getting more important in the context of climate change, urban population growth, urban compaction and continued surface sealing. Especially the urban heat island effect (UHI) is one of the most significant human induced alterations of Earth's surface climate. According to this the appearance frequency of heat waves in cities will increase with deep impacts on personal thermal comfort, human health and local residential quality of citizens. UHI can be very heterogenic within a city and research needs to focus more on the neighborhood scale perspective to get further insights about the heat burden of individuals. However, up to now, few is known about local thermal environmental variances and personal exposure loads. To monitor these processes and the impact on individuals, improved monitoring approaches are crucial, complementing data recorded at conventional fixed stations. Therefore we emphasize the importance of micro-meteorological modelling and mobile measurements to shed new light on the nexus of urban human-climate interactions. Contributing to this research we jointly present the approaches of our two PhD-projects. Firstly we illustrate on the basis of an example site, how local thermal conditions in an urban district can be simulated and predicted by a micro-meteorological model. Secondly we highlight the potentials of personal exposure measurements based on an evaluation of mobile micro-sensing devices (MSDs) and analyze and explain differences between model predictions and mobile records. For the examination of local thermal conditions we calculated ENVI-met simulations within the "Bayerischer Bahnhof" quarter in Leipzig (Saxony, Germany; 51°20', 12°22'). To accomplish the maximum temperature contrasts within the diverse built-up structures we chose a hot summer day (25 Aug 2016) under autochthonous weather conditions. From these simulations we analyzed a UHI effect between the model core (urban area) and the surrounding nesting area (rural area). Preparing for the outdoor application of mobile MSDs we tested their accuracy and performance between several MSDs and reliable sophisticated devices under laboratory conditions. We found that variations mainly depend on the device design and technology (e.g. active/passive ventilation). The standard deviation of the temperature records was quite stable over the whole range of values and the MSDs proved to be applicable for the purpose of our study. In conclusion the benefit of integrating mobile data and micrometeorological predictions is manifold. Mobile data can be used for the investigation of personal exposure in the context of heat stress and for the verification and training of micrometeorological models. Otherwise, model predictions can identify local areas of special climate interest where additional mobile measurements would be beneficial to provide new information for mitigation and adaptation actions.

Monitoring California's forage resource using ERTS-1 and supporting aircraft data

NASA Technical Reports Server (NTRS)

Carneggie, D. M.; Degloria, S. D.

1973-01-01

NASA's Earth Resource Technology Satellite (ERTS-1) launched July 23, 1972, offers for the first time the unique capabilities for regional monitoring of forage plant conditions. The repetitive coverage every 18 days, the synoptic view and the real-time recovery of the imagery for immediate analysis, combine to make the ERTS satellite a valuable tool for improving the evaluation of our rangeland resources. Studies presently underway at the University of California, Berkeley (sponsored jointly by NASA and the Bureau of Land Management), seek to determine if imagery obtained from high altitude aircraft and spacecraft (ERTS) can provide: (1) a means for monitoring the growth and development of annual and perennial range plants in California, and for determining the time and the rate of initial plant growth (germination) and terminal plant growth (maturation and senescence); (2) a means for determining or predicting the relative amount of forage that is produced; and (3) a means for mapping rangeland areas having different forage producing capabilities.

Prediction of contaminant persistence in aqueous phase: a quantum chemical approach.

PubMed

Blotevogel, Jens; Mayeno, Arthur N; Sale, Tom C; Borch, Thomas

2011-03-15

At contaminated field sites where active remediation measures are not feasible, monitored natural attenuation is sometimes the only alternative for surface water or groundwater decontamination. However, due to slow degradation rates of some contaminants under natural conditions, attenuation processes and their performance assessment can take several years to decades to complete. Here, we apply quantum chemical calculations to predict contaminant persistence in the aqueous phase. For the test compound hexamethylphosphoramide (HMPA), P-N bond hydrolysis is the only thermodynamically favorable reaction that may lead to its degradation under reducing conditions. Through calculation of aqueous Gibbs free energies of activation for all potential reaction mechanisms, it is predicted that HMPA hydrolyzes via an acid-catalyzed mechanism at pH < 8.2, and an uncatalyzed mechanism at pH 8.2-8.5. The estimated half-lives of thousands to hundreds of thousands of years over the groundwater-typical pH range of 6.0 to 8.5 indicate that HMPA will be persistent in the absence of suitable oxidants. At pH 0, where the hydrolysis reaction is rapid enough to enable measurement, the experimentally determined rate constant and half-life are in excellent agreement with the predicted values. Since the quantum chemical methodology described herein can be applied to virtually any contaminant or reaction of interest, it is especially valuable for the prediction of persistence when slow reaction rates impede experimental investigations and appropriate QSARs are unavailable.

Separation of lymphocytes by electrophoresis under terrestrial conditions and at zero gravity

NASA Technical Reports Server (NTRS)

Rubin, A. L.

1977-01-01

Electrophoretic mobility (EPM) of human peripheral lymphocytes were examined with the following objectives: To determine differences in EPM of lymphocytes under immuno-stimulated and immuno-suppressed states. To define the conditions necessary for the separation of lymphocyte sub-populations in normal and pathological conditions; To investigate immunological active, charged chemical groups on lymphocyte surfaces; and to investigate pathophysiological mechanisms of immune responsiveness, as reflected by alterations in EPM. To evaluate the potential of lymphocyte electrophoresis as: (1) a means of monitoring the immune status of kidney transplant recipients, (2) in predicting the outcome of kidney transplants, and (3) as a method for separation of lymphocyte sub-populations, the EPM was studied for unfractionated human peripheral lymphocytes and of populations enriched with T and "B" cells from normal adults, hemodialysis patients and kidney transplant recipients.

FRAMEWORK FOR STRUCTURAL ONLINE HEALTH MONITORING OF AGING AND DEGRADATION OF SECONDARY PIPING SYSTEMS DUE TO SOME ASPECTS OF EROSION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gribok, Andrei V.; Agarwal, Vivek

This paper describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary components in nuclear power plants (NPPs). The paper also proposes a framework for online health monitoring of aging and degradation of secondary components. The framework consists of an integrated multi-sensor modality system, which can be used to monitor different piping configurations under different degradation conditions. The report analyses the currently known degradation mechanisms and available predictive models. Based on this analysis, the structural health monitoring framework is proposed. The Light Water Reactor Sustainability Program began to evaluate technologies thatmore » could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, in 2017, companies that operate the national nuclear energy fleet started the Delivering the Nuclear Promise Initiative, which is a 3 year program aimed at maintaining operational focus, increasing value, and improving efficiency. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems (for example, in raw water piping or diesel piping) would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at answering this challenge by combining long range guided wave technologies with other monitoring techniques, which can significantly increase the inspection length and pinpoint the locations that degraded the most. More widely, the report suggests research efforts aimed at developing, validating, and deploying online corrosion monitoring techniques for complex geometries, which are pervasive in NPPs.« less

Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

PubMed

Keim-Malpass, Jessica; Kitzmiller, Rebecca R; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew T; Tai, Robert; Calland, James Forrest; Sullivan, Kevin; Randall Moorman, J; Anderson, Ruth A

2018-06-01

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS. Copyright © 2018 Elsevier Inc. All rights reserved.

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Teng, William; Chiu, Long; Doraiswamy, Paul; Kempler, Steven; Liu, Zhong; Pham, Long; Rui, Hualan

2005-01-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

Development of a Tool Condition Monitoring System for Impregnated Diamond Bits in Rock Drilling Applications

NASA Astrophysics Data System (ADS)

Perez, Santiago; Karakus, Murat; Pellet, Frederic

2017-05-01

The great success and widespread use of impregnated diamond (ID) bits are due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting at the cutting face as the bit wears out. It is therefore important to keep this mechanism acting throughout the lifespan of the bit. Nonetheless, such a mechanism can be altered by the blunting of the bit that ultimately leads to a less than optimal drilling performance. For this reason, this paper aims at investigating the applicability of artificial intelligence-based techniques in order to monitor tool condition of ID bits, i.e. sharp or blunt, under laboratory conditions. Accordingly, topologically invariant tests are carried out with sharp and blunt bits conditions while recording acoustic emissions (AE) and measuring-while-drilling variables. The combined output of acoustic emission root-mean-square value (AErms), depth of cut ( d), torque (tob) and weight-on-bit (wob) is then utilized to create two approaches in order to predict the wear state condition of the bits. One approach is based on the combination of the aforementioned variables and another on the specific energy of drilling. The two different approaches are assessed for classification performance with various pattern recognition algorithms, such as simple trees, support vector machines, k-nearest neighbour, boosted trees and artificial neural networks. In general, Acceptable pattern recognition rates were obtained, although the subset composed by AErms and tob excels due to the high classification performances rates and fewer input variables.

Stimulus onset predictability modulates proactive action control in a Go/No-go task

PubMed Central

Berchicci, Marika; Lucci, Giuliana; Spinelli, Donatella; Di Russo, Francesco

2015-01-01

The aim of the study was to evaluate whether the presence/absence of visual cues specifying the onset of an upcoming, action-related stimulus modulates pre-stimulus brain activity, associated with the proactive control of goal-directed actions. To this aim we asked 12 subjects to perform an equal probability Go/No-go task with four stimulus configurations in two conditions: (1) uncued, i.e., without any external information about the timing of stimulus onset; and (2) cued, i.e., with external visual cues providing precise information about the timing of stimulus onset. During task both behavioral performance and event-related potentials (ERPs) were recorded. Behavioral results showed faster response times in the cued than uncued condition, confirming existing literature. ERPs showed novel results in the proactive control stage, that started about 1 s before the motor response. We observed a slow rising prefrontal positive activity, more pronounced in the cued than the uncued condition. Further, also pre-stimulus activity of premotor areas was larger in cued than uncued condition. In the post-stimulus period, the P3 amplitude was enhanced when the time of stimulus onset was externally driven, confirming that external cueing enhances processing of stimulus evaluation and response monitoring. Our results suggest that different pre-stimulus processing come into play in the two conditions. We hypothesize that the large prefrontal and premotor activities recorded with external visual cues index the monitoring of the external stimuli in order to finely regulate the action. PMID:25964751

Probabilistic drought intensification forecasts using temporal patterns of satellite-derived drought indicators

NASA Astrophysics Data System (ADS)

Park, Sumin; Im, Jungho; Park, Seonyeong

2016-04-01

A drought occurs when the condition of below-average precipitation in a region continues, resulting in prolonged water deficiency. A drought can last for weeks, months or even years, so can have a great influence on various ecosystems including human society. In order to effectively reduce agricultural and economic damage caused by droughts, drought monitoring and forecasts are crucial. Drought forecast research is typically conducted using in situ observations (or derived indices such as Standardized Precipitation Index (SPI)) and physical models. Recently, satellite remote sensing has been used for short term drought forecasts in combination with physical models. In this research, drought intensification was predicted using satellite-derived drought indices such as Normalized Difference Drought Index (NDDI), Normalized Multi-band Drought Index (NMDI), and Scaled Drought Condition Index (SDCI) generated from Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) products over the Korean Peninsula. Time series of each drought index at the 8 day interval was investigated to identify drought intensification patterns. Drought condition at the previous time step (i.e., 8 days before) and change in drought conditions between two previous time steps (e.g., between 16 days and 8 days before the time step to forecast) Results show that among three drought indices, SDCI provided the best performance to predict drought intensification compared to NDDI and NMDI through qualitative assessment. When quantitatively compared with SPI, SDCI showed a potential to be used for forecasting short term drought intensification. Finally this research provided a SDCI-based equation to predict short term drought intensification optimized over the Korean Peninsula.

Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using Labview

NASA Astrophysics Data System (ADS)

Kong, Changduk; Kim, Keonwoo

2011-12-01

Recently, an aero gas turbine health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which occurs in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the online condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measured engine performance data and base performance results calculated by base engine performance model. This work aims to develop a GUI type on-line condition monitoring program for the PT6A-67 turboprop engine of a high altitude and long endurance operation UAV using LabVIEW. The base engine performance of the on-line condition monitoring program is simulated using component maps inversely generated from the limited performance deck data provided by engine manufacturer. The base engine performance simulation program is evaluated because analysis results by this program agree well with the performance deck data. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between clean engine performance results calculated by the base performance simulation program and measured engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed online monitoring system. For user friendly purpose, all monitoring program are coded by LabVIEW, and monitoring examples are demonstrated using the proposed GUI type on-condition monitoring program.

MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study

PubMed Central

Honkoop, Persijn J; Simpson, Andrew; Bonini, Matteo; Snoeck-Stroband, Jiska B; Meah, Sally; Fan Chung, Kian; Usmani, Omar S; Fowler, Stephen; Sont, Jacob K

2017-01-01

Introduction Asthma is a variable lung condition whereby patients experience periods of controlled and uncontrolled asthma symptoms. Patients who experience prolonged periods of uncontrolled asthma have a higher incidence of exacerbations and increased morbidity and mortality rates. The ability to determine and to predict levels of asthma control and the occurrence of exacerbations is crucial in asthma management. Therefore, we aimed to determine to what extent physiological, behavioural and environmental data, obtained by mobile healthcare (mHealth) and home-monitoring sensors, as well as patient characteristics, can be used to predict episodes of uncontrolled asthma and the onset of asthma exacerbations. Methods and analysis In an 1-year observational study, patients will be provided with mHealth and home-monitoring systems to record daily measurements for the first-month (phase I) and weekly measurements during a follow-up period of 11 months (phase II). Our study population consists of 150 patients, aged ≥18 years, with a clinician's diagnosis of asthma, currently on controller medication, with uncontrolled asthma and/or minimally one exacerbation in the past 12 months. They will be enrolled over three participating centres, including Leiden, London and Manchester. Our main outcomes are the association between physiological, behavioural and environmental data and (1) the loss of asthma control and (2) the occurrence of asthma exacerbations. Ethics This study was approved by the Medical Ethics Committee of the Leiden University Medical Center in the Netherlands and by the NHS ethics service in the UK. Trial registration number NCT02774772. PMID:28119390

[Usefulness of the examination of fetal blood oxygen saturation (FSpO2) and fetal heart rate (FHR) as a prognostic factor of the newborn outcome].

PubMed

Skoczylas, Michał; Laudański, Tadeusz

2003-10-01

Cardiotocography has become the standard for fetal monitoring in labor. False-positive findings during electronic fetal heart rate monitoring may were not associated with neonatal acidemia. Because of the poor specificity of fetal heart rate monitoring in predicting fetal distress, new methods are being investigated as a way to improve the accuracy of assessing the infant's condition during labor. The aim of this study was to determinate the efficiency of fetal blood oxygen saturation (FSpO2) and computer analysis of the fetal heart rate (Co-CTG) in the late 1-st stage of labor as a prognostic factor of newborn acidemia. Total 62 subjects were studied. During labors and deliveries fetal oxygen saturation was continuously recorded, with use of Nellecor N-400 fetal pulse oximeter and continous CTG were performed by Hewlett Packard 50A. Transdermal fetal oxygen saturation measurements and CTG results obtained during the labors was analyzed using MONAKO system (ITAM Zabrze). The results were compared with the values of pH and base deficit in the umbilical artery measured just after delivery. The sensitivity, specificity, negative, positive predictive values and Youden factor based on FHR and FSpO2, for prognosis of neonatal acidosis were: 65%, 80%, 16%, 97.5% 60% and 0.135 respectively FHR; and 100%, 60%, 100%, 96.8% and 0.968 respectively FSpO2. 1. The examination of fetal blood oxygen saturation in the labor is a useful prognostic factor of the newborn outcome. 2. The best predictive value for intrapartum fetal asphyxia with metabolic acidosis was found when fetal pulse oximetry is added to cardiotocography.

MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study.

PubMed

Honkoop, Persijn J; Simpson, Andrew; Bonini, Matteo; Snoeck-Stroband, Jiska B; Meah, Sally; Fan Chung, Kian; Usmani, Omar S; Fowler, Stephen; Sont, Jacob K

2017-01-24

Asthma is a variable lung condition whereby patients experience periods of controlled and uncontrolled asthma symptoms. Patients who experience prolonged periods of uncontrolled asthma have a higher incidence of exacerbations and increased morbidity and mortality rates. The ability to determine and to predict levels of asthma control and the occurrence of exacerbations is crucial in asthma management. Therefore, we aimed to determine to what extent physiological, behavioural and environmental data, obtained by mobile healthcare (mHealth) and home-monitoring sensors, as well as patient characteristics, can be used to predict episodes of uncontrolled asthma and the onset of asthma exacerbations. In an 1-year observational study, patients will be provided with mHealth and home-monitoring systems to record daily measurements for the first-month (phase I) and weekly measurements during a follow-up period of 11 months (phase II). Our study population consists of 150 patients, aged ≥18 years, with a clinician's diagnosis of asthma, currently on controller medication, with uncontrolled asthma and/or minimally one exacerbation in the past 12 months. They will be enrolled over three participating centres, including Leiden, London and Manchester. Our main outcomes are the association between physiological, behavioural and environmental data and (1) the loss of asthma control and (2) the occurrence of asthma exacerbations. This study was approved by the Medical Ethics Committee of the Leiden University Medical Center in the Netherlands and by the NHS ethics service in the UK. NCT02774772. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Clinical outcome of continuous facial nerve monitoring during primary parotidectomy.

PubMed

Terrell, J E; Kileny, P R; Yian, C; Esclamado, R M; Bradford, C R; Pillsbury, M S; Wolf, G T

1997-10-01

To assess whether continuous facial nerve monitoring during parotidectomy is associated with a lower incidence of facial nerve paresis or paralysis compared with parotidectomy without monitoring and to assess the cost of such monitoring. A retrospective analysis of outcomes for patients who underwent parotidectomy with or without continuous facial nerve monitoring. University medical center. Fifty-six patients undergoing parotidectomy in whom continuous electromyographic monitoring was used and 61 patients in whom it was not used. (1) The incidence of early and persistent facial nerve paresis or paralysis and (2) the cost associated with facial nerve monitoring. Early, unintentional facial weakness was significantly lower in the group monitored by electromyograpy (43.6%) than in the unmonitored group (62.3%) (P=.04). In the subgroup of patients without comorbid conditions or surgeries, early weakness in the monitored group (33.3%) remained statistically lower than the rate of early weakness in the unmonitored group (57.5%) (P=.03). There was no statistical difference in the final facial nerve function or incidence of permanent nerve injury between the groups or subgroups. After multivariate analysis, nonmonitored status (odds ratio [OR], 3.22), advancing age (OR, 1.47 per 10 years), and longer operative times (OR, 1.3 per hour) were the only significant independent predictive variables significantly associated with early postoperative facial weakness. The incremental cost of facial nerve monitoring was $379. The results suggest that continuous electromyographic monitoring of facial muscle during primary parotidectomy reduces the incidence of short-term postoperative facial paresis. Advantages and disadvantages of this technique need to be considered together with the additional costs in deciding whether routine use of continuous monitoring is a useful, cost-effective adjunct to parotid surgery.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Identifying the behavioural characteristics of clay cliffs using intensive monitoring and geotechnical numerical modelling

NASA Astrophysics Data System (ADS)

Quinn, J. D.; Rosser, N. J.; Murphy, W.; Lawrence, J. A.

2010-08-01

Coastal monitoring is routinely undertaken to provide an archival record of cliff-line movement that can be used in the development and validation of predictive coast retreat and evolution models. However, coastal monitoring is often purely quantitative in nature, and financial necessity requires deployment over extensive coastal sections. As a result, for local site conditions in particular, only limited geomorphological data are available or included during the development of such predictive models. This has resulted in many current models incorporating a simplistic or generalised representation of cliff behaviour, an approach that progressively loses local credibility when deployed over extensive heterogeneous coastlines. This study addresses this situation at a site of extreme coastline retreat, Holderness, UK, through the application of intensive monitoring of six representative cliff sections nested within a general geomorphological appraisal of the wider coastline as a whole. The data from these surveys have been used to validate a finite difference-based geotechnical modelling assessment of clay cliff stability. Once validated, the geotechnical model was used to simulate a range of scenarios that were sufficient to represent the range of topographic, hydrogeological, geological, and littoral conditions exhibited throughout the region. Our assessment identified that the cliff retreat occurs through the combined influence of direct marine erosion of the cliff, with shallow, structurally controlled failures or substantial mass failures. Critically, the predisposition to any one of these failure mechanisms arises principally as a result of initial cliff height. The results of the numerical modelling have been combined into an empirical slope model that derives the rate of landslide-induced retreat that would arise from mass failures under various future scenarios. Results of this study can be used in the selection and development of retreat models at coastlines of similar physiographic setting to that found at Holderness. The results represent a key step in linking material deformation properties to the processes of cliff change and the subsequent range of landforms found on clay cliffs. As such, the results could also be used more generally to illustrate the likely cliff behaviour of other soft rock coastlines.

Discrimination in measures of knowledge monitoring accuracy

PubMed Central

Was, Christopher A.

2014-01-01

Knowledge monitoring predicts academic outcomes in many contexts. However, measures of knowledge monitoring accuracy are often incomplete. In the current study, a measure of students’ ability to discriminate known from unknown information as a component of knowledge monitoring was considered. Undergraduate students’ knowledge monitoring accuracy was assessed and used to predict final exam scores in a specific course. It was found that gamma, a measure commonly used as the measure of knowledge monitoring accuracy, accounted for a small, but significant amount of variance in academic performance whereas the discrimination and bias indexes combined to account for a greater amount of variance in academic performance. PMID:25339979

Self-Monitoring Using Continuous Glucose Monitors with Real-Time Feedback Improves Exercise Adherence in Individuals with Impaired Blood Glucose: A Pilot Study.

PubMed

Bailey, Kaitlyn J; Little, Jonathan P; Jung, Mary E

2016-03-01

Exercise helps individuals with prediabetes or type 2 diabetes (T2D) manage their blood glucose (BG); however, exercise adherence in this population is dismal. In this pilot study we tested the efficacy of a self-monitoring group-based intervention using continuous glucose monitors (CGMs) at increasing exercise adherence in individuals with impaired BG. Thirteen participants with prediabetes or T2D were randomized to an 8-week standard care exercise program (CON condition) (n = 7) or self-monitoring exercise intervention (SM condition) (n = 6). Participants in the SM condition were taught how to self-monitor their exercise and BG, to goal set, and to use CGM to observe how exercise influences BG. We hypothesized that compared with the CON condition, using a real-time CGM would facilitate self-monitoring behavior, resulting in increased exercise adherence. Repeated-measures analysis of variance revealed significant Condition × Time interactions for self-monitoring (P < 0.01), goal setting (P = 0.01), and self-efficacy to self-monitor (P = 0.01), such that the SM condition showed greater increases in these outcomes immediately after the program and at the 1-month follow-up compared with the CON condition. The SM condition had higher program attendance rates (P = 0.03), and a greater proportion of participants reregistered for additional exercise programs (P = 0.048) compared with the CON condition. Participants in both conditions experienced improvements in health-related quality of life, waist circumference, and fitness (P values <0.05). These findings provide promising initial support for the use of a real-time CGM to foster self-monitoring and exercise behavior in individuals living with prediabetes or T2D.

Predictive Monitoring for Improved Management of Glucose Levels

PubMed Central

Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth

2007-01-01

Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110

Control of voice fundamental frequency in speaking versus singing

NASA Astrophysics Data System (ADS)

Natke, Ulrich; Donath, Thomas M.; Kalveram, Karl Th.

2003-03-01

In order to investigate control of voice fundamental frequency (F0) in speaking and singing, 24 adults had to utter the nonsense word ['ta:tatas] repeatedly, while in selected trials their auditory feedback was frequency-shifted by 100 cents downwards. In the speaking condition the target speech rate and prosodic pattern were indicated by a rhythmic sequence made of white noise. In the singing condition the sequence consisted of piano notes, and subjects were instructed to match the pitch of the notes. In both conditions a response in voice F0 begins with a latency of about 150 ms. As predicted, response magnitude is greater in the singing condition (66 cents) than in the speaking condition (47 cents). Furthermore the singing condition seems to prolong the after-effect which is a continuation of the response in trials after the frequency shift. In the singing condition, response magnitude and the ability to match the target F0 correlate significantly. Results support the view that in speaking voice F0 is monitored mainly supra-segmentally and controlled less tightly than in singing.

Control of voice fundamental frequency in speaking versus singing.

PubMed

Natke, Ulrich; Donath, Thomas M; Kalveram, Karl Th

2003-03-01

In order to investigate control of voice fundamental frequency (F0) in speaking and singing, 24 adults had to utter the nonsense word ['ta:tatas] repeatedly, while in selected trials their auditory feedback was frequency-shifted by 100 cents downwards. In the speaking condition the target speech rate and prosodic pattern were indicated by a rhythmic sequence made of white noise. In the singing condition the sequence consisted of piano notes, and subjects were instructed to match the pitch of the notes. In both conditions a response in voice F0 begins with a latency of about 150 ms. As predicted, response magnitude is greater in the singing condition (66 cents) than in the speaking condition (47 cents). Furthermore the singing condition seems to prolong the after-effect which is a continuation of the response in trials after the frequency shift. In the singing condition, response magnitude and the ability to match the target F0 correlate significantly. Results support the view that in speaking voice F0 is monitored mainly supra-segmentally and controlled less tightly than in singing.

On the use of temperature for online condition monitoring of geared systems - A review

NASA Astrophysics Data System (ADS)

Touret, T.; Changenet, C.; Ville, F.; Lalmi, M.; Becquerelle, S.

2018-02-01

Gear unit condition monitoring is a key factor for mechanical system reliability management. When they are subjected to failure, gears and bearings may generate excessive vibration, debris and heat. Vibratory, acoustic or debris analyses are proven approaches to perform condition monitoring. An alternative to those methods is to use temperature as a condition indicator to detect gearbox failure. The review focuses on condition monitoring studies which use this thermal approach. According to the failure type and the measurement method, it exists a distinction whether it is contact (e.g. thermocouple) or non-contact temperature sensor (e.g. thermography). Capabilities and limitations of this approach are discussed. It is shown that the use of temperature for condition monitoring has a clear potential as an alternative to vibratory or acoustic health monitoring.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

NASA Astrophysics Data System (ADS)

Tesoriero, Anthony J.; Gronberg, Jo Ann; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-08-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

USGS Publications Warehouse

Tesoriero, Anthony J.; Gronberg, Jo Ann M.; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-01-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Neural evidence for predictive coding in auditory cortex during speech production.

PubMed

Okada, Kayoko; Matchin, William; Hickok, Gregory

2018-02-01

Recent models of speech production suggest that motor commands generate forward predictions of the auditory consequences of those commands, that these forward predications can be used to monitor and correct speech output, and that this system is hierarchically organized (Hickok, Houde, & Rong, Neuron, 69(3), 407--422, 2011; Pickering & Garrod, Behavior and Brain Sciences, 36(4), 329--347, 2013). Recent psycholinguistic research has shown that internally generated speech (i.e., imagined speech) produces different types of errors than does overt speech (Oppenheim & Dell, Cognition, 106(1), 528--537, 2008; Oppenheim & Dell, Memory & Cognition, 38(8), 1147-1160, 2010). These studies suggest that articulated speech might involve predictive coding at additional levels than imagined speech. The current fMRI experiment investigates neural evidence of predictive coding in speech production. Twenty-four participants from UC Irvine were recruited for the study. Participants were scanned while they were visually presented with a sequence of words that they reproduced in sync with a visual metronome. On each trial, they were cued to either silently articulate the sequence or to imagine the sequence without overt articulation. As expected, silent articulation and imagined speech both engaged a left hemisphere network previously implicated in speech production. A contrast of silent articulation with imagined speech revealed greater activation for articulated speech in inferior frontal cortex, premotor cortex and the insula in the left hemisphere, consistent with greater articulatory load. Although both conditions were silent, this contrast also produced significantly greater activation in auditory cortex in dorsal superior temporal gyrus in both hemispheres. We suggest that these activations reflect forward predictions arising from additional levels of the perceptual/motor hierarchy that are involved in monitoring the intended speech output.

Using remote sensing to monitor past changes and assess future scenarios for the Sacramento-San Joaquin River Delta waterways, California USA

NASA Astrophysics Data System (ADS)

Santos, Maria J.; Hestir, Erin; Khanna, Shruti; Ustin, Susan L.

2017-04-01

Historically, deltas have been extensively affected both by natural processes and human intervention. Thus, understanding drivers, predicting impacts and optimizing solutions to delta problems requires a holistic approach spanning many sectors, disciplines and fields of expertise. Deltas are ideal model systems to understand the effects of the interaction between social and ecological domains, as they face unprecedented disturbances and threats to their biological and ecological sustainability. The challenge for deltas is to meet the goals of supporting biodiversity and ecosystem processes while also provisioning fresh water resources for human use. We provide an overview of the last 150 years of the Sacramento-San Joaquin River delta, where we illustrate the parallel process of an increase in disturbances, by particularly zooming in on the current cascading effects of invasive species on geophysical and biological processes. Using remote sensing data coupled with in situ measurements of water quality, turbidity, and species presence we show how the spread and persistence of aquatic invasive species affects sedimentation processes and ecosystem functioning. Our results show that the interactions between the biological and physical conditions in the Delta affect the trajectory of dominance by native and invasive aquatic plant species. Trends in growth and community characteristics associated with predicted impacts of climate change (sea level rise, warmer temperatures, changes in the hydrograph with high winter and low summer outflows) do not provide simple predictions. Individually, the impact of specific environmental changes on the biological components can be predicted, however it is the complex interactions of biological communities with the suite of physical changes that make predictions uncertain. Systematic monitoring is critical to provide the data needed to document and understand change of these delta systems, and to identify successful adaptation strategies.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models.

PubMed

Xu, Wei; Riley, Erin A; Austin, Elena; Sasakura, Miyoko; Schaal, Lanae; Gould, Timothy R; Hartin, Kris; Simpson, Christopher D; Sampson, Paul D; Yost, Michael G; Larson, Timothy V; Xiu, Guangli; Vedal, Sverre

2017-03-01

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO X ) and ozone (O 3 ) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO X and O 3 , with LOOCV R 2 s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO X had LOOCV R 2 s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O 3 . Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO X and O 3 and are a better source of data for these models than 2-week passive badge data.

In-process, non-destructive multimodal dynamic testing of high-speed composite rotors

NASA Astrophysics Data System (ADS)

Kuschmierz, Robert; Filippatos, Angelos; Langkamp, Albert; Hufenbach, Werner; Czarske, Jürgern W.; Fischer, Andreas

2014-03-01

Fibre reinforced plastic (FRP) rotors are lightweight and offer great perspectives in high-speed applications such as turbo machinery. Currently, novel rotor structures and materials are investigated for the purpose of increasing machine efficiency, lifetime and loading limits. Due to complex rotor structures, high anisotropy and non-linear behavior of FRP under dynamic loads, an in-process measurement system is necessary to monitor and to investigate the evolution of damages under real operation conditions. A non-invasive, optical laser Doppler distance sensor measurement system is applied to determine the biaxial deformation of a bladed FRP rotor with micron uncertainty as well as the tangential blade vibrations at surface speeds above 300 m/s. The laser Doppler distance sensor is applicable under vacuum conditions. Measurements at varying loading conditions are used to determine elastic and plastic deformations. Furthermore they allow to determine hysteresis, fatigue, Eigenfrequency shifts and loading limits. The deformation measurements show a highly anisotropic and nonlinear behavior and offer a deeper understanding of the damage evolution in FRP rotors. The experimental results are used to validate and to calibrate a simulation model of the deformation. The simulation combines finite element analysis and a damage mechanics model. The combination of simulation and measurement system enables the monitoring and prediction of damage evolutions of FRP rotors in process.

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

Tobon-Mejia, D. A.; Medjaher, K.; Zerhouni, N.

2012-04-01

The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a computer numerical control (CNC) tool machine and the estimation of its remaining useful life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

[Monitoring of occupational activities under the risk of heat stress: use of mathematical models in the prediction of physiological parameters].

PubMed

Terzi, R; Catenacci, G; Marcaletti, G

1985-01-01

Some authors proposed mathematical models that, starting from standardized conditions of environmental microclimate parameters, thermal impedance of the clothing, and energetic expenditure allowed the forecast of the body temperature and heart rate variations in respect to the basal values in subjects standing in the same environment. In the present work we verify the usefulness of these models applied to the working tasks characterized by standardized job made under unfavourable thermal conditions. In subject working in an electric power station the values of the body temperature and heart rate are registered and compared with the values obtained by the application of the studied models. The results are discussed in view of the practical use.

Real time video analysis to monitor neonatal medical condition

NASA Astrophysics Data System (ADS)

Shirvaikar, Mukul; Paydarfar, David; Indic, Premananda

2017-05-01

One in eight live births in the United States is premature and these infants have complications leading to life threatening events such as apnea (pauses in breathing), bradycardia (slowness of heart) and hypoxia (oxygen desaturation). Infant movement pattern has been hypothesized as an important predictive marker for these life threatening events. Thus estimation of movement along with behavioral states, as a precursor of life threatening events, can be useful for risk stratification of infants as well as for effective management of disease state. However, more important and challenging is the determination of the behavioral state of the infant. This information includes important cues such as sleep position and the status of the eyes, which are important markers for neonatal neurodevelopment state. This paper explores the feasibility of using real time video analysis to monitor the condition of premature infants. The image of the infant can be segmented into regions to localize and focus on specific areas of interest. Analysis of the segmented regions can be performed to identify different parts of the body including the face, arms, legs and torso. This is necessary due to real-time processing speed considerations. Such a monitoring system would be of great benefit as an aide to medical staff in neonatal hospital settings requiring constant surveillance. Any such system would have to satisfy extremely stringent reliability and accuracy requirements, before it can be deployed in a hospital care unit, due to obvious reasons. The effect of lighting conditions and interference will have to be mitigated to achieve such performance.

Remote Assessment of Forest Ecosystem Stress (RAFES): Development of a Real Time Decision Support Tool for the Eastern U.S

NASA Astrophysics Data System (ADS)

Clinton, B.; Vose, J.; Novick, K.; Liu, Y.

2011-12-01

Drier and warmer conditions predicted with climate change models are likely to significantly impact forest ecosystems over the next several decades. The U.S. has experienced significant droughts over the past several years that have increased the susceptibility of forests to insect outbreaks, disease, and wildfire. Weather data collected with traditional approaches provide an indirect measure of drought or temperature stress; however, the significance of short-term or prolonged climate-related stress varies considerably across the landscape as topography, elevations, edaphic condition and antecedent conditions vary. This limits the capacity of land managers to anticipate and initiate management activities that could offset the impacts of climate-related forest stress. Decision support tools are needed that allow fine scale monitoring of stress conditions in forest ecosystems in real time to help land managers evaluate response strategies. To assist land managers in managing the impacts of climate change, we are developing a stress monitoring and decision support system across multiple sites in the eastern U.S. that (1) provides remote data capture of environmental parameters that quantify climate-related forest stress, (2) links remotely captured data with physiologically-based indices of tree water stress, and (3) provides a PC-based analytical tool for land managers to monitor and assess the severity of climate-related stress. Currently the network represents southern coastal plain pine plantation, Atlantic coastal flatwoods mixed pine-hardwood, southern piedmont upland mixed pine-hardwood, southern Appalachian dry ridge and mesic riparian, southern Arkansas managed mature pine, and northern Minnesota mature aspen. The strategy for selecting additional sites for the network will be a focus on at-risk ecosystems deemed particularly vulnerable to the affects of predicted climate change such as those in ecotonal transition regions, or those at the fringes of their ranges. The sensor arrays at each site detect water and temperature stress variables and transmit those data to a field office. Sensors include air and soil temperature, relative humidity, fuel moisture and temperature, xylem sap flux density, soil moisture and matric potential, precipitation, and solar radiation. Data are transmitted in real-time to the NOAA Geostationary Operational Environmental Satellite (GOES). A PC-based software program that downloads monitoring data from the GOES satellite, analyzes the data, and provides the land manager with an assessment of climate-related stress conditions and potential forest health threat levels in real time is under development. Data collection began in early 2010 on most sites, and we have at least one year of data from all nine sites within the network. We are currently comparing estimates of stress levels on our sites with estimates of stress from common drought indices. For this presentation, we are comparing and contrasting four sites representing an environmental gradient within the network.

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

PubMed

Finan, Daniel A; Doyle, Francis J; Palerm, Cesar C; Bevier, Wendy C; Zisser, Howard C; Jovanovic, Lois; Seborg, Dale E

2009-09-01

A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose-insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both "normal" data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell. 2009 Diabetes Technology Society.

Groundwater/surface-water interactions in the Bad River Watershed, Wisconsin

USGS Publications Warehouse

Leaf, Andrew T.; Fienen, Michael N.; Hunt, Randall J.; Buchwald, Cheryl A.

2015-11-23

Finally, a new data-worth analysis of potential new monitoring-well locations was performed by using the model. The relative worth of new measurements was evaluated based on their ability to increase confidence in model predictions of groundwater levels and base flows at 35 locations, under the condition of a proposed open-pit iron mine. Results of the new data-worth analysis, and other inputs and outputs from the Bad River model, are available through an online dynamic web mapping service at (http://wim.usgs.gov/badriver/).

Contact thermal shock test of ceramics

NASA Technical Reports Server (NTRS)

Rogers, W. P.; Emery, A. F.

1992-01-01

A novel quantitative thermal shock test of ceramics is described. The technique employs contact between a metal-cooling rod and hot disk-shaped specimen. In contrast with traditional techniques, the well-defined thermal boundary condition allows for accurate analyses of heat transfer, stress, and fracture. Uniform equibiaxial tensile stresses are induced in the center of the test specimen. Transient specimen temperature and acoustic emission are monitored continuously during the thermal stress cycle. The technique is demonstrated with soda-lime glass specimens. Experimental results are compared with theoretical predictions based on a finite-element method thermal stress analysis combined with a statistical model of fracture. Material strength parameters are determined using concentric ring flexure tests. Good agreement is found between experimental results and theoretical predictions of failure probability as a function of time and initial specimen temperature.

Research on joint parameter inversion for an integrated underground displacement 3D measuring sensor.

PubMed

Shentu, Nanying; Qiu, Guohua; Li, Qing; Tong, Renyuan; Shentu, Nankai; Wang, Yanjie

2015-04-13

Underground displacement monitoring is a key means to monitor and evaluate geological disasters and geotechnical projects. There exist few practical instruments able to monitor subsurface horizontal and vertical displacements simultaneously due to monitoring invisibility and complexity. A novel underground displacement 3D measuring sensor had been proposed in our previous studies, and great efforts have been taken in the basic theoretical research of underground displacement sensing and measuring characteristics by virtue of modeling, simulation and experiments. This paper presents an innovative underground displacement joint inversion method by mixing a specific forward modeling approach with an approximate optimization inversion procedure. It can realize a joint inversion of underground horizontal displacement and vertical displacement for the proposed 3D sensor. Comparative studies have been conducted between the measured and inversed parameters of underground horizontal and vertical displacements under a variety of experimental and inverse conditions. The results showed that when experimentally measured horizontal displacements and vertical displacements are both varied within 0~30 mm, horizontal displacement and vertical displacement inversion discrepancies are generally less than 3 mm and 1 mm, respectively, under three kinds of simulated underground displacement monitoring circumstances. This implies that our proposed underground displacement joint inversion method is robust and efficient to predict the measuring values of underground horizontal and vertical displacements for the proposed sensor.

Model-based monitoring of stormwater runoff quality.

PubMed

Birch, Heidi; Vezzaro, Luca; Mikkelsen, Peter Steen

2013-01-01

Monitoring of micropollutants (MP) in stormwater is essential to evaluate the impacts of stormwater on the receiving aquatic environment. The aim of this study was to investigate how different strategies for monitoring of stormwater quality (combining a model with field sampling) affect the information obtained about MP discharged from the monitored system. A dynamic stormwater quality model was calibrated using MP data collected by automatic volume-proportional sampling and passive sampling in a storm drainage system on the outskirts of Copenhagen (Denmark) and a 10-year rain series was used to find annual average (AA) and maximum event mean concentrations. Use of this model reduced the uncertainty of predicted AA concentrations compared to a simple stochastic method based solely on data. The predicted AA concentration, obtained by using passive sampler measurements (1 month installation) for calibration of the model, resulted in the same predicted level but with narrower model prediction bounds than by using volume-proportional samples for calibration. This shows that passive sampling allows for a better exploitation of the resources allocated for stormwater quality monitoring.

The Reciprocal Influence of Callous-Unemotional Traits, Oppositional Defiant Disorder and Parenting Practices in Preschoolers.

PubMed

Brown, Caitlin A; Granero, Roser; Ezpeleta, Lourdes

2017-04-01

The present study investigates reciprocal associations between positive parenting, parental monitoring, CU traits, and ODD in children assessed at age 3 and again at age 6. Data were collected from a sample of preschoolers (N = 419; 51.58 % female) through diagnostic interviews and questionnaires answered by parents and teachers. Structural equation modeling revealed a bidirectional relationship between poor monitoring and ODD, with poor monitoring at age 3 predicting ODD at age 6 (β = 0.11, p < 0.05), and ODD at age 3 predicting poor monitoring at age 6 (β = 0.10, p < 0.05). While poor monitoring at age 3 predicted CU traits at age 6 (β = 0.11, p < 0.05), CU traits at age 3 predicted positive parenting (β = 0.09, p < 0.05) and ODD (β = 0.13, p < 0.05) at age 6. Results have important implications for early targeted parenting interventions for CU traits and ODD.

Technology review: prototyping platforms for monitoring ambient conditions.

PubMed

Afolaranmi, Samuel Olaiya; Ramis Ferrer, Borja; Martinez Lastra, Jose Luis

2018-05-08

The monitoring of ambient conditions in indoor spaces is very essential owing to the amount of time spent indoors. Specifically, the monitoring of air quality is significant because contaminated air affects the health, comfort and productivity of occupants. This research work presents a technology review of prototyping platforms for monitoring ambient conditions in indoor spaces. It involves the research on sensors (for CO 2 , air quality and ambient conditions), IoT platforms, and novel and commercial prototyping platforms. The ultimate objective of this review is to enable the easy identification, selection and utilisation of the technologies best suited for monitoring ambient conditions in indoor spaces. Following the review, it is recommended to use metal oxide sensors, optical sensors and electrochemical sensors for IAQ monitoring (including NDIR sensors for CO 2 monitoring), Raspberry Pi for data processing, ZigBee and Wi-Fi for data communication, and ThingSpeak IoT platform for data storage, analysis and visualisation.

Longitudinal Relations Among Parental Monitoring Strategies, Knowledge, and Adolescent Delinquency in a Racially Diverse At-Risk Sample.

PubMed

Bendezú, Jason J; Pinderhughes, Ellen E; Hurley, Sean M; McMahon, Robert J; Racz, Sarah J

2016-04-04

Parents raising youth in high-risk communities at times rely on active, involved monitoring strategies in order to increase both knowledge about youth activities and the likelihood that adolescents will abstain from problem behavior. Key monitoring literature suggests that some of these active monitoring strategies predict increases in adolescent problem behavior rather than protect against it. However, this literature has studied racially homogenous, low-risk samples, raising questions about generalizability. With a diverse sample of youth (N = 753; 58% male; 46% Black) and families living in high-risk neighborhoods, bidirectional longitudinal relations were examined among three aspects of monitoring (parental discussions of daily activities, parental curfew rules, and adolescent communication with parents), parental knowledge, and youth delinquency. Parental discussion of daily activities was the strongest predictor of parental knowledge, which negatively predicted delinquency. However, these aspects of monitoring did not predict later delinquency. Findings were consistent across gender and race/urbanicity. Results highlight the importance of active and involved parental monitoring strategies in contexts where they are most needed.

Preliminary results of the FVS gypsy moth event monitor using remeasurement plot data from Northern West Virginia

Treesearch

Matthew P. Perkowski; John R. Brooks; Kurt W. Gottschalk

2008-01-01

Predictions based on the Gypsy Moth Event Monitor were compared to remeasurement plot data from stands receiving gypsy moth defoliation. These stands were part of a silvicultural treatment study located in northern West Virginia that included a sanitation thinning, a presalvage thinning and paired no-treatment controls. In all cases the event monitor under predicted...

Applications systems verification and transfer project. Volume 2: Operational applications of satellite snow-cover observations and data-collection systems in the Arizona test site

NASA Technical Reports Server (NTRS)

Schumann, H. H.

1981-01-01

Ground surveys and aerial observations were used to monitor rapidly changing moisture conditions in the Salt-Verde watershed. Repetitive satellite snow cover observations greatly reduce the necessity for routine aerial snow reconnaissance flights over the mountains. High resolution, multispectral imagery provided by LANDSAT satellite series enabled rapid and accurate mapping of snow-cover distributions for small- to medium-sized subwatersheds; however, the imagery provided only one observation every 9 days of about a third of the watershed. Low resolution imagery acquired by the ITOSa dn SMS/GOES meteorological satellite series provides the daily synoptic observation necessary to monitor the rapid changes in snow-covered area in the entire watershed. Short term runoff volumes can be predicted from daily sequential snow cover observations.

Analytical method for optimal source reduction with monitored natural attenuation in contaminated aquifers

USGS Publications Warehouse

Widdowson, M.A.; Chapelle, F.H.; Brauner, J.S.; ,

2003-01-01

A method is developed for optimizing monitored natural attenuation (MNA) and the reduction in the aqueous source zone concentration (??C) required to meet a site-specific regulatory target concentration. The mathematical model consists of two one-dimensional equations of mass balance for the aqueous phase contaminant, to coincide with up to two distinct zones of transformation, and appropriate boundary and intermediate conditions. The solution is written in terms of zone-dependent Peclet and Damko??hler numbers. The model is illustrated at a chlorinated solvent site where MNA was implemented following source treatment using in-situ chemical oxidation. The results demonstrate that by not taking into account a variable natural attenuation capacity (NAC), a lower target ??C is predicted, resulting in unnecessary source concentration reduction and cost with little benefit to achieving site-specific remediation goals.

Remote Sensing the Patterns of Vector-borne Disease in El Nino and non-El Nino Years

NASA Technical Reports Server (NTRS)

Wood, B. L.; Chang, J.; Lobitz, B.; Beck, L.; DAntoni, Hector (Technical Monitor)

1997-01-01

The relationship between El Nino and non-El Nino and the patterns of vector-borne disease can be viewed at a variety of spatial and temporal scales. At one extreme are long term predictions of changing precipitation and temperature patterns at continental and global scales. At the opposite extreme are the local or site specific ecological changes associated with the long term events. In order to understand and address the human health consequences of El Nino events, especially the patterns of vector-borne diseases, it is necessary to combine both scales of observation. At a local or regional scale the patterns of vector-borne diseases are determined by temperature, precipitation, and habitat availability. These factors, as well as disease incidence can be altered by El Nino events. Remote sensing data such as that acquired by the NOAA AVHRR and Landsat TM sensors can be used to characterize and monitor changing ecological conditions and therefore predict vector-borne disease patterns. The authors present the results of preliminary work on the analysis of historical AVHRR and TM data acquired during El Nino and nonfatal Nino years to characterize ecological conditions in Peru on a monthly basis. This information will then be combined with disease data to determine the relationship between changes in ecological conditions and disease incidence. Our goal is to produce a sequence of remotely sensed images which can be used to show the ecological and disease patterns associated with long term El Nino events and predictions.

A regularized auxiliary particle filtering approach for system state estimation and battery life prediction

NASA Astrophysics Data System (ADS)

Liu, Jie; Wang, Wilson; Ma, Fai

2011-07-01

System current state estimation (or condition monitoring) and future state prediction (or failure prognostics) constitute the core elements of condition-based maintenance programs. For complex systems whose internal state variables are either inaccessible to sensors or hard to measure under normal operational conditions, inference has to be made from indirect measurements using approaches such as Bayesian learning. In recent years, the auxiliary particle filter (APF) has gained popularity in Bayesian state estimation; the APF technique, however, has some potential limitations in real-world applications. For example, the diversity of the particles may deteriorate when the process noise is small, and the variance of the importance weights could become extremely large when the likelihood varies dramatically over the prior. To tackle these problems, a regularized auxiliary particle filter (RAPF) is developed in this paper for system state estimation and forecasting. This RAPF aims to improve the performance of the APF through two innovative steps: (1) regularize the approximating empirical density and redraw samples from a continuous distribution so as to diversify the particles; and (2) smooth out the rather diffused proposals by a rejection/resampling approach so as to improve the robustness of particle filtering. The effectiveness of the proposed RAPF technique is evaluated through simulations of a nonlinear/non-Gaussian benchmark model for state estimation. It is also implemented for a real application in the remaining useful life (RUL) prediction of lithium-ion batteries.

A fully automated health-care monitoring at home without attachment of any biological sensors and its clinical evaluation.

PubMed

Motoi, Kosuke; Ogawa, Mitsuhiro; Ueno, Hiroshi; Kuwae, Yutaka; Ikarashi, Akira; Yuji, Tadahiko; Higashi, Yuji; Tanaka, Shinobu; Fujimoto, Toshiro; Asanoi, Hidetsugu; Yamakoshi, Ken-ichi

2009-01-01

Daily monitoring of health condition is important for an effective scheme for early diagnosis, treatment and prevention of lifestyle-related diseases such as adiposis, diabetes, cardiovascular diseases and other diseases. Commercially available devices for health care monitoring at home are cumbersome in terms of self-attachment of biological sensors and self-operation of the devices. From this viewpoint, we have been developing a non-conscious physiological monitor installed in a bath, a lavatory, and a bed for home health care and evaluated its measurement accuracy by simultaneous recordings of a biological sensors directly attached to the body surface. In order to investigate its applicability to health condition monitoring, we have further developed a new monitoring system which can automatically monitor and store the health condition data. In this study, by evaluation on 3 patients with cardiac infarct or sleep apnea syndrome, patients' health condition such as body and excretion weight in the toilet and apnea and hypopnea during sleeping were successfully monitored, indicating that the system appears useful for monitoring the health condition during daily living.

Proton therapy treatment monitoring with the DoPET system: activity range, positron emitters evaluation and comparison with Monte Carlo predictions

NASA Astrophysics Data System (ADS)

Muraro, S.; Battistoni, G.; Belcari, N.; Bisogni, M. G.; Camarlinghi, N.; Cristoforetti, L.; Del Guerra, A.; Ferrari, A.; Fracchiolla, F.; Morrocchi, M.; Righetto, R.; Sala, P.; Schwarz, M.; Sportelli, G.; Topi, A.; Rosso, V.

2017-12-01

Ion beam irradiations can deliver conformal dose distributions minimizing damage to healthy tissues thanks to their characteristic dose profiles. Nevertheless, the location of the Bragg peak can be affected by different sources of range uncertainties: a critical issue is the treatment verification. During the treatment delivery, nuclear interactions between the ions and the irradiated tissues generate β+ emitters: the detection of this activity signal can be used to perform the treatment monitoring if an expected activity distribution is available for comparison. Monte Carlo (MC) codes are widely used in the particle therapy community to evaluate the radiation transport and interaction with matter. In this work, FLUKA MC code was used to simulate the experimental conditions of irradiations performed at the Proton Therapy Center in Trento (IT). Several mono-energetic pencil beams were delivered on phantoms mimicking human tissues. The activity signals were acquired with a PET system (DoPET) based on two planar heads, and designed to be installed along the beam line to acquire data also during the irradiation. Different acquisitions are analyzed and compared with the MC predictions, with a special focus on validating the PET detectors response for activity range verification.

Field-Integrated Studies of Long-Term Sustainability of Chromium Bioreduction at Hanford 100H Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, Philip E.

2006-06-01

The objectives of the project are to investigate coupled hydraulic, geochemical, and microbial conditions, and to determine the critical biogeochemical parameters necessary to maximize the extent of Cr(VI) bioreduction and minimize Cr(III) reoxidation in groundwater. Specific goals of the project are as follows: (1) Field testing and monitoring of Cr(VI) bioreduction in ground water and its transformation into insoluble species of Cr(III) at the Hanford 100H site, to develop the optimal strategy of water sampling for chemical, microbial, stable isotope analyses, and noninvasive geophysical monitoring; (2) Bench-scale flow and transport investigations using columns of undisturbed sediments to obtain diffusion andmore » kinetic parameters needed for the development of a numerical model, predictions of Cr(VI) bioreduction, and potential of Cr(III) reoxidation; and (3) Development of a multiphase, multi-component 3D reactive transport model and a code, TOUGHREACT-BIO, to predict coupled biogeochemical-hydrological processes associated with bioremediation, and to calibrate and validate the developed code based on the results of bench-scale and field-scale Cr(VI) biostimulation experiments in ground water at the Hanford Site.« less

A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case study.

PubMed

Wang, Bao-Zhen; Chen, Zhi

2013-01-01

This article presents a GIS-based multi-source and multi-box modeling approach (GMSMB) to predict the spatial concentration distributions of airborne pollutant on local and regional scales. In this method, an extended multi-box model combined with a multi-source and multi-grid Gaussian model are developed within the GIS framework to examine the contributions from both point- and area-source emissions. By using GIS, a large amount of data including emission sources, air quality monitoring, meteorological data, and spatial location information required for air quality modeling are brought into an integrated modeling environment. It helps more details of spatial variation in source distribution and meteorological condition to be quantitatively analyzed. The developed modeling approach has been examined to predict the spatial concentration distribution of four air pollutants (CO, NO(2), SO(2) and PM(2.5)) for the State of California. The modeling results are compared with the monitoring data. Good agreement is acquired which demonstrated that the developed modeling approach could deliver an effective air pollution assessment on both regional and local scales to support air pollution control and management planning.

Disturbance, neutral theory, and patterns of beta diversity in soil communities.

PubMed

Maaß, Stefanie; Migliorini, Massimo; Rillig, Matthias C; Caruso, Tancredi

2014-12-01

Beta diversity describes how local communities within an area or region differ in species composition/abundance. There have been attempts to use changes in beta diversity as a biotic indicator of disturbance, but lack of theory and methodological caveats have hampered progress. We here propose that the neutral theory of biodiversity plus the definition of beta diversity as the total variance of a community matrix provide a suitable, novel, starting point for ecological applications. Observed levels of beta diversity (BD) can be compared to neutral predictions with three possible outcomes: Observed BD equals neutral prediction or is larger (divergence) or smaller (convergence) than the neutral prediction. Disturbance might lead to either divergence or convergence, depending on type and strength. We here apply these ideas to datasets collected on oribatid mites (a key, very diverse soil taxon) under several regimes of disturbances. When disturbance is expected to increase the heterogeneity of soil spatial properties or the sampling strategy encompassed a range of diverging environmental conditions, we observed diverging assemblages. On the contrary, we observed patterns consistent with neutrality when disturbance could determine homogenization of soil properties in space or the sampling strategy encompassed fairly homogeneous areas. With our method, spatial and temporal changes in beta diversity can be directly and easily monitored to detect significant changes in community dynamics, although the method itself cannot inform on underlying mechanisms. However, human-driven disturbances and the spatial scales at which they operate are usually known. In this case, our approach allows the formulation of testable predictions in terms of expected changes in beta diversity, thereby offering a promising monitoring tool.

Evaluation of infrared thermography as a diagnostic tool to predict heat stress events in feedlot cattle.

PubMed

Unruh, Ellen M; Theurer, Miles E; White, Brad J; Larson, Robert L; Drouillard, James S; Schrag, Nora

2017-07-01

OBJECTIVE To determine whether infrared thermographic images obtained the morning after overnight heat abatement could be used as the basis for diagnostic algorithms to predict subsequent heat stress events in feedlot cattle exposed to high ambient temperatures. ANIMALS 60 crossbred beef heifers (mean ± SD body weight, 385.8 ± 20.3 kg). PROCEDURES Calves were housed in groups of 20 in 3 pens without any shade. During the 6 am and 3 pm hours on each of 10 days during a 14-day period when the daily ambient temperature was forecasted to be > 29.4°C, an investigator walked outside each pen and obtained profile digital thermal images of and assigned panting scores to calves near the periphery of the pen. Relationships between infrared thermographic data and panting scores were evaluated with artificial learning models. RESULTS Afternoon panting score was positively associated with morning but not afternoon thermographic data (body surface temperature). Evaluation of multiple artificial learning models indicated that morning body surface temperature was not an accurate predictor of an afternoon heat stress event, and thermographic data were of little predictive benefit, compared with morning and forecasted weather conditions. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated infrared thermography was an objective method to monitor beef calves for heat stress in research settings. However, thermographic data obtained in the morning did not accurately predict which calves would develop heat stress later in the day. The use of infrared thermography as a diagnostic tool for monitoring heat stress in feedlot cattle requires further investigation.

Switching Kalman filter for failure prognostic

NASA Astrophysics Data System (ADS)

Lim, Chi Keong Reuben; Mba, David

2015-02-01

The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.

Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Rahmawati, P.; Prajitno, P.

2018-04-01

Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

A feasibility study on the predictive emission monitoring system applied to the Hsinta power plant of Taiwan Power Company.

PubMed

Chien, T W; Chu, H; Hsu, W C; Tseng, T K; Hsu, C H; Chen, K Y

2003-08-01

The continuous emission monitoring system (CEMS) can monitor flue gas emissions continuously and instantaneously. However, it has the disadvantages of enormous cost, easily producing errors in sampling periods of bad weather, lagging response in variable ambient environments, and missing data in daily zero and span tests and maintenance. The concept of a predictive emission monitoring system (PEMS) is to use the operating parameters of combustion equipment through thermodynamic or statistical methods to construct a mathematic model that can predict emissions by a computer program. The goal of this study is to set up a PEMS in a gas-fired combined cycle power generation unit at the Hsinta station of Taiwan Power Co. The emissions to be monitored include nitrogen oxides (NOx) and oxygen (O2) in flue gas. The major variables of the predictive model were determined based on the combustion theory. The data of these variables then were analyzed to establish a regression model. From the regression results, the influences of these variables are discussed and the predicted values are compared with the CEMS data for accuracy. In addition, according to the cost information, the capital and operation and maintenance costs for a PEMS can be much lower than those for a CEMS.

Predictability and context determine differences in conflict monitoring between adolescence and adulthood.

PubMed

Chmielewski, Witold X; Roessner, Veit; Beste, Christian

2015-10-01

The ability to link contextual information to actions is an important aspect of conflict monitoring and response selection. These mechanisms depend on medial prefrontal networks. Although these areas undergo a protracted development from adolescence to adulthood, it has remained elusive how the influence of contextual information on conflict monitoring is modulated between adolescence and adulthood. Using event-related potentials (ERPs) and source localization techniques we show that the ability to link contextual information to actions is altered and that the predictability of upcoming events is an important factor to consider in this context. In adolescents, conflict monitoring functions are not as much modulated by predictability factors as in adults. It seems that adults exhibit a stronger anticipation of upcoming events than adolescents. This results in disadvantages for adults when the upcoming context is not predictable. In adolescents, problems to predict upcoming events therefore turn out to be beneficial. Two cognitive-neurophysiological factors are important for this: The first factor is related to altered conflict monitoring functions associated with modulations of neural activity in the medial frontal cortex. The second factor is related to altered perceptual processing of target stimuli associated with modulations of neural activity in parieto-occipital areas. Copyright © 2015 Elsevier B.V. All rights reserved.

Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

NASA Astrophysics Data System (ADS)

Dong, Hancheng; Jin, Xiaoning; Lou, Yangbing; Wang, Changhong

2014-12-01

Lithium-ion batteries are used as the main power source in many electronic and electrical devices. In particular, with the growth in battery-powered electric vehicle development, the lithium-ion battery plays a critical role in the reliability of vehicle systems. In order to provide timely maintenance and replacement of battery systems, it is necessary to develop a reliable and accurate battery health diagnostic that takes a prognostic approach. Therefore, this paper focuses on two main methods to determine a battery's health: (1) Battery State-of-Health (SOH) monitoring and (2) Remaining Useful Life (RUL) prediction. Both of these are calculated by using a filter algorithm known as the Support Vector Regression-Particle Filter (SVR-PF). Models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. Moreover, the RUL prediction model is proposed, which is able to provide the RUL value and update the RUL probability distribution to the End-of-Life cycle. Results for both methods are presented, showing that the proposed SOH monitoring and RUL prediction methods have good performance and that the SVR-PF has better monitoring and prediction capability than the standard particle filter (PF).

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

PubMed

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-04-09

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

PubMed Central

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-01-01

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web. PMID:28397776

Physically based approaches incorporating evaporation for early warning predictions of rainfall-induced landslides

NASA Astrophysics Data System (ADS)

Reder, Alfredo; Rianna, Guido; Pagano, Luca

2018-02-01

In the field of rainfall-induced landslides on sloping covers, models for early warning predictions require an adequate trade-off between two aspects: prediction accuracy and timeliness. When a cover's initial hydrological state is a determining factor in triggering landslides, taking evaporative losses into account (or not) could significantly affect both aspects. This study evaluates the performance of three physically based predictive models, converting precipitation and evaporative fluxes into hydrological variables useful in assessing slope safety conditions. Two of the models incorporate evaporation, with one representing evaporation as both a boundary and internal phenomenon, and the other only a boundary phenomenon. The third model totally disregards evaporation. Model performances are assessed by analysing a well-documented case study involving a 2 m thick sloping volcanic cover. The large amount of monitoring data collected for the soil involved in the case study, reconstituted in a suitably equipped lysimeter, makes it possible to propose procedures for calibrating and validating the parameters of the models. All predictions indicate a hydrological singularity at the landslide time (alarm). A comparison of the models' predictions also indicates that the greater the complexity and completeness of the model, the lower the number of predicted hydrological singularities when no landslides occur (false alarms).

Monitoring of corrosion damage using high-frequency guided ultrasonic waves

NASA Astrophysics Data System (ADS)

Chew, D.; Fromme, P.

2014-03-01

Due to adverse environmental conditions corrosion can develop during the life cycle of industrial structures, e.g., offshore oil platforms, ships, and desalination plants. Both pitting corrosion and generalized corrosion leading to wall thickness loss can cause the degradation of the integrity and load bearing capacity of the structure. Structural health monitoring of corrosion damage in difficult to access areas can in principle be achieved using high frequency guided waves propagating along the structure from accessible areas. Using standard ultrasonic transducers with single sided access to the structure, high frequency guided wave modes were generated that penetrate through the complete thickness of the structure. Wall thickness reduction was induced using accelerated corrosion in a salt water bath. The corrosion damage was monitored based on the effect on the wave propagation and interference of the different modes. The change in the wave interference was quantified based on an analysis in the frequency domain (Fourier transform) and was found to match well with theoretical predictions for the wall thickness loss. High frequency guided waves have the potential for corrosion damage monitoring at critical and difficult to access locations from a stand-off distance.

Monitoring of corrosion damage using high-frequency guided ultrasonic waves

NASA Astrophysics Data System (ADS)

Chew, D.; Fromme, P.

2015-03-01

Due to adverse environmental conditions corrosion can develop during the life cycle of industrial structures, e.g., offshore oil platforms, ships, and desalination plants. Both pitting corrosion and generalized corrosion leading to wall thickness loss can cause the degradation of the integrity and load bearing capacity of the structure. Structural health monitoring of corrosion damage in difficult to access areas can in principle be achieved using high frequency guided waves propagating along the structure from accessible areas. Using standard ultrasonic transducers with single sided access to the structure, high frequency guided wave modes were generated that penetrate through the complete thickness of the structure. Wall thickness reduction was induced using accelerated corrosion in a salt water bath. The corrosion damage was monitored based on the effect on the wave propagation and interference of the different modes. The change in the wave interference was quantified based on an analysis in the frequency domain (Fourier transform) and was found to match well with theoretical predictions for the wall thickness loss. High frequency guided waves have the potential for corrosion damage monitoring at critical and difficult to access locations from a stand-off distance.

Mechanical and Chemical Properties of Harvested Hypalon Cable Jacket Subjected to Accelerated Thermal Aging

DOE PAGES

Duckworth, Robert C.; Kidder, Michelle K.; Aytug, Tolga; ...

2018-02-27

We report that for nuclear power plants (NPPs) considering second license renewal for operation beyond 60 years, knowledge of long-term operation, condition monitoring, and viability for the reactor components including reactor pressure vessel, concrete structures, and cable systems is essential. Such knowledge will provide NPP owners/operators with a basis for predicting performance and estimating the costs associated with monitoring or replacement programs for the affected systems. For cable systems that encompass a wide variety of materials, manufacturers, and in-plant locations, accelerated aging of harvested cable jacket and insulation can provide insight into a remaining useful life and methods for monitoring.more » Accelerated thermal aging in air at temperatures between 80°C and 120°C was conducted on a multiconductor control rod drive mechanism cable manufactured by Boston Insulated Wire (BIW). The cable, which had been in service for over 30 years, was jacketed with Hypalon and insulated with ethylene propylene rubber. From elongation at break (EAB) measurements and supporting Arrhenius analysis of the jacket material, an activation energy of 97.84 kJ/mol was estimated, and the time to degradation, as represented by 50% EAB at the expected maximum operating temperature of 45°C, was estimated to be 80 years. These values were slightly below previous measurements on similar BIW Hypalon cable jacket and could be attributed to either in-service degradation or variations in material properties from production variations. Lastly, results from indenter modulus measurements and Fourier transform infrared spectroscopy suggest possible markers that could be beneficial in monitoring cable conditions.« less

An Assessment of Long-Term Compliance with Performance Standards in Compensatory Mitigation Wetlands.

PubMed

Van den Bosch, Kyle; Matthews, Jeffrey W

2017-04-01

Under the US Clean Water Act, wetland restoration is used to compensate for adverse impacts to wetlands. Following construction, compensation wetlands are monitored for approximately 5 years to determine if they comply with project-specific performance standards. Once a compensation site complies with performance standards, it is assumed that the site will continue to meet standards indefinitely. However, there have been few assessments of long-term compliance. We surveyed, in 2012, 30 compensation sites 8-20 years after restoration to determine whether projects continued to meet performance standards. Additionally, we compared floristic quality of compensation sites to the quality of adjacent natural wetlands to determine whether wetland condition in compensation sites could be predicted based on the condition of nearby wetlands. Compensation sites met, on average, 65% of standards during the final year of monitoring and 53% of standards in 2012, a significant decrease in compliance. Although forested wetlands often failed to meet standards for planted tree survival, the temporal decrease in compliance was driven by increasing dominance by invasive plants in emergent wetlands. The presumption of continued compliance with performance standards after a 5-year monitoring period was not supported. Wetlands restored near better quality natural wetlands achieved and maintained greater floristic quality, suggesting that landscape context was an important determinant of long-term restoration outcomes. Based on our findings, we recommend that compensation wetlands should be monitored for longer time periods, and we suggest that nearby or adjacent natural wetlands provide good examples of reasonably achievable restoration outcomes in a particular landscape.

Mechanical and Chemical Properties of Harvested Hypalon Cable Jacket Subjected to Accelerated Thermal Aging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duckworth, Robert C.; Kidder, Michelle K.; Aytug, Tolga

We report that for nuclear power plants (NPPs) considering second license renewal for operation beyond 60 years, knowledge of long-term operation, condition monitoring, and viability for the reactor components including reactor pressure vessel, concrete structures, and cable systems is essential. Such knowledge will provide NPP owners/operators with a basis for predicting performance and estimating the costs associated with monitoring or replacement programs for the affected systems. For cable systems that encompass a wide variety of materials, manufacturers, and in-plant locations, accelerated aging of harvested cable jacket and insulation can provide insight into a remaining useful life and methods for monitoring.more » Accelerated thermal aging in air at temperatures between 80°C and 120°C was conducted on a multiconductor control rod drive mechanism cable manufactured by Boston Insulated Wire (BIW). The cable, which had been in service for over 30 years, was jacketed with Hypalon and insulated with ethylene propylene rubber. From elongation at break (EAB) measurements and supporting Arrhenius analysis of the jacket material, an activation energy of 97.84 kJ/mol was estimated, and the time to degradation, as represented by 50% EAB at the expected maximum operating temperature of 45°C, was estimated to be 80 years. These values were slightly below previous measurements on similar BIW Hypalon cable jacket and could be attributed to either in-service degradation or variations in material properties from production variations. Lastly, results from indenter modulus measurements and Fourier transform infrared spectroscopy suggest possible markers that could be beneficial in monitoring cable conditions.« less

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring

PubMed Central

Kim, Sun-Young; Olives, Casey; Sheppard, Lianne; Sampson, Paul D.; Larson, Timothy V.; Keller, Joshua P.; Kaufman, Joel D.

2016-01-01

Introduction: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. Objectives: We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications. Methods: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). Results: In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84–0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00–0.85) most likely because of fewer monitoring sites and inconsistent sampling methods. Conclusions: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. Citation: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38–46; http://dx.doi.org/10.1289/EHP131 PMID:27340825

Evaluating effects of Everglades restoration on American crocodile populations in south Florida using a spatially-explicit, stage-based population model

USGS Publications Warehouse

Green, Timothy W.; Slone, Daniel H.; Swain, Eric D.; Cherkiss, Michael S.; Lohmann, Melinda; Mazzotti, Frank J.; Rice, Kenneth G.

2014-01-01

The distribution and abundance of the American crocodile (Crocodylus acutus) in the Florida Everglades is dependent on the timing, amount, and location of freshwater flow. One of the goals of the Comprehensive Everglades Restoration Plan (CERP) is to restore historic freshwater flows to American crocodile habitat throughout the Everglades. To predict the impacts on the crocodile population from planned restoration activities, we created a stage-based spatially explicit crocodile population model that incorporated regional hydrology models and American crocodile research and monitoring data. Growth and survival were influenced by salinity, water depth, and density-dependent interactions. A stage-structured spatial model was used with discrete spatial convolution to direct crocodiles toward attractive sources where conditions were favorable. The model predicted that CERP would have both positive and negative impacts on American crocodile growth, survival, and distribution. Overall, crocodile populations across south Florida were predicted to decrease approximately 3 % with the implementation of CERP compared to future conditions without restoration, but local increases up to 30 % occurred in the Joe Bay area near Taylor Slough, and local decreases up to 30 % occurred in the vicinity of Buttonwood Canal due to changes in salinity and freshwater flows.

A new approach for structural health monitoring by applying anomaly detection on strain sensor data

NASA Astrophysics Data System (ADS)

Trichias, Konstantinos; Pijpers, Richard; Meeuwissen, Erik

2014-03-01

Structural Health Monitoring (SHM) systems help to monitor critical infrastructures (bridges, tunnels, etc.) remotely and provide up-to-date information about their physical condition. In addition, it helps to predict the structure's life and required maintenance in a cost-efficient way. Typically, inspection data gives insight in the structural health. The global structural behavior, and predominantly the structural loading, is generally measured with vibration and strain sensors. Acoustic emission sensors are more and more used for measuring global crack activity near critical locations. In this paper, we present a procedure for local structural health monitoring by applying Anomaly Detection (AD) on strain sensor data for sensors that are applied in expected crack path. Sensor data is analyzed by automatic anomaly detection in order to find crack activity at an early stage. This approach targets the monitoring of critical structural locations, such as welds, near which strain sensors can be applied during construction and/or locations with limited inspection possibilities during structural operation. We investigate several anomaly detection techniques to detect changes in statistical properties, indicating structural degradation. The most effective one is a novel polynomial fitting technique, which tracks slow changes in sensor data. Our approach has been tested on a representative test structure (bridge deck) in a lab environment, under constant and variable amplitude fatigue loading. In both cases, the evolving cracks at the monitored locations were successfully detected, autonomously, by our AD monitoring tool.

International Space Station Internal Thermal Control System Cold Plate/Fluid-Stability Test: Two Year Update

NASA Technical Reports Server (NTRS)

Wieland, Paul; Holt, Mike; Roman, Monsi; Cole, Harold; Daugherty, Steve

2003-01-01

Operation of the Internal Thermal Control System (ITCS) Cold Plate/Fluid-Stability Test Facility commenced on September 5, 2000. The facility was intended to provide advance indication of potential problems on board the International Space Station (ISS) and was designed: 1) To be materially similar to the flight ITCS. 2) To allow for monitoring during operation. 3) To run continuously for three years. During the first two years of operation the conditions of the coolant and components were remarkably stable. During this same period of time, the conditions of the ISS ITCS significantly diverged from the desired state. Due to this divergence, the test facility has not been providing information useful for predicting the flight ITCS condition. Results of the first two years are compared with flight conditions over the same time period, showing the similarities and divergences. To address the divergences, the test facility was modified incrementally to more closely match the flight conditions, and to gain insight into the reasons for the divergence. Results of these incremental changes are discussed and provide insight into the development of the conditions on orbit.