Real-time control of combined surface water quantity and quality: polder flushing.

PubMed

Xu, M; van Overloop, P J; van de Giesen, N C; Stelling, G S

2010-01-01

In open water systems, keeping both water depths and water quality at specified values is critical for maintaining a 'healthy' water system. Many systems still require manual operation, at least for water quality management. When applying real-time control, both quantity and quality standards need to be met. In this paper, an artificial polder flushing case is studied. Model Predictive Control (MPC) is developed to control the system. In addition to MPC, a 'forward estimation' procedure is used to acquire water quality predictions for the simplified model used in MPC optimization. In order to illustrate the advantages of MPC, classical control [Proportional-Integral control (PI)] has been developed for comparison in the test case. The results show that both algorithms are able to control the polder flushing process, but MPC is more efficient in functionality and control flexibility.

RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems

PubMed Central

Yu, Ruiyun; Yang, Yu; Yang, Leyou; Han, Guangjie; Move, Oguti Ann

2016-01-01

Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing. PMID:26761008

Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting.

PubMed

Lee, JuneHyuck; Noh, Sang Do; Kim, Hyun-Jung; Kang, Yong-Shin

2018-05-04

The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry.

CRN5EXP: Expert system for statistical quality control

NASA Technical Reports Server (NTRS)

Hentea, Mariana

1991-01-01

The purpose of the Expert System CRN5EXP is to assist in checking the quality of the coils at two very important mills: Hot Rolling and Cold Rolling in a steel plant. The system interprets the statistical quality control charts, diagnoses and predicts the quality of the steel. Measurements of process control variables are recorded in a database and sample statistics such as the mean and the range are computed and plotted on a control chart. The chart is analyzed through patterns using the C Language Integrated Production System (CLIPS) and a forward chaining technique to reach a conclusion about the causes of defects and to take management measures for the improvement of the quality control techniques. The Expert System combines the certainty factors associated with the process control variables to predict the quality of the steel. The paper presents the approach to extract data from the database, the reason to combine certainty factors, the architecture and the use of the Expert System. However, the interpretation of control charts patterns requires the human expert's knowledge and lends to Expert Systems rules.

Modelling of beef sensory quality for a better prediction of palatability.

PubMed

Hocquette, Jean-François; Van Wezemael, Lynn; Chriki, Sghaier; Legrand, Isabelle; Verbeke, Wim; Farmer, Linda; Scollan, Nigel D; Polkinghorne, Rod; Rødbotten, Rune; Allen, Paul; Pethick, David W

2014-07-01

Despite efforts by the industry to control the eating quality of beef, there remains a high level of variability in palatability, which is one reason for consumer dissatisfaction. In Europe, there is still no reliable on-line tool to predict beef quality and deliver consistent quality beef to consumers. Beef quality traits depend in part on the physical and chemical properties of the muscles. The determination of these properties (known as muscle profiling) will allow for more informed decisions to be made in the selection of individual muscles for the production of value-added products. Therefore, scientists and professional partners of the ProSafeBeef project have brought together all the data they have accumulated over 20 years. The resulting BIF-Beef (Integrated and Functional Biology of Beef) data warehouse contains available data of animal growth, carcass composition, muscle tissue characteristics and beef quality traits. This database is useful to determine the most important muscle characteristics associated with a high tenderness, a high flavour or generally a high quality. Another more consumer driven modelling tool was developed in Australia: the Meat Standards Australia (MSA) grading scheme that predicts beef quality for each individual muscle×specific cooking method combination using various information on the corresponding animals and post-slaughter processing factors. This system has also the potential to detect variability in quality within muscles. The MSA system proved to be effective in predicting beef palatability not only in Australia but also in many other countries. The results of the work conducted in Europe within the ProSafeBeef project indicate that it would be possible to manage a grading system in Europe similar to the MSA system. The combination of the different modelling approaches (namely muscle biochemistry and a MSA-like meat grading system adapted to the European market) is a promising area of research to improve the prediction of beef quality. In both approaches, the volume of data available not only provides statistically sound correlations between various factors and beef quality traits but also a better understanding of the variability of beef quality according to various criteria (breed, age, sex, pH, marbling etc.). © 2013 The American Meat Science Association. All rights reserved.

The reliability-quality relationship for quality systems and quality risk management.

PubMed

Claycamp, H Gregg; Rahaman, Faiad; Urban, Jason M

2012-01-01

Engineering reliability typically refers to the probability that a system, or any of its components, will perform a required function for a stated period of time and under specified operating conditions. As such, reliability is inextricably linked with time-dependent quality concepts, such as maintaining a state of control and predicting the chances of losses from failures for quality risk management. Two popular current good manufacturing practice (cGMP) and quality risk management tools, failure mode and effects analysis (FMEA) and root cause analysis (RCA) are examples of engineering reliability evaluations that link reliability with quality and risk. Current concepts in pharmaceutical quality and quality management systems call for more predictive systems for maintaining quality; yet, the current pharmaceutical manufacturing literature and guidelines are curiously silent on engineering quality. This commentary discusses the meaning of engineering reliability while linking the concept to quality systems and quality risk management. The essay also discusses the difference between engineering reliability and statistical (assay) reliability. The assurance of quality in a pharmaceutical product is no longer measured only "after the fact" of manufacturing. Rather, concepts of quality systems and quality risk management call for designing quality assurance into all stages of the pharmaceutical product life cycle. Interestingly, most assays for quality are essentially static and inform product quality over the life cycle only by being repeated over time. Engineering process reliability is the fundamental concept that is meant to anticipate quality failures over the life cycle of the product. Reliability is a well-developed theory and practice for other types of manufactured products and manufacturing processes. Thus, it is well known to be an appropriate index of manufactured product quality. This essay discusses the meaning of reliability and its linkages with quality systems and quality risk management.

Predictive Techniques for Spacecraft Cabin Air Quality Control

NASA Technical Reports Server (NTRS)

Perry, J. L.; Cromes, Scott D. (Technical Monitor)

2001-01-01

As assembly of the International Space Station (ISS) proceeds, predictive techniques are used to determine the best approach for handling a variety of cabin air quality challenges. These techniques use equipment offgassing data collected from each ISS module before flight to characterize the trace chemical contaminant load. Combined with crew metabolic loads, these data serve as input to a predictive model for assessing the capability of the onboard atmosphere revitalization systems to handle the overall trace contaminant load as station assembly progresses. The techniques for predicting in-flight air quality are summarized along with results from early ISS mission analyses. Results from groundbased analyses of in-flight air quality samples are compared to the predictions to demonstrate the technique's relative conservatism.

Predicting Nitrogen Transport From Individual Sewage Disposal Systems for a Proposed Development in Adams County, Colorado

NASA Astrophysics Data System (ADS)

Heatwole, K. K.; McCray, J.; Lowe, K.

2005-12-01

Individual sewage disposal systems (ISDS) have demonstrated the capability to be an effective method of treatment for domestic wastewater. They also are advantageous from a water resources standpoint because there is little water leaving the local hydrologic system. However, if unfavorable settings exist, ISDS can have a detrimental effect on local water-quality. This presentation will focus on assessing the potential impacts of a large housing development to area water quality. The residential development plans to utilize ISDS to accommodate all domestic wastewater generated within the development. The area of interest is located just west of Brighton, Colorado, on the northwestern margin of the Denver Basin. Efforts of this research will focus on impacts of ISDS to local groundwater and surface water systems. The Arapahoe Aquifer, which exists at relatively shallow depths in the area of proposed development, is suspected to be vulnerable to contamination from ISDS. Additionally, the local water quality of the Arapahoe Aquifer was not well known at the start of the study. As a result, nitrate was selected as a fo-cus water quality parameter because it is easily produced through nitrification of septic tank effluent and because of the previous agricultural practices that could be another potential source of nitrate. Several different predictive tools were used to attempt to predict the potential impacts of ISDS to water quality in the Arapahoe Aquifer. The objectives of these tools were to 1) assess the vulnerability of the Arapahoe Aquifer to ni-trate contamination, 2) predict the nitrate load to the aquifer, and 3) determine the sensitivity of different parameter inputs and the overall prediction uncertainty. These predictive tools began with very simple mass-loading calcula-tions and progressed to more complex, vadose-zone numerical contaminant transport modeling.

Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting

PubMed Central

Lee, JuneHyuck; Noh, Sang Do; Kim, Hyun-Jung; Kang, Yong-Shin

2018-01-01

The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry. PMID:29734699

The Simulations of Wildland Fire Smoke PM25 in the NWS Air Quality Forecasting Systems

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2017-12-01

The increase of wildland fire intensity and frequency in the United States (U.S.) has led to property loss, human fatality, and poor air quality due to elevated particulate matters and surface ozone concentrations. The NOAA/National Weather Service (NWS) built the National Air Quality Forecast Capability (NAQFC) based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) Modeling System driven by the NCEP North American Mesoscale Forecast System meteorology to provide ozone and fine particulate matter (PM2.5) forecast guidance publicly. State and local forecasters use the NWS air quality forecast guidance to issue air quality alerts in their area. The NAQFC PM2.5 predictions include emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and wildland fires. The wildland fire emission inputs to the NAQFC is derived from the NOAA National Environmental Satellite, Data, and Information Service Hazard Mapping System fire and smoke detection product and the emission module of the U.S. Forest Service (USFS) BlueSky Smoke Modeling Framework. Wildland fires are unpredictable and can be ignited by natural causes such as lightning or be human-caused. It is extremely difficult to predict future occurrences and behavior of wildland fires, as is the available bio-fuel to be burned for real-time air quality predictions. Assumptions of future day's wildland fire behavior often have to be made from older observed wildland fire information. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that large errors in PM2.5 prediction can occur if fire smoke emissions are sometimes placed at the wrong location and/or time. A configuration of NAQFC CMAQ-system to re-run previous 24 hours, during which wildland fires were observed from satellites has been included recently. This study focuses on the effort performed to minimize the error in NAQFC PM2.5 predictions resulting from incorporating fire smoke emissions into the NAQFC from a recently updated newer version of USFS BlueSky system. This study will show how new approaches has improved the PM2.5 predictions at both nearby and downstream areas from fire sources. Furthermore, Environment and Climate Change Canada (ECCC) fire emissions data are being tested.

LARGE-SCALE PREDICTIONS OF MOBILE SOURCE CONTRIBUTIONS TO CONCENTRATIONS OF TOXIC AIR POLLUTANTS

EPA Science Inventory

This presentation shows concentrations and deposition of toxic air pollutants predicted by a 3-D air quality model, the Community Multi Scale Air Quality (CMAQ) modeling system. Contributions from both on-road and non-road mobile sources are analyzed.

Environmental effects of dredging. Documentation of the efqual module for ADDAMS: Comparison of predicted effluent water quality with standards. Technical notes

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

Palermo, M.R.; Schroeder, P.R.

This technical note describes a technique for comparison of the predicted quality of effluent discharged from confined dredged material disposal areas with applicable water quality standards. This note also serves as documentation of a computer program called EFQUAL written for that purpose as part of the Automated Dredging and Disposal Alternatives Management System (ADDAMS).

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE PAGES

Li, Mingjie; Zhou, Ping; Wang, Hong; ...

2017-09-19

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

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

Li, Mingjie; Zhou, Ping; Wang, Hong

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

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.

Documentation of the runqual module for ADDAMS: Comparison of predicted runoff water quality with standards. Environmental effects of dredging. Technical notes

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

Schroeder, P.R.; Gibson, A.C.; Dardeau, E.A.

This technical note has a twofold purpose: to describe a technique for comparing the predicted quality of surface runoff from confined dredged material disposal areas with applicable water quality standards and to document a computer program called RUNQUAL, written for that purpose as a part of the Automated Dredging and Disposal Alternatives Management System (ADDAMS).

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.

LINKING ETA MODEL WITH THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM: OZONE BOUNDARY CONDITIONS

EPA Science Inventory

A prototype surface ozone concentration forecasting model system for the Eastern U.S. has been developed. The model system is consisting of a regional meteorological and a regional air quality model. It demonstrated a strong prediction dependence on its ozone boundary conditions....

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.

[Value of sepsis single-disease manage system in predicting mortality in patients with sepsis].

PubMed

Chen, J; Wang, L H; Ouyang, B; Chen, M Y; Wu, J F; Liu, Y J; Liu, Z M; Guan, X D

2018-04-03

Objective: To observe the effect of sepsis single-disease manage system on the improvement of sepsis treatment and the value in predicting mortality in patients with sepsis. Methods: A retrospective study was conducted. Patients with sepsis admitted to the Department of Surgical Intensive Care Unit of Sun Yat-Sen University First Affiliated Hospital from September 22, 2013 to May 5, 2015 were enrolled in this study. Sepsis single-disease manage system (Rui Xin clinical data manage system, China data, China) was used to monitor 25 clinical quality parameters, consisting of timeliness, normalization and outcome parameters. Based on whether these quality parameters could be completed or not, the clinical practice was evaluated by the system. The unachieved quality parameter was defined as suspicious parameters, and these suspicious parameters were used to predict mortality of patients with receiver operating characteristic curve (ROC). Results: A total of 1 220 patients with sepsis were enrolled, included 805 males and 415 females. The mean age was (59±17) years, and acute physiology and chronic health evaluation (APACHE Ⅱ) scores was 19±8. The area under ROC curve of total suspicious numbers for predicting 28-day mortality was 0.70; when the suspicious parameters number was more than 6, the sensitivity was 68.0% and the specificity was 61.0% for predicting 28-day mortality. In addition, the area under ROC curve of outcome suspicious number for predicting 28-day mortality was 0.89; when the suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 78.0% for predicting 28-day mortality. Moreover, the area under ROC curve of total suspicious number for predicting 90-day mortality was 0.73; when the total suspicious parameters number was more than 7, the sensitivity was 60.0% and the specificity was 74.0% for predicting 90-day mortality. Finally, the area under ROC curve of outcome suspicious numbers for predicting 90-day mortality was 0.92; when suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 81.0% for predicting 90-day mortality. Conclusion: The single center study suggests that this sepsis single-disease manage system could be used to monitor the completion of clinical practice for intensivist in managing sepsis, and the number of quality parameters failed to complete could be used to predict the mortality of the patients.

Thin-slice vision: inference of confidence measure from perceptual video quality

NASA Astrophysics Data System (ADS)

Hameed, Abdul; Balas, Benjamin; Dai, Rui

2016-11-01

There has been considerable research on thin-slice judgments, but no study has demonstrated the predictive validity of confidence measures when assessors watch videos acquired from communication systems, in which the perceptual quality of videos could be degraded by limited bandwidth and unreliable network conditions. This paper studies the relationship between high-level thin-slice judgments of human behavior and factors that contribute to perceptual video quality. Based on a large number of subjective test results, it has been found that the confidence of a single individual present in all the videos, called speaker's confidence (SC), could be predicted by a list of features that contribute to perceptual video quality. Two prediction models, one based on artificial neural network and the other based on a decision tree, were built to predict SC. Experimental results have shown that both prediction models can result in high correlation measures.

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART II--OZONE PREDICTIONS. (R825260)

EPA Science Inventory

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectra...

PERFORMANCE AND DIAGNOSTIC EVALUATION OF OZONE PREDICTIONS BY THE ETA-COMMUNITY MULTISCALE AIR QUALITY FORECAST SYSTEM DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY

EPA Science Inventory

A real-time air quality forecasting system (Eta-CMAQ model suite) has been developed by linking the NCEP Eta model to the U.S. EPA CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting O₃ over the northeastern U.S d...

Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare

PubMed Central

Wilson, Richard; Goodacre, Steve W; Klingbajl, Marcin; Kelly, Anne-Maree; Rainer, Tim; Coats, Tim; Holloway, Vikki; Townend, Will; Crane, Steve

2014-01-01

Background and objective Risk-adjusted mortality rates can be used as a quality indicator if it is assumed that the discrepancy between predicted and actual mortality can be attributed to the quality of healthcare (ie, the model has attributional validity). The Development And Validation of Risk-adjusted Outcomes for Systems of emergency care (DAVROS) model predicts 7-day mortality in emergency medical admissions. We aimed to test this assumption by evaluating the attributional validity of the DAVROS risk-adjustment model. Methods We selected cases that had the greatest discrepancy between observed mortality and predicted probability of mortality from seven hospitals involved in validation of the DAVROS risk-adjustment model. Reviewers at each hospital assessed hospital records to determine whether the discrepancy between predicted and actual mortality could be explained by the healthcare provided. Results We received 232/280 (83%) completed review forms relating to 179 unexpected deaths and 53 unexpected survivors. The healthcare system was judged to have potentially contributed to 10/179 (8%) of the unexpected deaths and 26/53 (49%) of the unexpected survivors. Failure of the model to appropriately predict risk was judged to be responsible for 135/179 (75%) of the unexpected deaths and 2/53 (4%) of the unexpected survivors. Some 10/53 (19%) of the unexpected survivors died within a few months of the 7-day period of model prediction. Conclusions We found little evidence that deaths occurring in patients with a low predicted mortality from risk-adjustment could be attributed to the quality of healthcare provided. PMID:23605036

Multiple Sensitivity Testing for Regional Air Quality Model in summer 2014

NASA Astrophysics Data System (ADS)

Tang, Y.; Lee, P.; Pan, L.; Tong, D.; Kim, H. C.; Huang, M.; Wang, J.; McQueen, J.; Lu, C. H.; Artz, R. S.

2015-12-01

The NOAA Air Resources laboratory leads to improve the performance of the U.S. Air Quality Forecasting Capability (NAQFC). It is operational in NOAA National Centers for Environmental Prediction (NCEP) which focuses on predicting surface ozone and PM2.5. In order to improve its performance, we tested several approaches, including NOAA Environmental Modeling System Global Aerosol Component (NGAC) simulation derived ozone and aerosol lateral boundary conditions (LBC), bi-direction NH3 emission and HMS(Hazard Mapping System)-BlueSky emission with the latest U.S. EPA Community Multi-scale Air Quality model (CMAQ) version and the U.S EPA National Emission Inventory (NEI)-2011 anthropogenic emissions. The operational NAQFC uses static profiles for its lateral boundary condition (LBC), which does not impose severe issue for near-surface air quality prediction. However, its degraded performance for the upper layer (e.g. above 3km) is evident when comparing with aircraft measured ozone. NCEP's Global Forecast System (GFS) has tracer O3 prediction treated as 3-D prognostic variable (Moorthi and Iredell, 1998) after being initialized with Solar Backscatter Ultra Violet-2 (SBUV-2) satellite data. We applied that ozone LBC to the CMAQ's upper layers and yield more reasonable O3 prediction than that with static LBC comparing with the aircraft data in Discover-AQ Colorado campaign. NGAC's aerosol LBC also improved the PM2.5 prediction with more realistic background aerosols. The bi-direction NH3 emission used in CMAQ also help reduce the NH3 and nitrate under-prediction issue. During summer 2014, strong wildfires occurred in northwestern USA, and we used the US Forest Service's BlueSky fire emission with HMS fire counts to drive CMAQ and tested the difference of day-1 and day-2 fire emission estimation. Other related issues were also discussed.

Dynamic evaluation of the CMAQv5.0 modeling system: Assessing the model’s ability to simulate ozone changes due to NOx emission reductions

EPA Science Inventory

Regional air quality models are frequently used for regulatory applications to predict changes in air quality due to changes in emissions or changes in meteorology. Dynamic model evaluation is thus an important step in establishing credibility in the model predicted pollutant re...

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Imaging characteristics of photogrammetric camera systems

USGS Publications Warehouse

Welch, R.; Halliday, J.

1973-01-01

In view of the current interest in high-altitude and space photographic systems for photogrammetric mapping, the United States Geological Survey (U.S.G.S.) undertook a comprehensive research project designed to explore the practical aspects of applying the latest image quality evaluation techniques to the analysis of such systems. The project had two direct objectives: (1) to evaluate the imaging characteristics of current U.S.G.S. photogrammetric camera systems; and (2) to develop methodologies for predicting the imaging capabilities of photogrammetric camera systems, comparing conventional systems with new or different types of systems, and analyzing the image quality of photographs. Image quality was judged in terms of a number of evaluation factors including response functions, resolving power, and the detectability and measurability of small detail. The limiting capabilities of the U.S.G.S. 6-inch and 12-inch focal length camera systems were established by analyzing laboratory and aerial photographs in terms of these evaluation factors. In the process, the contributing effects of relevant parameters such as lens aberrations, lens aperture, shutter function, image motion, film type, and target contrast procedures for analyzing image quality and predicting and comparing performance capabilities. ?? 1973.

Predictive monitoring and diagnosis of periodic air pollution in a subway station.

PubMed

Kim, YongSu; Kim, MinJung; Lim, JungJin; Kim, Jeong Tai; Yoo, ChangKyoo

2010-11-15

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models--global and seasonal--were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources. Copyright © 2010 Elsevier B.V. All rights reserved.

APPLICATION OF A WATER QUALITY ASSESSMENT MODELING SYSTEM AT A SUPERFUND SITE

EPA Science Inventory

Water quality modeling and related exposure assessments at a Superfund site, Silver Bow Creek-Clark Fork River in Montana, demonstrate the capability to predict the fate of mining waste pollutants in the environment. inked assessment system--consisting of hydrology and erosion, r...

Nonstarch polysaccharides in wheat flour wire-cut cookie making.

PubMed

Guttieri, Mary J; Souza, Edward J; Sneller, Clay

2008-11-26

Nonstarch polysaccharides in wheat flour have significant capacity to affect the processing quality of wheat flour dough and the finished quality of wheat flour products. Most research has focused on the effects of arabinoxylans (AX) in bread making. This study found that water-extractable AX and arabinogalactan peptides can predict variation in pastry wheat quality as captured by the wire-cut cookie model system. The sum of water-extractable AX plus arabinogalactan was highly predictive of cookie spread factor. The combination of cookie spread factor and the ratio of water-extractable arabinose to xylose predicted peak force of the three-point bend test of cookie texture.

Flared landing approach flying qualities. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Weingarten, Norman C.; Berthe, Charles J., Jr.; Rynaski, Edmund G.; Sarrafian, Shahan K.

1986-01-01

An in-flight research study was conducted utilizing the USAF/Total In-Flight Simulator (TIFS) to investigate longitudinal flying qualities for the flared landing approach phase of flight. A consistent set of data were generated for: determining what kind of command response the pilot prefers/requires in order to flare and land an aircraft with precision, and refining a time history criterion that took into account all the necessary variables and the characteristics that would accurately predict flying qualities. Seven evaluation pilots participated representing NASA Langley, NASA Dryden, Calspan, Boeing, Lockheed, and DFVLR (Braunschweig, Germany). The results of the first part of the study provide guidelines to the flight control system designer, using MIL-F-8785-(C) as a guide, that yield the dynamic behavior pilots prefer in flared landings. The results of the second part provide the flying qualities engineer with a derived flying qualities predictive tool which appears to be highly accurate. This time-domain predictive flying qualities criterion was applied to the flight data as well as six previous flying qualities studies, and the results indicate that the criterion predicted the flying qualities level 81% of the time and the Cooper-Harper pilot rating, within + or - 1%, 60% of the time.

Flared landing approach flying qualities. Volume 1: Experiment design and analysis

NASA Technical Reports Server (NTRS)

Weingarten, Norman C.; Berthe, Charles J., Jr.; Rynaski, Edmund G.; Sarrafian, Shahan K.

1986-01-01

An inflight research study was conducted utilizing the USAF Total Inflight Simulator (TIFS) to investigate longitudinal flying qualities for the flared landing approach phase of flight. The purpose of the experiment was to generate a consistent set of data for: (1) determining what kind of commanded response the pilot prefers in order to flare and land an airplane with precision, and (2) refining a time history criterion that took into account all the necessary variables and their characteristics that would accurately predict flying qualities. The result of the first part provides guidelines to the flight control system designer, using MIL-F-8785-(C) as a guide, that yield the dynamic behavior pilots perfer in flared landings. The results of the second part provides the flying qualities engineer with a newly derived flying qualities predictive tool which appears to be highly accurate. This time domain predictive flying qualities criterion was applied to the flight data as well as six previous flying qualities studies, and the results indicate that the criterion predicted the flying qualities level 81% of the time and the Cooper-Harper pilot rating, within + or - 1, 60% of the time.

Contaminant Permeation in the Ionomer-Membrane Water Processor (IWP) System

NASA Technical Reports Server (NTRS)

Kelsey, Laura K.; Finger, Barry W.; Pasadilla, Patrick; Perry, Jay

2016-01-01

The Ionomer-membrane Water Processor (IWP) is a patented membrane-distillation based urine brine water recovery system. The unique properties of the IWP membrane pair limit contaminant permeation from the brine to the recovered water and purge gas. A paper study was conducted to predict volatile trace contaminant permeation in the IWP system. Testing of a large-scale IWP Engineering Development Unit (EDU) with urine brine pretreated with the International Space Station (ISS) pretreatment formulation was then conducted to collect air and water samples for quality analysis. Distillate water quality and purge air GC-MS results are presented and compared to predictions, along with implications for the IWP brine processing system.

Meteorological Processes Affecting Air Quality – Research and Model Development Needs

EPA Science Inventory

Meteorology modeling is an important component of air quality modeling systems that defines the physical and dynamical environment for atmospheric chemistry. The meteorology models used for air quality applications are based on numerical weather prediction models that were devel...

Video quality assessment using motion-compensated temporal filtering and manifold feature similarity

PubMed Central

Yu, Mei; Jiang, Gangyi; Shao, Feng; Peng, Zongju

2017-01-01

Well-performed Video quality assessment (VQA) method should be consistent with human visual systems for better prediction accuracy. In this paper, we propose a VQA method using motion-compensated temporal filtering (MCTF) and manifold feature similarity. To be more specific, a group of frames (GoF) is first decomposed into a temporal high-pass component (HPC) and a temporal low-pass component (LPC) by MCTF. Following this, manifold feature learning (MFL) and phase congruency (PC) are used to predict the quality of temporal LPC and temporal HPC respectively. The quality measures of the LPC and the HPC are then combined as GoF quality. A temporal pooling strategy is subsequently used to integrate GoF qualities into an overall video quality. The proposed VQA method appropriately processes temporal information in video by MCTF and temporal pooling strategy, and simulate human visual perception by MFL. Experiments on publicly available video quality database showed that in comparison with several state-of-the-art VQA methods, the proposed VQA method achieves better consistency with subjective video quality and can predict video quality more accurately. PMID:28445489

Mathematical model for prediction of efficiency indicators of educational activity in high school

NASA Astrophysics Data System (ADS)

Tikhonova, O. M.; Kushnikov, V. A.; Fominykh, D. S.; Rezchikov, A. F.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.

2018-05-01

The quality of high school is a current problem all over the world. The paper presents the system dedicated to predicting the accreditation indicators of technical universities based on J. Forrester mechanism of system dynamics. The mathematical model is developed for prediction of efficiency indicators of the educational activity and is based on the apparatus of nonlinear differential equations.

Predicting Observer Training Satisfaction and Certification

ERIC Educational Resources Information Center

Bell, Courtney A.; Jones, Nathan D.; Lewis, Jennifer M.; Liu, Shuangshuang

2013-01-01

The last decade produced numerous studies that show that students learn more from high-quality teachers than they do from lower quality teachers. If instruction is to improve through the use of more rigorous teacher evaluation systems, the implementation of these systems must provide consistent and interpretable information about which aspects of…

ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...

Structure Prediction and Analysis of Neuraminidase Sequence Variants

ERIC Educational Resources Information Center

Thayer, Kelly M.

2016-01-01

Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

Impact of inherent meteorology uncertainty on air quality model predictions

EPA Science Inventory

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

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.

Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

PubMed

Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

2017-07-01

The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Web-Based Decision Support System for Assessing Regional Water-Quality Conditions and Management Actions

NASA Astrophysics Data System (ADS)

Booth, N. L.; Everman, E.; Kuo, I.; Sprague, L.; Murphy, L.

2011-12-01

A new web-based decision support system has been developed as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Program's (NAWQA) effort to provide ready access to Spatially Referenced Regressions On Watershed attributes (SPARROW) results of stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via an intuitive graphical user interface with a map-based display. The SPARROW Decision Support System (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions, distribution of nutrient sources, nutrient delivery to downstream waterbodies, and simulations of altered nutrient inputs including atmospheric and agricultural sources. The DSS offers other features for analysis including various background map layers, model output exports, and the ability to save and share prediction scenarios. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. The underlying modeling framework and server infrastructure illustrate innovations in the information technology and geosciences fields for delivering SPARROW model predictions over the web by performing intensive model computations and map visualizations of the predicted conditions within the stream network.

Interpretation of fingerprint image quality features extracted by self-organizing maps

NASA Astrophysics Data System (ADS)

Danov, Ivan; Olsen, Martin A.; Busch, Christoph

2014-05-01

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

A Quality Classification System for Young Hardwood Trees - The First Step in Predicting Future Products

Treesearch

David L. Sonderman; Robert L. Brisbin

1978-01-01

Forest managers have no objective way to determine the relative value of culturally treated forest stands in terms of product potential. This paper describes the first step in the development of a quality classification system based on the measurement of individual tree characteristics for young hardwood stands.

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Implementing subgrid-scale cloudiness into the Model for Prediction Across Scales-Atmosphere (MPAS-A) for next generation global air quality modeling

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable seamless modeling of air quality from global to regional to (eventually) local scales. State of the science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) mo...

Surrogate Analysis and Index Developer (SAID) tool

USGS Publications Warehouse

Domanski, Marian M.; Straub, Timothy D.; Landers, Mark N.

2015-10-01

The regression models created in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water quality and associated engineering and ecological management decisions.

Relationships of cotton fiber properties to ring-spun yarn quality on selected High Plains cottons

USDA-ARS?s Scientific Manuscript database

The objective of this research was to evaluate the adequacy of High Volume Instruement (HVI) and Advanced Fiber Information System (AFIS) fiber quality parameters for predicting quality parameters of ring-spun yarns considering differences in harvest method. Fiber properties measured using the HVI (...

Water Quality Projects Summary for the Mid-Columbia and Cumberland River Systems

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

Stewart, Kevin M.; Witt, Adam M.; Hadjerioua, Boualem

Scheduling and operational control of hydropower systems is accompanied with a keen awareness of the management of water use, environmental effects, and policy, especially within the context of strict water rights policy and generation maximization. This is a multi-objective problem for many hydropower systems, including the Cumberland and Mid-Columbia river systems. Though each of these two systems have distinct operational philosophies, hydrologic characteristics, and system dynamics, they both share a responsibility to effectively manage hydropower and the environment, which requires state-of-the art improvements in the approaches and applications for water quality modeling. The Department of Energy and Oak Ridge Nationalmore » Laboratory have developed tools for total dissolved gas (TDG) prediction on the Mid-Columbia River and a decision-support system used for hydropower generation and environmental optimization on the Cumberland River. In conjunction with IIHR - Hydroscience & Engineering, The University of Iowa and University of Colorado s Center for Advanced Decision Support for Water and Environmental Systems (CADSWES), ORNL has managed the development of a TDG predictive methodology at seven dams along the Mid-Columbia River and has enabled the ability to utilize this methodology for optimization of operations at these projects with the commercially available software package Riverware. ORNL has also managed the collaboration with Vanderbilt University and Lipscomb University to develop a state-of-the art method for reducing high-fidelity water quality modeling results into surrogate models which can be used effectively within the context of optimization efforts to maximize generation for a reservoir system based on environmental and policy constraints. The novel contribution of these efforts is the ability to predict water quality conditions with simplified methodologies at the same level of accuracy as more complex and resource intensive computing methods. These efforts were designed to incorporate well into existing hydropower and reservoir system scheduling models, with runtimes that are comparable to existing software tools. In addition, the transferability of these tools to assess other systems is enhanced due the use of simplistic and easily attainable values for inputs, straight-forward calibration of predictive equation coefficients, and standardized comparison of traditionally familiar outputs.« less

Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle.

PubMed

Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L

2016-05-01

Product quality is a high priority for the beef industry because of its importance as a major driver of consumer demand for beef and the ability of the industry to improve it. A 2-prong approach based on implementation of a genetic program to improve eating quality and a system to communicate eating quality and increase the probability that consumers' eating quality expectations are met is outlined. The objectives of this study were 1) to identify the best carcass and meat composition traits to be used in a selection program to improve eating quality and 2) to develop a relatively small number of classes that reflect real and perceptible differences in eating quality that can be communicated to consumers and identify a subset of carcass and meat composition traits with the highest predictive accuracy across all eating quality classes. Carcass traits, meat composition, including Warner-Bratzler shear force (WBSF), intramuscular fat content (IMFC), trained sensory panel scores, and mineral composition traits of 1,666 Angus cattle were used in this study. Three eating quality indexes, EATQ1, EATQ2, and EATQ3, were generated by using different weights for the sensory traits (emphasis on tenderness, flavor, and juiciness, respectively). The best model for predicting eating quality explained 37%, 9%, and 19% of the variability of EATQ1, EATQ2, and EATQ3, and 2 traits, WBSF and IMFC, accounted for most of the variability explained by the best models. EATQ1 combines tenderness, juiciness, and flavor assessed by trained panels with 0.60, 0.15, and 0.25 weights, best describes North American consumers, and has a moderate heritability (0.18 ± 0.06). A selection index (I= -0.5[WBSF] + 0.3[IMFC]) based on phenotypic and genetic variances and covariances can be used to improve eating quality as a correlated trait. The 3 indexes (EATQ1, EATQ2, and EATQ3) were used to generate 3 equal (33.3%) low, medium, and high eating quality classes, and linear combinations of traits that best predict class membership were estimated using a predictive discriminant analysis. The best predictive model to classify new observations into low, medium, and high eating quality classes defined by the EATQ1 index included WBSF, IMFC, HCW, and marbling score and resulted in a total error rate of 47.06%, much lower than the 60.74% error rate when the prediction of class membership was based on the USDA grading system. The 2 best predictors were WBSF and IMFC, and they accounted for 97.2% of the variability explained by the best model.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Space Shuttle Program Primary Avionics Software System (PASS) Success Legacy - Quality and Reliability Date

NASA Technical Reports Server (NTRS)

Orr, James K.; Peltier, Daryl

2010-01-01

Thsi slide presentation reviews the avionics software system on board the space shuttle, with particular emphasis on the quality and reliability. The Primary Avionics Software System (PASS) provides automatic and fly-by-wire control of critical shuttle systems which executes in redundant computers. Charts given show the number of space shuttle flights vs time, PASS's development history, and other charts that point to the reliability of the system's development. The reliability of the system is also compared to predicted reliability.

Prediction of beef carcass and meat quality traits from factors characterising the rearing management system applied during the whole life of heifers.

PubMed

Soulat, J; Picard, B; Léger, S; Monteils, V

2018-06-01

In this study, four prediction models were developed by logistic regression using individual data from 96 heifers. Carcass and sensory rectus abdominis quality clusters were identified then predicted using the rearing factors data. The obtained models from rearing factors applied during the fattening period were compared to those characterising the heifers' whole life. The highest prediction power of carcass and meat quality clusters were obtained from the models considering the whole life, with success rates of 62.8% and 54.9%, respectively. Rearing factors applied during both pre-weaning and fattening periods influenced carcass and meat quality. According to models, carcass traits were improved when heifer's mother was older for first calving, calves ingested concentrates during pasture preceding weaning and heifers were slaughtered older. Meat traits were improved by the genetic of heifers' parents (i.e., calving ease and early muscularity) and when heifers were slaughtered older. A management of carcass and meat quality traits is possible at different periods of the heifers' life. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparative Study of the MTFA, ICS, and SQRI Image Quality Metrics for Visual Display Systems

DTIC Science & Technology

1991-09-01

reasonable image quality predictions across select display and viewing condition parameters. 101 6.0 REFERENCES American National Standard for Human Factors Engineering of ’ Visual Display Terminal Workstations . ANSI

Untrained consumer assessment of the eating quality of beef: 1. A single composite score can predict beef quality grades.

PubMed

Bonny, S P F; Hocquette, J-F; Pethick, D W; Legrand, I; Wierzbicki, J; Allen, P; Farmer, L J; Polkinghorne, R J; Gardner, G E

2017-08-01

Quantifying consumer responses to beef across a broad range of demographics, nationalities and cooking methods is vitally important for any system evaluating beef eating quality. On the basis of previous work, it was expected that consumer scores would be highly accurate in determining quality grades for beef, thereby providing evidence that such a technique could be used to form the basis of and eating quality grading system for beef. Following the Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia tasted cooked beef samples, then allocated them to a quality grade; unsatisfactory, good-every-day, better-than-every-day and premium. The consumers also scored beef samples for tenderness, juiciness, flavour-liking and overall-liking. The beef was sourced from all countries involved in the study and cooked by four different cooking methods and to three different degrees of doneness, with each experimental group in the study consisting of a single cooking doneness within a cooking method for each country. For each experimental group, and for the data set as a whole, a linear discriminant function was calculated, using the four sensory scores which were used to predict the quality grade. This process was repeated using two conglomerate scores which are derived from weighting and combining the consumer sensory scores for tenderness, juiciness, flavour-liking and overall-liking, the original meat quality 4 score (oMQ4) (0.4, 0.1, 0.2, 0.3) and current meat quality 4 score (cMQ4) (0.3, 0.1, 0.3, 0.3). From the results of these analyses, the optimal weightings of the sensory scores to generate an 'ideal meat quality 4 score (MQ4)' for each country were calculated, and the MQ4 values that reflected the boundaries between the four quality grades were determined. The oMQ4 weightings were far more accurate in categorising European meat samples than the cMQ4 weightings, highlighting that tenderness is more important than flavour to the consumer when determining quality. The accuracy of the discriminant analysis to predict the consumer scored quality grades was similar across all consumer groups, 68%, and similar to previously reported values. These results demonstrate that this technique, as used in the MSA system, could be used to predict consumer assessment of beef eating quality and therefore to underpin a commercial eating quality guarantee for all European consumers.

Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans.

PubMed

Tolessa, Kassaye; Rademaker, Michael; De Baets, Bernard; Boeckx, Pascal

2016-04-01

The growing global demand for specialty coffee increases the need for improved coffee quality assessment methods. Green bean coffee quality analysis is usually carried out by physical (e.g. black beans, immature beans) and cup quality (e.g. acidity, flavour) evaluation. However, these evaluation methods are subjective, costly, time consuming, require sample preparation and may end up in poor grading systems. This calls for the development of a rapid, low-cost, reliable and reproducible analytical method to evaluate coffee quality attributes and eventually chemical compounds of interest (e.g. chlorogenic acid) in coffee beans. The aim of this study was to develop a model able to predict coffee cup quality based on NIR spectra of green coffee beans. NIR spectra of 86 samples of green Arabica beans of varying quality were analysed. Partial least squares (PLS) regression method was used to develop a model correlating spectral data to cupping score data (cup quality). The selected PLS model had a good predictive power for total specialty cup quality and its individual quality attributes (overall cup preference, acidity, body and aftertaste) showing a high correlation coefficient with r-values of 90, 90,78, 72 and 72, respectively, between measured and predicted cupping scores for 20 out of 86 samples. The corresponding root mean square error of prediction (RMSEP) was 1.04, 0.22, 0.27, 0.24 and 0.27 for total specialty cup quality, overall cup preference, acidity, body and aftertaste, respectively. The results obtained suggest that NIR spectra of green coffee beans are a promising tool for fast and accurate prediction of coffee quality and for classifying green coffee beans into different specialty grades. However, the model should be further tested for coffee samples from different regions in Ethiopia and test if one generic or region-specific model should be developed. Copyright © 2015 Elsevier B.V. All rights reserved.

An In-Process Surface Roughness Recognition System in End Milling Operations

ERIC Educational Resources Information Center

Yang, Lieh-Dai; Chen, Joseph C.

2004-01-01

To develop an in-process quality control system, a sensor technique and a decision-making algorithm need to be applied during machining operations. Several sensor techniques have been used in the in-process prediction of quality characteristics in machining operations. For example, an accelerometer sensor can be used to monitor the vibration of…

Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Key Questions in Building Defect Prediction Models in Practice

NASA Astrophysics Data System (ADS)

Ramler, Rudolf; Wolfmaier, Klaus; Stauder, Erwin; Kossak, Felix; Natschläger, Thomas

The information about which modules of a future version of a software system are defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. However, constructing effective defect prediction models in an industrial setting involves a number of key questions. In this paper we discuss ten key questions identified in context of establishing defect prediction in a large software development project. Seven consecutive versions of the software system have been used to construct and validate defect prediction models for system test planning. Furthermore, the paper presents initial empirical results from the studied project and, by this means, contributes answers to the identified questions.

Operation quality assessment model for video conference system

NASA Astrophysics Data System (ADS)

Du, Bangshi; Qi, Feng; Shao, Sujie; Wang, Ying; Li, Weijian

2018-01-01

Video conference system has become an important support platform for smart grid operation and management, its operation quality is gradually concerning grid enterprise. First, the evaluation indicator system covering network, business and operation maintenance aspects was established on basis of video conference system's operation statistics. Then, the operation quality assessment model combining genetic algorithm with regularized BP neural network was proposed, which outputs operation quality level of the system within a time period and provides company manager with some optimization advice. The simulation results show that the proposed evaluation model offers the advantages of fast convergence and high prediction accuracy in contrast with regularized BP neural network, and its generalization ability is superior to LM-BP neural network and Bayesian BP neural network.

Review: The variability of the eating quality of beef can be reduced by predicting consumer satisfaction.

PubMed

Bonny, S P F; Hocquette, J-F; Pethick, D W; Legrand, I; Wierzbicki, J; Allen, P; Farmer, L J; Polkinghorne, R J; Gardner, G E

2018-04-02

The Meat Standards Australia (MSA) grading scheme has the ability to predict beef eating quality for each 'cut×cooking method combination' from animal and carcass traits such as sex, age, breed, marbling, hot carcass weight and fatness, ageing time, etc. Following MSA testing protocols, a total of 22 different muscles, cooked by four different cooking methods and to three different degrees of doneness, were tasted by over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia. Consumers scored the sensory characteristics (tenderness, flavor liking, juiciness and overall liking) and then allocated samples to one of four quality grades: unsatisfactory, good-every-day, better-than-every-day and premium. We observed that 26% of the beef was unsatisfactory. As previously reported, 68% of samples were allocated to the correct quality grades using the MSA grading scheme. Furthermore, only 7% of the beef unsatisfactory to consumers was misclassified as acceptable. Overall, we concluded that an MSA-like grading scheme could be used to predict beef eating quality and hence underpin commercial brands or labels in a number of European countries, and possibly the whole of Europe. In addition, such an eating quality guarantee system may allow the implementation of an MSA genetic index to improve eating quality through genetics as well as through management. Finally, such an eating quality guarantee system is likely to generate economic benefits to be shared along the beef supply chain from farmers to retailors, as consumers are willing to pay more for a better quality product.

An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

NASA Astrophysics Data System (ADS)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation of toxic substances. The capability of the Framework to address cumulative impacts will be demonstrated for freshwater ecosystem services and mountaintop mining.

An object programming based environment for protein secondary structure prediction.

PubMed

Giacomini, M; Ruggiero, C; Sacile, R

1996-01-01

The most frequently used methods for protein secondary structure prediction are empirical statistical methods and rule based methods. A consensus system based on object-oriented programming is presented, which integrates the two approaches with the aim of improving the prediction quality. This system uses an object-oriented knowledge representation based on the concepts of conformation, residue and protein, where the conformation class is the basis, the residue class derives from it and the protein class derives from the residue class. The system has been tested with satisfactory results on several proteins of the Brookhaven Protein Data Bank. Its results have been compared with the results of the most widely used prediction methods, and they show a higher prediction capability and greater stability. Moreover, the system itself provides an index of the reliability of its current prediction. This system can also be regarded as a basis structure for programs of this kind.

Research and application of a novel hybrid air quality early-warning system: A case study in China.

PubMed

Li, Chen; Zhu, Zhijie

2018-06-01

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency. Copyright © 2018 Elsevier B.V. All rights reserved.

Simplified APC for Space Shuttle applications. [Adaptive Predictive Coding for speech transmission

NASA Technical Reports Server (NTRS)

Hutchins, S. E.; Batson, B. H.

1975-01-01

This paper describes an 8 kbps adaptive predictive digital speech transmission system which was designed for potential use in the Space Shuttle Program. The system was designed to provide good voice quality in the presence of both cabin noise on board the Shuttle and the anticipated bursty channel. Minimal increase in size, weight, and power over the current high data rate system was also a design objective.

Do Standard Measures of Preschool Quality Used in Statewide Policy Predict School Readiness?

ERIC Educational Resources Information Center

Sabol, Terri J.; Pianta, Robert C.

2014-01-01

In the majority of states using Quality Rating and Improvement Systems (QRIS) to improve children's school readiness, the Early Childhood Environmental Rating Scale-Revised (ECERS-R) is a core assessment of preschool program quality and is central to QRIS metrics and incentive structures. The present study utilizes nationally representative data…

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

NASA Astrophysics Data System (ADS)

Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

2018-02-01

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

Modelling sewer sediment deposition, erosion, and transport processes to predict acute influent and reduce combined sewer overflows and CO(2) emissions.

PubMed

Mouri, Goro; Oki, Taikan

2010-01-01

Understanding of solids deposition, erosion, and transport processes in sewer systems has improved considerably in the past decade. This has provided guidance for controlling sewer solids and associated acute pollutants to protect the environment and improve the operation of wastewater systems. Although measures to decrease combined sewer overflow (CSO) events have reduced the amount of discharged pollution, overflows continue to occur during rainy weather in combined sewer systems. The solution lies in the amount of water allotted to various processes in an effluent treatment system, in impact evaluation of water quality and prediction technology, and in stressing the importance of developing a control technology. Extremely contaminated inflow has been a serious research subject, especially in connection with the influence of rainy weather on nitrogen and organic matter removal efficiency in wastewater treatment plants (WWTP). An intensive investigation of an extremely polluted inflow load to WWTP during rainy weather was conducted in the city of Matsuyama, the region used for the present research on total suspended solid (TSS) concentration. Since the inflow during rainy weather can be as much as 400 times that in dry weather, almost all sewers are unsettled and overflowing when a rain event is more than moderate. Another concern is the energy consumed by wastewater treatment; this problem has become important from the viewpoint of reducing CO(2) emissions and overall costs. Therefore, while establishing a prediction technology for the inflow water quality characteristics of a sewage disposal plant is an important priority, the development of a management/control method for an effluent treatment system that minimises energy consumption and CO(2) emissions due to water disposal is also a pressing research topic with regards to the quality of treated water. The procedure to improve water quality must make use of not only water quality and biotic criteria, but also modelling systems to enable the user to link the effect of changes in urban sewage systems with specific quality, energy consumption, CO(2) emission, and ecological improvements of the receiving water.

Predicting indoor pollutant concentrations, and applications to air quality management

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

Lorenzetti, David M.

Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptomsmore » such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.« less

Quantitative structure-activity relationship models that stand the test of time.

PubMed

Davis, Andrew M; Wood, David J

2013-04-01

The pharmaceutical industry is in a period of intense change. While this has many drivers, attrition through the development process continues to be an important pressure. The emerging definitions of "compound quality" that are based on retrospective analyses of developmental attrition have highlighted a new direction for medicinal chemistry and the paradigm of "quality at the point of design". The time has come for retrospective analyses to catalyze prospective action. Quality at the point of design places pressure on the quality of our predictive models. Empirical QSAR models when built with care provide true predictive control, but their accuracy and precision can be improved. Here we describe AstraZeneca's experience of automation in QSAR model building and validation, and how an informatics system can provide a step-change in predictive power to project design teams, if they choose to use it.

Binary classification of items of interest in a repeatable process

DOEpatents

Abell, Jeffrey A; Spicer, John Patrick; Wincek, Michael Anthony; Wang, Hui; Chakraborty, Debejyo

2015-01-06

A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.

A classification system for predicting pallet part quality from hardwood cants

Treesearch

E. Paul Craft; Kenneth R., Jr. Whitenack

1982-01-01

Producers who manufacture pallet parts from hardwood cants generally must purchase cants on the basis of existing structural timber grades that do not adequately reflect the quality of pallet parts produced from resawed cants. A system for classifying cants for pallet part production has been developed that more accurately reflects the parts grade mix that can be...

Assessing the Challenges Associated with Developing an Integrated Modeling Approach for Predicting and Managing Water Quality and Quantity from the Watershed through the Drinking Water Treatment System

EPA Science Inventory

Natural and Engineered water systems interact throughout watersheds (e.g., at water intakes, wastewater outfalls and water pipe breaks of all kinds), and while there is clearly a link between watershed activities and the quality of water entering the engineered environment, surfa...

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Prediction of passenger ride quality in a multifactor environment

NASA Technical Reports Server (NTRS)

Dempsey, T. K.; Leatherwood, J. D.

1976-01-01

A model being developed, permits the understanding and prediction of passenger discomfort in a multifactor environment with particular emphasis upon combined noise and vibration. The model has general applicability to diverse transportation systems and provides a means of developing ride quality design criteria as well as a diagnostic tool for identifying the vibration and/or noise stimuli causing discomfort. Presented are: (1) a review of the basic theoretical and mathematical computations associated with the model, (2) a discussion of methodological and criteria investigations for both the vertical and roll axes of vibration, (3) a description of within-axis masking of discomfort responses for the vertical axis, thereby allowing prediction of the total discomfort due to any random vertical vibration, (4) a discussion of initial data on between-axis masking, and (5) discussion of a study directed towards extension of the vibration model to the more general case of predicting ride quality in the combined noise and vibration environments.

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations

PubMed Central

Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge. PMID:27313604

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations.

PubMed

Castelli, Mauro; Manzoni, Luca; Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge.

JPL's Role in Advancing Earth System Science to Meet the Challenges of Climate and Environmental Change

NASA Technical Reports Server (NTRS)

Evans, Diane

2012-01-01

Objective 2.1.1: Improve understanding of and improve the predictive capability for changes in the ozone layer, climate forcing, and air quality associated with changes in atmospheric composition. Objective 2.1.2: Enable improved predictive capability for weather and extreme weather events. Objective 2.1.3: Quantify, understand, and predict changes in Earth s ecosystems and biogeochemical cycles, including the global carbon cycle, land cover, and biodiversity. Objective 2.1.4: Quantify the key reservoirs and fluxes in the global water cycle and assess water cycle change and water quality. Objective 2.1.5: Improve understanding of the roles of the ocean, atmosphere, land and ice in the climate system and improve predictive capability for its future evolution. Objective 2.1.6: Characterize the dynamics of Earth s surface and interior and form the scientific basis for the assessment and mitigation of natural hazards and response to rare and extreme events. Objective 2.1.7: Enable the broad use of Earth system science observations and results in decision-making activities for societal benefits.

Speech Clarity Index (Ψ): A Distance-Based Speech Quality Indicator and Recognition Rate Prediction for Dysarthric Speakers with Cerebral Palsy

NASA Astrophysics Data System (ADS)

Kayasith, Prakasith; Theeramunkong, Thanaruk

It is a tedious and subjective task to measure severity of a dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (Ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of Ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that Ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders’ vocabulary and reading comprehension outcomes

PubMed Central

Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L.; Giuliani, Sarah; Ingebrand, Sarah W.; McLean, Leigh; Morrison, Frederick J.

2014-01-01

We examined classrooms as complex systems that affect students’ literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students’ comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement. PMID:25400293

Analysis of the predictive qualities of betting odds and FIFA World Ranking: evidence from the 2006, 2010 and 2014 Football World Cups.

PubMed

Wunderlich, Fabian; Memmert, Daniel

2016-12-01

The present study aims to investigate the ability of a new framework enabling to derive more detailed model-based predictions from ranking systems. These were compared to predictions from the bet market including data from the World Cups 2006, 2010, and 2014. The results revealed that the FIFA World Ranking has essentially improved its predictive qualities compared to the bet market since the mode of calculation was changed in 2006. While both predictors were useful to obtain accurate predictions in general, the world ranking was able to outperform the bet market significantly for the World Cup 2014 and when the data from the World Cups 2010 and 2014 were pooled. Our new framework can be extended in future research to more detailed prediction tasks (i.e., predicting the final scores of a match or the tournament progress of a team).

Use of Air Quality Observations by the National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Kondragunta, S.; Ruminski, M.; Tong, D.; Pan, L.; Huang, J. P.; Shafran, P.; Huang, H. C.; Dickerson, P.; Upadhayay, S.

2015-12-01

The National Air Quality Forecast Capability (NAQFC) operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust for continental U.S. are available at http://airquality.weather.gov/. NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions are combined with the Community Multiscale Air Quality (CMAQ) model to produce the ozone predictions and test fine particulate matter (PM2.5) predictions. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model provides smoke and dust predictions. Air quality observations constrain emissions used by NAQFC predictions. NAQFC NOx emissions from mobile sources were updated using National Emissions Inventory (NEI) projections for year 2012. These updates were evaluated over large U.S. cities by comparing observed changes in OMI NO2 observations and NOx measured by surface monitors. The rate of decrease in NOx emission projections from year 2005 to year 2012 is in good agreement with the observed changes over the same period. Smoke emissions rely on the fire locations detected from satellite observations obtained from NESDIS Hazard Mapping System (HMS). Dust emissions rely on a climatology of areas with a potential for dust emissions based on MODIS Deep Blue aerosol retrievals. Verification of NAQFC predictions uses AIRNow compilation of surface measurements for ozone and PM2.5. Retrievals of smoke from GOES satellites are used for verification of smoke predictions. Retrievals of dust from MODIS are used for verification of dust predictions. In summary, observations are the basis for the emissions inputs for NAQFC, they are critical for evaluation of performance of NAQFC predictions, and furthermore they are used in real-time testing of bias correction of PM2.5 predictions, as we continue to work on improving modeling and emissions important for representation of PM2.5.

Predicting Risk from Radon in Source Waters from Water Quality Parameters

EPA Science Inventory

Overall, 47 groundwater samples were collected from 45 small community water systems (CWSs) and analyzed for radon and other water quality constituents. In general, groundwater from unconsolidated deposits and sedimentary rocks had lower average radon levels (ranging from 223 to...

Low-delay predictive audio coding for the HIVITS HDTV codec

NASA Astrophysics Data System (ADS)

McParland, A. K.; Gilchrist, N. H. C.

1995-01-01

The status of work relating to predictive audio coding, as part of the European project on High Quality Video Telephone and HD(TV) Systems (HIVITS), is reported. The predictive coding algorithm is developed, along with six-channel audio coding and decoding hardware. Demonstrations of the audio codec operating in conjunction with the video codec, are given.

Evaluating imaging quality between different ghost imaging systems based on the coherent-mode representation

NASA Astrophysics Data System (ADS)

Shen, Qian; Bai, Yanfeng; Shi, Xiaohui; Nan, Suqin; Qu, Lijie; Li, Hengxing; Fu, Xiquan

2017-07-01

The difference in imaging quality between different ghost imaging schemes is studied by using coherent-mode representation of partially coherent fields. It is shown that the difference mainly relies on the distribution changes of the decomposition coefficients of the object imaged when the light source is fixed. For a new-designed imaging scheme, we only need to give the distribution of the decomposition coefficients and compare them with that of the existing imaging system, thus one can predict imaging quality. By choosing several typical ghost imaging systems, we theoretically and experimentally verify our results.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment.

PubMed

Mirroshandel, Seyed Abolghasem; Ghasemian, Fatemeh; Monji-Azad, Sara

2016-12-01

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection. The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process. In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time. A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALIC PIPES

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfect...

Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

2015-12-01

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

Towards an agent based traffic regulation and recommendation system for the on-road air quality control.

PubMed

Sadiq, Abderrahmane; El Fazziki, Abdelaziz; Ouarzazi, Jamal; Sadgal, Mohamed

2016-01-01

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system's traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

NASA Technical Reports Server (NTRS)

Vermillion, Stephanie C.; Raqueno, Rolando; Simmons, Rulon

2001-01-01

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

A deep learning approach for predicting the quality of online health expert question-answering services.

PubMed

Hu, Ze; Zhang, Zhan; Yang, Haiqin; Chen, Qing; Zuo, Decheng

2017-07-01

Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users' votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrating modal-based NDE techniques and bridge management systems using quality management

NASA Astrophysics Data System (ADS)

Sikorsky, Charles S.

1997-05-01

The intent of bridge management systems is to help engineers and managers determine when and where to spend bridge funds such that commerce and the motoring public needs are satisfied. A major shortcoming which states are experiencing is the NBIS data available is insufficient to perform certain functions required by new bridge management systems, such as modeling bridge deterioration and predicting costs. This paper will investigate how modal based nondestructive damage evaluation techniques can be integrated into bridge management using quality management principles. First, quality from the manufacturing perspective will be summarized. Next, the implementation of quality management in design and construction will be reinterpreted for bridge management. Based on this, a theory of approach will be formulated to improve the productivity of a highway transportation system.

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALLIC PIPES (PRESENTATION)

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfecta...

PREDICTING CHLORINE RESIDUAL LOSSES IN UNLINED METALLIC PIPES (POSTER)

EPA Science Inventory

There is substantial evidence that as water moves through a water distribution system its quality can deteriorate through interactions between the bulk phase and the pipe wall. One of the most serious aspects of water quality deterioration, in a network, is the loss of disinfect...

PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES (PRESENTATION)

EPA Science Inventory

Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES

EPA Science Inventory

Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

The Effect of Nylon and Polyester Peel Ply Surface Preparation on the Bond Quality of Composite Laminates

NASA Astrophysics Data System (ADS)

Moench, Molly K.

The preparation of the surfaces to be bonded is critical to the success of composite bonds. Peel ply surface preparation is attractive from a manufacturing and quality assurance standpoint, but is a well known example of the extremely system-specific nature of composite bonds. This study examined the role of the surface energy, morphology, and chemistry left by peel ply removal in resulting bond quality. It also evaluated the use of contact angle surface energy measurement techniques for predicting the resulting bond quality of a prepared surface. The surfaces created by preparing three aerospace fiber-reinforced composite prepregs were compared when prepared with a nylon vs a polyester peel ply. The prepared surfaces were characterized with contact angle measurements with multiple fluids, scanning electron microscopy (SEM), and x-ray electron spectroscopy. The laminates were bonded with aerospace grade film adhesives. Bond quality was assessed via double cantilever beam testing followed by optical and scanning electron microscopy of the fracture surfaces.The division was clear between strong bonds (GIC of 600- 1000J/m2 and failure in cohesion) and weak bonds (GIC of 80-400J/m2 and failure in adhesion). All prepared laminates showed the imprint of the peel ply texture and evidence of peel ply remnants after fabric removal, either through SEM or XPS. Within an adhesive system, large amounts of SEM-visible peel ply material transfer correlated with poor bond quality and cleaner surfaces with higher bond quality. The both sides of failed weak bonds showed evidence of peel ply remnants under XPS, showing that at least some failure is occurring through the remnants. The choice of adhesive was found to be significant. AF 555 adhesive was more tolerant of peel ply contamination than MB 1515-3. Although the bond quality results varied substantially between tested combinations, the total surface energies of all prepared surfaces were very similar. Single fluid contact angle measurements/water break tests were therefore not predictive of bond quality, and are recommended against. The multiple fluids used allowed the construction of wettability envelopes, a more detailed look at the surface energy profile. The envelopes of nylon and polyester prepared systems were noticeably different, but while potentially useful for detecting changes or errors in surface preparation of known systems, they were not valid for predicting bond quality in new systems. Ultimately, it was determined that wetting is a necessary but not sufficient condition for bonding.

Development and status of data quality assurance program at NASA Langley research center: Toward national standards

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.

1996-01-01

As part of a continuing effort to re-engineer the wind tunnel testing process, a comprehensive data quality assurance program is being established at NASA Langley Research Center (LaRC). The ultimate goal of the program is routing provision of tunnel-to-tunnel reproducibility with total uncertainty levels acceptable for test and evaluation of civilian transports. The operational elements for reaching such levels of reproducibility are: (1) statistical control, which provides long term measurement uncertainty predictability and a base for continuous improvement, (2) measurement uncertainty prediction, which provides test designs that can meet data quality expectations with the system's predictable variation, and (3) national standards, which provide a means for resolving tunnel-to-tunnel differences. The paper presents the LaRC design for the program and discusses the process of implementation.

Binary classification of items of interest in a repeatable process

DOEpatents

Abell, Jeffrey A.; Spicer, John Patrick; Wincek, Michael Anthony; Wang, Hui; Chakraborty, Debejyo

2014-06-24

A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.

Breast cancer mammographic diagnosis performance in a public health institution: a retrospective cohort study.

PubMed

Mello, Juliana M R B; Bittelbrunn, Fernando P; Rockenbach, Marcio A B C; May, Guilherme G; Vedolin, Leonardo M; Kruger, Marilia S; Soldatelli, Matheus D; Zwetsch, Guilherme; de Miranda, Gabriel T F; Teixeira, Saone I P; Arruda, Bruna S

2017-12-01

To evaluate the quality assurance of mammography results at a reference institution for the diagnosis and treatment of breast cancer in southern Brazil, based on the BIRADS (Breast Imaging Reporting and Data System) 5th edition recommendations for auditing purposes. Retrospective cohort and cross-sectional study with 4502 patients (9668 mammographies)) who underwent at least one or both breast mammographies throughout 2013 at a regional public hospital, linked to a federal public university. The results were followed until 31 December 2014, including true positives (TPs), true negatives (TNs), false positives (FPs), false negatives (FNs), positive predictive values (PPVs), negative predictive value (NPV), sensitivity and specificity, with a confidence interval of 95%. The study showed high quality assurance, particularly regarding sensitivity (90.22%) and specificity (92.31%). The overall positive predictive value (PPV) was 65.35%, and the negative predictive value (NPV) was 98.32%. The abnormal interpretation rate (recall rate) was 12.26%. The results are appropriate when compared to the values proposed by the BIRADS 5th edition. Additionally, the study provided self-reflection considering our radiological practice, which is essential for improvements and collaboration regarding breast cancer detection. It may stimulate better radiological practice performance and continuing education, despite possible infrastructure and facility limitations. • Accurate quality performance rates are possible despite financial and governmental limitations. • Low-income institutions should develop standardised teamwork to improve radiological practice. • Regular mammography audits may help to increase the quality of public health systems.

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

PubMed

Aris-Brosou, Stephane; Kim, James; Li, Li; Liu, Hui

2018-05-15

Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement. ©Stephane Aris-Brosou, James Kim, Li Li, Hui Liu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 15.05.2018.

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning

PubMed Central

Kim, James; Li, Li; Liu, Hui

2018-01-01

Background Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. Objective The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. Methods QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. Results The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. Conclusions This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement. PMID:29764796

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

NASA Astrophysics Data System (ADS)

Nwankwo, Obioma; Sihono, Dwi Seno K.; Schneider, Frank; Wenz, Frederik

2014-09-01

Introduction: the quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. Materials and method: one hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. Results: a voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. Conclusion: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

Automatic evidence quality prediction to support evidence-based decision making.

PubMed

Sarker, Abeed; Mollá, Diego; Paris, Cécile

2015-06-01

Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care. Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades. Following an in-depth analysis of the usefulness of features (e.g., publication types of articles), they are extracted from the text via rule-based approaches and from the meta-data associated with the articles, and then applied in the supervised classification model. We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers, and introduce a simple evaluation metric called average error distance (AED) that simplifies the comparison of systems. We also perform elaborate human evaluations to compare the performance of our system against human judgments. We test and evaluate our approaches on a publicly available, specialised, annotated corpus containing 1132 evidence-based recommendations. Our rule-based approach performs exceptionally well at the automatic extraction of publication types of articles, with F-scores of up to 0.99 for high-quality publication types. For evidence quality classification, our approach obtains an accuracy of 63.84% and an AED of 0.271. The human evaluations show that the performance of our system, in terms of AED and accuracy, is comparable to the performance of humans on the same data. The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance. Our overall classification approach and evaluation technique are also highly portable and can be used for various evidence grading scales. Copyright © 2015 Elsevier B.V. All rights reserved.

Forecasting PM10 in metropolitan areas: Efficacy of neural networks.

PubMed

Fernando, H J S; Mammarella, M C; Grandoni, G; Fedele, P; Di Marco, R; Dimitrova, R; Hyde, P

2012-04-01

Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

EPA Science Inventory

The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

Progress on Implementing Additional Physics Schemes into MPAS-A v5.1 for Next Generation Air Quality Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (USEPA) has a team of scientists developing a next generation air quality modeling system employing the Model for Prediction Across Scales – Atmosphere (MPAS-A) as its meteorological foundation. Several preferred physics schemes and ...

Modeling the cadmium balance in Australian agricultural systems in view of potential impacts on food and water quality.

PubMed

de Vries, W; McLaughlin, M J

2013-09-01

The historical build up and future cadmium (Cd) concentrations in top soils and in crops of four Australian agricultural systems are predicted with a mass balance model, focusing on the period 1900-2100. The systems include a rotation of dryland cereals, a rotation of sugarcane and peanuts/soybean, intensive dairy production and intensive horticulture. The input of Cd to soil is calculated from fertilizer application and atmospheric deposition and also examines options including biosolid and animal manure application in the sugarcane rotation and dryland cereal production systems. Cadmium output from the soil is calculated from leaching to deeper horizons and removal with the harvested crop or with livestock products. Parameter values for all Cd fluxes were based on a number of measurements on Australian soil-plant systems. In the period 1900-2000, soil Cd concentrations were predicted to increase on average between 0.21 mg kg(-1) in dryland cereals, 0.42 mg kg(-1) in intensive agriculture and 0.68 mg kg(-1) in dairy production, which are within the range of measured increases in soils in these systems. Predicted soil concentrations exceed critical soil Cd concentrations, based on food quality criteria for Cd in crops during the simulation period in clay-rich soils under dairy production and intensive horticulture. Predicted dissolved Cd concentrations in soil pore water exceed a ground water quality criterion of 2 μg l(-1) in light textured soils, except for the sugarcane rotation due to large water leaching fluxes. Results suggest that the present fertilizer Cd inputs in Australia are in excess of the long-term critical loads in heavy-textured soils for dryland cereals and that all other systems are at low risk. Calculated critical Cd/P ratios in P fertilizers vary from <50 to >1000 mg Cd kg P(-1) for the different soil, crop and environmental conditions applied. Copyright © 2013 Elsevier B.V. All rights reserved.

Predicting non-stationary algal dynamics following changes in hydrometeorological conditions using data assimilation techniques

NASA Astrophysics Data System (ADS)

Kim, S.; Seo, D. J.

2017-12-01

When water temperature (TW) increases due to changes in hydrometeorological conditions, the overall ecological conditions change in the aquatic system. The changes can be harmful to human health and potentially fatal to fish habitat. Therefore, it is important to assess the impacts of thermal disturbances on in-stream processes of water quality variables and be able to predict effectiveness of possible actions that may be taken for water quality protection. For skillful prediction of in-stream water quality processes, it is necessary for the watershed water quality models to be able to reflect such changes. Most of the currently available models, however, assume static parameters for the biophysiochemical processes and hence are not able to capture nonstationaries seen in water quality observations. In this work, we assess the performance of the Hydrological Simulation Program-Fortran (HSPF) in predicting algal dynamics following TW increase. The study area is located in the Republic of Korea where waterway change due to weir construction and drought concurrently occurred around 2012. In this work we use data assimilation (DA) techniques to update model parameters as well as the initial condition of selected state variables for in-stream processes relevant to algal growth. For assessment of model performance and characterization of temporal variability, various goodness-of-fit measures and wavelet analysis are used.

Development of a multi-ensemble Prediction Model for China

NASA Astrophysics Data System (ADS)

Brasseur, G. P.; Bouarar, I.; Petersen, A. K.

2016-12-01

As part of the EU-sponsored Panda and MarcoPolo Projects, a multi-model prediction system including 7 models has been developed. Most regional models use global air quality predictions provided by the Copernicus Atmospheric Monitoring Service and downscale the forecast at relatively high spatial resolution in eastern China. The paper will describe the forecast system and show examples of forecasts produced for several Chinese urban areas and displayed on a web site developed by the Dutch Meteorological service. A discussion on the accuracy of the predictions based on a detailed validation process using surface measurements from the Chinese monitoring network will be presented.

The Predictive Validity of Interim Assessment Scores Based on the Full-Information Bifactor Model for the Prediction of End-of-Grade Test Performance

ERIC Educational Resources Information Center

Immekus, Jason C.; Atitya, Ben

2016-01-01

Interim tests are a central component of district-wide assessment systems, yet their technical quality to guide decisions (e.g., instructional) has been repeatedly questioned. In response, the study purpose was to investigate the validity of a series of English Language Arts (ELA) interim assessments in terms of dimensionality and prediction of…

[Development of whole process quality control and management system of traditional Chinese medicine decoction pieces based on traditional Chinese medicine quality tree].

PubMed

Yu, Wen-Kang; Dong, Ling; Pei, Wen-Xuan; Sun, Zhi-Rong; Dai, Jun-Dong; Wang, Yun

2017-12-01

The whole process quality control and management of traditional Chinese medicine (TCM) decoction pieces is a system engineering, involving the base environment, seeds and seedlings, harvesting, processing and other multiple steps, so the accurate identification of factors in TCM production process that may induce the quality risk, as well as reasonable quality control measures are very important. At present, the concept of quality risk is mainly concentrated in the aspects of management and regulations, etc. There is no comprehensive analysis on possible risks in the quality control process of TCM decoction pieces, or analysis summary of effective quality control schemes. A whole process quality control and management system for TCM decoction pieces based on TCM quality tree was proposed in this study. This system effectively combined the process analysis method of TCM quality tree with the quality risk management, and can help managers to make real-time decisions while realizing the whole process quality control of TCM. By providing personalized web interface, this system can realize user-oriented information feedback, and was convenient for users to predict, evaluate and control the quality of TCM. In the application process, the whole process quality control and management system of the TCM decoction pieces can identify the related quality factors such as base environment, cultivation and pieces processing, extend and modify the existing scientific workflow according to their own production conditions, and provide different enterprises with their own quality systems, to achieve the personalized service. As a new quality management model, this paper can provide reference for improving the quality of Chinese medicine production and quality standardization. Copyright© by the Chinese Pharmaceutical Association.

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.

Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

NASA Astrophysics Data System (ADS)

Sivaraos; Khalim, A. Z.; Salleh, M. S.; Sivakumar, D.; Kadirgama, K.

2018-03-01

Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process.

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

NASA Technical Reports Server (NTRS)

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily;

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot-induced oscillations (PIO) is formulated. Finally, a model-based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot induced oscillations is formulated. Finally, a model based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Risk Factors Analysis and Death Prediction in Some Life-Threatening Ailments Using Chi-Square Case-Based Reasoning (χ2 CBR) Model.

PubMed

Adeniyi, D A; Wei, Z; Yang, Y

2018-01-30

A wealth of data are available within the health care system, however, effective analysis tools for exploring the hidden patterns in these datasets are lacking. To alleviate this limitation, this paper proposes a simple but promising hybrid predictive model by suitably combining the Chi-square distance measurement with case-based reasoning technique. The study presents the realization of an automated risk calculator and death prediction in some life-threatening ailments using Chi-square case-based reasoning (χ 2 CBR) model. The proposed predictive engine is capable of reducing runtime and speeds up execution process through the use of critical χ 2 distribution value. This work also showcases the development of a novel feature selection method referred to as frequent item based rule (FIBR) method. This FIBR method is used for selecting the best feature for the proposed χ 2 CBR model at the preprocessing stage of the predictive procedures. The implementation of the proposed risk calculator is achieved through the use of an in-house developed PHP program experimented with XAMP/Apache HTTP server as hosting server. The process of data acquisition and case-based development is implemented using the MySQL application. Performance comparison between our system, the NBY, the ED-KNN, the ANN, the SVM, the Random Forest and the traditional CBR techniques shows that the quality of predictions produced by our system outperformed the baseline methods studied. The result of our experiment shows that the precision rate and predictive quality of our system in most cases are equal to or greater than 70%. Our result also shows that the proposed system executes faster than the baseline methods studied. Therefore, the proposed risk calculator is capable of providing useful, consistent, faster, accurate and efficient risk level prediction to both the patients and the physicians at any time, online and on a real-time basis.

Automated assessment of cognitive health using smart home technologies.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2013-01-01

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Predicting Air Quality at First Ingress into Vehicles Visiting the International Space Station.

PubMed

Romoser, Amelia A; Scully, Robert R; Limero, Thomas F; De Vera, Vanessa; Cheng, Patti F; Hand, Jennifer J; James, John T; Ryder, Valerie E

2017-02-01

NASA regularly performs ground-based offgas tests (OGTs), which allow prediction of accumulated volatile pollutant concentrations at first entry on orbit, on whole modules and vehicles scheduled to connect to the International Space Station (ISS). These data guide crew safety operations and allow for estimation of ISS air revitalization systems impact from additional pollutant load. Since volatiles released from vehicle, module, and payload materials can affect crew health and performance, prediction of first ingress air quality is important. To assess whether toxicological risk is typically over or underpredicted, OGT and first ingress samples from 10 vehicles and modules were compared. Samples were analyzed by gas chromatography and gas chromatography-mass spectrometry. The rate of pollutant accumulation was extrapolated over time. Ratios of analytical values and Spacecraft Maximum Allowable Concentrations were used to predict total toxicity values (T-values) at first entry. Results were also compared by compound. Frequently overpredicted was 2-butanone (9/10), whereas propanal (6/10) and ethanol (8/10) were typically underpredicted, but T-values were not substantially affected. Ingress sample collection delay (estimated by octafluoropropane introduced from ISS atmosphere) and T-value prediction accuracy correlated well (R2 = 0.9008), highlighting the importance of immediate air sample collection and accounting for ISS air dilution. Importantly, T-value predictions were conservative 70% of the time. Results also suggest that T-values can be normalized to octafluoropropane levels to adjust for ISS air dilution at first ingress. Finally, OGT and ingress sampling has allowed small leaks in vehicle fluid systems to be recognized and addressed.Romoser AA, Scully RR, Limero TF, De Vera V, Cheng PF, Hand JJ, James JT, Ryder VE. Predicting air quality at first ingress into vehicles visiting the International Space Station. Aerosp Med Hum Perform. 2017; 88(2):104-113.

Evaluating the success of an emergency response medical information system.

PubMed

Petter, Stacie; Fruhling, Ann

2011-07-01

STATPack™ is an information system used to aid in the diagnosis of pathogens in hospitals and state public health laboratories. STATPack™ is used as a communication and telemedicine diagnosis tool during emergencies. This paper explores the success of this emergency response medical information system (ERMIS) using a well-known framework of information systems success developed by DeLone and McLean. Using an online survey, the entire population of STATPack™ users evaluated the success of the information system by considering system quality, information quality, system use, intention to use, user satisfaction, individual impact, and organizational impact. The results indicate that the overall quality of this ERMIS (i.e., system quality, information quality, and service quality) has a positive impact on both user satisfaction and intention to use the system. However, given the nature of ERMIS, overall quality does not necessarily predict use of the system. Moreover, the user's satisfaction with the information system positively affected the intention to use the system. User satisfaction, intention to use, and system use had a positive influence on the system's impact on the individual. Finally, the organizational impacts of the system were positively influenced by use of the system and the system's individual impact on the user. The results of the study demonstrate how to evaluate the success of an ERMIS as well as introduce potential changes in how one applies the DeLone and McLean success model in an emergency response medical information system context. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Beef quality grading using machine vision

NASA Astrophysics Data System (ADS)

Jeyamkondan, S.; Ray, N.; Kranzler, Glenn A.; Biju, Nisha

2000-12-01

A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.

An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

PubMed

Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

2015-11-01

This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.

Profit through predictability: The MRF difference at optimax

NASA Astrophysics Data System (ADS)

Light, Brandon

2007-05-01

In the manufacturing business, there is one product that matters, money. Whether making shoelaces or aircraft carriers a business that doesn't also make a profit doesn't stay around long. Being able to predict operational expenses is critical to determining a product's sale price. Priced too high a product won't sell, too low profit goes away. In the business of precision optics manufacturing, predictability has been often impossible or had large error bars. Manufacturing unpredictability made setting price a challenge. What if predictability could improve by changing the polishing process? Would a predictable, deterministic process lead to profit? Optimax Systems has experienced exactly that. Incorporating Magnetorheological Finishing (MRF) into its finishing process, Optimax saw parts categorized financially as "high risk" become a routine product of higher quality, delivered on time and within budget. Using actual production figures, this presentation will show how much incorporating MRF reduced costs, improved output and increased quality all at the same time.

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

PubMed

Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

2016-01-01

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

Real-time quality assurance testing using photonic techniques: Application to iodine water system

NASA Technical Reports Server (NTRS)

Arendale, W. F.; Hatcher, Richard; Garlington, Yadilett; Harwell, Jack; Everett, Tracey

1990-01-01

A feasibility study of the use of inspection systems incorporating photonic sensors and multivariate analyses to provide an instrumentation system that in real-time assures quality and that the system in control has been conducted. A system is in control when the near future of the product quality is predictable. Off-line chemical analyses can be used for a chemical process when slow kinetics allows time to take a sample to the laboratory and the system provides a recovery mechanism that returns the system to statistical control without intervention of the operator. The objective for this study has been the implementation of do-it-right-the-first-time and just-in-time philosophies. The Environment Control and Life Support Systems (ECLSS) water reclamation system that adds iodine for biocidal control is an ideal candidate for the study and implementation of do-it-right-the-first-time technologies.

Application of the Wind Erosion Prediction System in the AIRPACT regional air quality modeling framework

USDA-ARS?s Scientific Manuscript database

Wind erosion of soil is a major concern of the agricultural community as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion contribute to poor air quality, reduce visibility, and cause perturbations to regional radiation ...

Stressor-response modeling using the 2D water quality model and regression trees to predict chlorophyll-a in a reservoir system

USDA-ARS?s Scientific Manuscript database

In order to control algal blooms, stressor-response relationships between water quality metrics, environmental variables, and algal growth should be understood and modeled. Machine-learning methods were suggested to express stressor-response relationships found by application of mechanistic water qu...

An Evaluation of carbon monoxide emissions models and mobile source dispersion models applicable to Alaskan cities : final report

DOT National Transportation Integrated Search

1986-01-01

This report describes an investigation of state-of-the-art models for predicting the impact on air quality of additions or changes to a highway system identified by the U.S. Environmental Protection Agency as a "non-attainment area" for air quality s...

Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

NASA Technical Reports Server (NTRS)

Matthies, Larry; Grandjean, Pierrick

1993-01-01

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART I--METEOROLOGICAL PREDICTIONS. (R825260)

EPA Science Inventory

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperatur...

Modeling erosion from forest roads with WEPP

Treesearch

J. McFero Grace

2007-01-01

Forest roads can be major sources of soil erosion from forest watersheds. Sediments from forest roads are a concern due to their potential delivery to stream systems resulting in degradation of water quality. The Water Erosion Prediction Project (WEPP) was used to predict erosion from forest road components under different management practices. WEPP estimates are...

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

On-line prediction of yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score using the MARC beef carcass image analysis system.

PubMed

Shackelford, S D; Wheeler, T L; Koohmaraie, M

2003-01-01

The present experiment was conducted to evaluate the ability of the U.S. Meat Animal Research Center's beef carcass image analysis system to predict calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score under commercial beef processing conditions. In two commercial beef-processing facilities, image analysis was conducted on 800 carcasses on the beef-grading chain immediately after the conventional USDA beef quality and yield grades were applied. Carcasses were blocked by plant and observed calculated yield grade. The carcasses were then separated, with 400 carcasses assigned to a calibration data set that was used to develop regression equations, and the remaining 400 carcasses assigned to a prediction data set used to validate the regression equations. Prediction equations, which included image analysis variables and hot carcass weight, accounted for 90, 88, 90, 88, and 76% of the variation in calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score, respectively, in the prediction data set. In comparison, the official USDA yield grade as applied by online graders accounted for 73% of the variation in calculated yield grade. The technology described herein could be used by the beef industry to more accurately determine beef yield grades; however, this system does not provide an accurate enough prediction of marbling score to be used without USDA grader interaction for USDA quality grading.

Diagnostic Accuracy of Global Pharma Health Fund Minilab™ in Assessing Pharmacopoeial Quality of Antimicrobials.

PubMed

Pan, Hui; Ba-Thein, William

2018-01-01

Global Pharma Health Fund (GPHF) Minilab™, a semi-quantitative thin-layer chromatography (TLC)-based commercially available test kit, is widely used in drug quality surveillance globally, but its diagnostic accuracy is unclear. We investigated the diagnostic accuracy of Minilab system for antimicrobials, using high-performance liquid chromatography (HPLC) as reference standard. Following the Minilab protocols and the Pharmacopoeia of the People's Republic of China protocols, Minilab-TLC and HPLC were used to test five common antimicrobials (506 batches) for relative concentration of active pharmaceutical ingredients. The prevalence of poor-quality antimicrobials determined, respectively, by Minilab TLC and HPLC was amoxicillin (0% versus 14.9%), azithromycin (0% versus 17.4%), cefuroxime axetil (14.3% versus 0%), levofloxacin (0% versus 3.0%), and metronidazole (0% versus 38.0%). The Minilab TLC had false-positive and false-negative detection rates of 2.6% (13/506) and 15.2% (77/506) accordingly, resulting in the following test characteristics: sensitivity 0%, specificity 97.0%, positive predictive value 0, negative predictive value 0.8, positive likelihood ratio 0, negative likelihood ratio 1.0, diagnostic odds ratio 0, and adjusted diagnostic odds ratio 0.2. This study demonstrates unsatisfying diagnostic accuracy of Minilab system in screening poor-quality antimicrobials of common use. Using Minilab as a stand-alone system for monitoring drug quality should be reconsidered.

Power flow prediction in vibrating systems via model reduction

NASA Astrophysics Data System (ADS)

Li, Xianhui

This dissertation focuses on power flow prediction in vibrating systems. Reduced order models (ROMs) are built based on rational Krylov model reduction which preserve power flow information in the original systems over a specified frequency band. Stiffness and mass matrices of the ROMs are obtained by projecting the original system matrices onto the subspaces spanned by forced responses. A matrix-free algorithm is designed to construct ROMs directly from the power quantities at selected interpolation frequencies. Strategies for parallel implementation of the algorithm via message passing interface are proposed. The quality of ROMs is iteratively refined according to the error estimate based on residual norms. Band capacity is proposed to provide a priori estimate of the sizes of good quality ROMs. Frequency averaging is recast as ensemble averaging and Cauchy distribution is used to simplify the computation. Besides model reduction for deterministic systems, details of constructing ROMs for parametric and nonparametric random systems are also presented. Case studies have been conducted on testbeds from Harwell-Boeing collections. Input and coupling power flow are computed for the original systems and the ROMs. Good agreement is observed in all cases.

On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours reanalysis-run of NAQFC using same-day observed fire emission are being tested. Preliminary results have shown that this procedure greatly improves the PM2.5 predictions at both nearby and downstream areas from fire sources. The 24 hours reanalysis-run is critical and necessary especially during extreme fire events to provide better PM2.5 predictions.

Natural language processing in an intelligent writing strategy tutoring system.

PubMed

McNamara, Danielle S; Crossley, Scott A; Roscoe, Rod

2013-06-01

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (Written Communication 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6% of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38% of the variance in human scores of essay quality with 91% adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44% of the variance with 94% adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.

Assessment of Predictable Productivity of Nurses Working in Kerman University of Medical Sciences' Teaching Hospitals via the Dimensions of Quality of Work Life.

PubMed

Borhani, Fariba; Arbabisarjou, Azizollah; Kianian, Toktam; Saber, Saman

2016-10-01

Despite the existence of a large community of nurses, specific mechanisms have not been developed yet to consider their needs and the quality of their work life. Moreover, few studies have been conducted to analyze the nature of nursing, nursing places or nurses' quality of work life. In this regard, the present study aimed to assess predictable productivity of nurses working in Kerman University of Medical Sciences' teaching hospitals via the dimensions of Quality of Work Life. The present descriptive-correlational study was conducted to assess predictable productivity of nurses via the dimensions of Quality of Work Life. The study's population consisted of all nurses working in different wards of teaching hospitals associated with Kerman University of Medical Sciences. Out of the whole population, 266 nurses were selected based on the simple random sampling method. To collect data, the questionnaires of 'Quality of Nursing Work Life' and 'Productivity' were used after confirming their reliability (test-retest) and content validity. Finally, the collected data were analyzed through the SPSS software (version 16). Although the quality of work life for nurses was average and their productivity was low but the results showed that quality of life is directly related to nurses' productivity. Quality of life and its dimensions are predictive factors in the in the nurses' productivity. It can conclude that by recognizing the nurses' quality of work life situation, it can realize this group productivity and their values to the efficiency of the health system. For the quality of working life improvement and increasing nurses' productivity more efforts are needed by authorities. The findings can be applied by managers of hospitals and nursing services along with head nurses to enhance the quality of health services and nursing profession in general.

Using biotic ligand models to predict metal toxicity in mineralized systems

USGS Publications Warehouse

Smith, Kathleen S.; Balistrieri, Laurie S.; Todd, Andrew S.

2015-01-01

The biotic ligand model (BLM) is a numerical approach that couples chemical speciation calculations with toxicological information to predict the toxicity of aquatic metals. This approach was proposed as an alternative to expensive toxicological testing, and the U.S. Environmental Protection Agency incorporated the BLM into the 2007 revised aquatic life ambient freshwater quality criteria for Cu. Research BLMs for Ag, Ni, Pb, and Zn are also available, and many other BLMs are under development. Current BLMs are limited to ‘one metal, one organism’ considerations. Although the BLM generally is an improvement over previous approaches to determining water quality criteria, there are several challenges in implementing the BLM, particularly at mined and mineralized sites. These challenges include: (1) historically incomplete datasets for BLM input parameters, especially dissolved organic carbon (DOC), (2) several concerns about DOC, such as DOC fractionation in Fe- and Al-rich systems and differences in DOC quality that result in variations in metal-binding affinities, (3) water-quality parameters and resulting metal-toxicity predictions that are temporally and spatially dependent, (4) additional influences on metal bioavailability, such as multiple metal toxicity, dietary metal toxicity, and competition among organisms or metals, (5) potential importance of metal interactions with solid or gas phases and/or kinetically controlled reactions, and (6) tolerance to metal toxicity observed for aquatic organisms living in areas with elevated metal concentrations.

Pulse Vector-Excitation Speech Encoder

NASA Technical Reports Server (NTRS)

Davidson, Grant; Gersho, Allen

1989-01-01

Proposed pulse vector-excitation speech encoder (PVXC) encodes analog speech signals into digital representation for transmission or storage at rates below 5 kilobits per second. Produces high quality of reconstructed speech, but with less computation than required by comparable speech-encoding systems. Has some characteristics of multipulse linear predictive coding (MPLPC) and of code-excited linear prediction (CELP). System uses mathematical model of vocal tract in conjunction with set of excitation vectors and perceptually-based error criterion to synthesize natural-sounding speech.

Predicting Trihalomethanes (THMs) in the New York City Water Supply

NASA Astrophysics Data System (ADS)

Mukundan, R.; Van Dreason, R.

2013-12-01

Chlorine, a commonly used disinfectant in most water supply systems, can combine with organic carbon to form disinfectant byproducts including carcinogenic trihalomethanes (THMs). We used water quality data from 24 monitoring sites within the New York City (NYC) water supply distribution system, measured between January 2009 and April 2012, to develop site-specific empirical models for predicting total trihalomethane (TTHM) levels. Terms in the model included various combinations of the following water quality parameters: total organic carbon, pH, specific conductivity, and water temperature. Reasonable estimates of TTHM levels were achieved with overall R2 of about 0.87 and predicted values within 5 μg/L of measured values. The relative importance of factors affecting TTHM formation was estimated by ranking the model regression coefficients. Site-specific models showed improved model performance statistics compared to a single model for the entire system most likely because the single model did not consider locational differences in the water treatment process. Although never out of compliance in 2011, the TTHM levels in the water supply increased following tropical storms Irene and Lee with 45% of the samples exceeding the 80 μg/L Maximum Contaminant Level (MCL) in October and November. This increase was explained by changes in water quality parameters, particularly by the increase in total organic carbon concentration and pH during this period.

Modeling and performance assessment in QinetiQ of EO and IR airborne reconnaissance systems

NASA Astrophysics Data System (ADS)

Williams, John W.; Potter, Gary E.

2002-11-01

QinetiQ are the technical authority responsible for specifying the performance requirements for the procurement of airborne reconnaissance systems, on behalf of the UK MoD. They are also responsible for acceptance of delivered systems, overseeing and verifying the installed system performance as predicted and then assessed by the contractor. Measures of functional capability are central to these activities. The conduct of these activities utilises the broad technical insight and wide range of analysis tools and models available within QinetiQ. This paper focuses on the tools, methods and models that are applicable to systems based on EO and IR sensors. The tools, methods and models are described, and representative output for systems that QinetiQ has been responsible for is presented. The principle capability applicable to EO and IR airborne reconnaissance systems is the STAR (Simulation Tools for Airborne Reconnaissance) suite of models. STAR generates predictions of performance measures such as GRD (Ground Resolved Distance) and GIQE (General Image Quality) NIIRS (National Imagery Interpretation Rating Scales). It also generates images representing sensor output, using the scene generation software CAMEO-SIM and the imaging sensor model EMERALD. The simulated image 'quality' is fully correlated with the predicted non-imaging performance measures. STAR also generates image and table data that is compliant with STANAG 7023, which may be used to test ground station functionality.

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

NASA Astrophysics Data System (ADS)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key physical parameters inputs affecting transfer of heat, momentum and soil moisture in land-surface process in MM5. Using base the accurate input datasets, we are able to have improved see the differences of predictions of ground temperatures, winds and even thunderstorm activities within boundary layer.

Application of a computer model to predict optimum slaughter end points for different biological types of feeder cattle.

PubMed

Williams, C B; Bennett, G L

1995-10-01

A bioeconomic model was developed to predict slaughter end points of different genotypes of feeder cattle, where profit/rotation and profit/day were maximized. Growth, feed intake, and carcass weight and composition were simulated for 17 biological types of steers. Distribution of carcass weight and proportion in four USDA quality and five USDA yield grades were obtained from predicted carcass weights and composition. Average carcass value for each genotype was calculated from these distributions under four carcass pricing systems that varied from value determined on quality grade alone to value determined on yield grade alone. Under profitable market conditions, rotation length was shorter and carcass weights lighter when the producer's goal was maximum profit/day, compared with maximum profit/rotation. A carcass value system based on yield grade alone resulted in greater profit/rotation and in lighter and leaner carcasses than a system based on quality grade alone. High correlations ( > .97) were obtained between breed profits obtained with different sets of input/output prices and carcass price discount weight ranges. This suggests that breed rankings on the basis of breed profits may not be sensitive to changes in input/output market prices. Steers that were on a grower-stocker system had leaner carcasses, heavier optimum carcass weight, greater profits, and less variation in optimum carcass weights between genotypes than steers that were started on a high-energy finishing diet at weaning. Overall results suggest that breed choices may change with different carcass grading and value systems and postweaning production systems. This model has potential to provide decision support in marketing fed cattle.

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Examining health-related quality of life, adaptive skills, and psychological functioning in children and adolescents with epilepsy presenting for a neuropsychological evaluation.

PubMed

Clary, Lauren E; Vander Wal, Jillon S; Titus, Jeffrey B

2010-11-01

The purpose of this study was to characterize 132 children and adolescents (mean age = 10 years, 11 months) with epilepsy in terms of psychosocial functioning and to determine the extent to which adaptive skills and psychological functioning predict health-related quality of life (HRQOL), above and beyond demographic and epilepsy-specific characteristics. A chart review was conducted to obtain demographic and epilepsy-specific information as well as caregiver responses on the Behavior Assessment System for Children, Second Edition (BASC-2) Parent Report and the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE). In addition to Full Scale IQ and age at seizure onset, the BASC-2 Clinical and Adaptive Skills subscales also predicted HRQOL, indicating that this measure may be particularly helpful in predicting HRQOL above and beyond information routinely collected in a medical setting. It is imperative to evaluate children with epilepsy for psychosocial difficulties and diminished HRQOL to ensure the provision of comprehensive quality care and intervention services. Copyright © 2010 Elsevier Inc. All rights reserved.

Observed Parent-Child Relationship Quality Predicts Antibody Response to Vaccination in Children

PubMed Central

O'Connor, Thomas G; Wang, Hongyue; Moynihan, Jan A; Wyman, Peter A.; Carnahan, Jennifer; Lofthus, Gerry; Quataert, Sally A.; Bowman, Melissa; Burke, Anne S.; Caserta, Mary T

2015-01-01

Background Quality of the parent-child relationship is a robust predictor of behavioral and emotional health for children and adolescents; the application to physical health is less clear. Methods We investigated the links between observed parent-child relationship quality in an interaction task and antibody response to meningococcal conjugate vaccine in a longitudinal study of 164 ambulatory 10-11 year-old children; additional analyses examine associations with cortisol reactivity, BMI, and somatic illness. Results Observed negative/conflict behavior in the interaction task predicted a less robust antibody response to meningococcal serotype C vaccine in the child over a 6 month-period, after controlling for socio-economic and other covariates. Observer rated interaction conflict also predicted increased cortisol reactivity following the interaction task and higher BMI, but these factors did not account for the link between relationship quality and antibody response. Conclusions The results begin to document the degree to which a major source of child stress exposure, parent-child relationship conflict, is associated with altered immune system development in children, and may constitute an important public health consideration. PMID:25862953

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

A Mass-balance nitrate model for predicting the effects of land use on ground-water quality in municipal wellhead-protection areas

USGS Publications Warehouse

Frimpter, M.H.; Donohue, J.J.; Rapacz, M.V.; Beye, H.G.

1990-01-01

A mass-balance accounting model can be used to guide the management of septic systems and fertilizers to control the degradation of groundwater quality in zones of an aquifer that contributes water to public supply wells. The nitrate nitrogen concentration of the mixture in the well can be predicted for steady-state conditions by calculating the concentration that results from the total weight of nitrogen and total volume of water entering the zone of contribution to the well. These calculations will allow water-quality managers to predict the nitrate concentrations that would be produced by different types and levels of development, and to plan development accordingly. Computations for different development schemes provide a technical basis for planners and managers to compare water quality effects and to select alternatives that limit nitrate concentration in wells. Appendix A contains tables of nitrate loads and water volumes from common sources for use with the accounting model. Appendix B describes the preparation of a spreadsheet for the nitrate loading calculations with a software package generally available for desktop computers. (USGS)

Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

NASA Technical Reports Server (NTRS)

Miller, Christopher J.

2011-01-01

A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.

PubMed

Najah, A; El-Shafie, A; Karim, O A; El-Shafie, Amr H

2014-02-01

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

The role of future scenarios to understand deep uncertainty in air quality management

EPA Science Inventory

The environment and it’s interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents ...

DECISION-SUPPORT TOOLS FOR PREDICTING THE PERFORMANCE OF WATER DISTRIBUTION AND WASTEWATER COLLECTION SYSTEMS

EPA Science Inventory

Water and wastewater infrastructure systems represent a major capital investment; utilities must ensure they are getting the highest yield possible on their investment, both in terms of dollars and water quality. Accurate information related to equipment, pipe characteristics, l...

DECISION-SUPPORT TOOLS FOR PREDICTING THE PERFORMANCE OF WATER DISTRIBUTION AND WASTEWATER COLLECTION SYSTEMS

EPA Science Inventory

Water and wastewater infrastructure systems represent a major capital investment; utilities must ensure they are getting the highest yield possible on their investment, both in terms of dollars and water quality. Accurate information related to equipment, pipe characteristics, lo...

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Quality Measures for Digital Business Ecosystems Formation

NASA Astrophysics Data System (ADS)

Raza, Muhammad; Hussain, Farookh Khadeer; Chang, Elizabeth

To execute a complex business task, business entities may need to collaborate with each other as individually they may not have the capability or willingness to perform the task on its own. Such collaboration can be seen implemented in digital business ecosystems in the form of simple coalitions using multi-agent systems or by employing Electronic Institutions. A major challenge is choosing optimal partners who will deliver the agreed commitments, and act in the coalition’s interest. Business entities are scaled according to their quality level. Determining the quality of previously unknown business entities and predicting the quality of such an entity in a dynamic environment are crucial issues in Business Ecosystems. A comprehensive quality management system grounded in the concepts of Trust and Reputation can help address these issues.

POSSUM--a model for surgical outcome audit in quality care.

PubMed

Ng, K J; Yii, M K

2003-10-01

Comparative surgical audit to monitor quality of care should be performed with a risk-adjusted scoring system rather than using crude morbidity and mortality rates. A validated and widely applied risk adjusted scoring system, P-POSSUM (Portsmouth-Physiological and Operative Severity Score for the enUmeration of Mortality) methodology, was applied to a prospective series of predominantly general surgical patients at the Sarawak General Hospital, Kuching over a six months period. The patients were grouped into four risk groups. The observed mortality rates were not significantly different from predicted rates, showing that the quality of surgical care was at par with typical western series. The simplicity and advantages of this scoring system over other auditing tools are discussed. The P-POSSUM methodology could form the basis of local comparative surgical audit for assessment and maintenance of quality care.

Big Data Analytic, Big Step for Patient Management and Care in Puerto Rico.

PubMed

Borrero, Ernesto E

2018-01-01

This letter provides an overview of the application of big data in health care system to improve quality of care, including predictive modelling for risk and resource use, precision medicine and clinical decision support, quality of care and performance measurement, public health and research applications, among others. The author delineates the tremendous potential for big data analytics and discuss how it can be successfully implemented in clinical practice, as an important component of a learning health-care system.

Utility of NCEP Operational and Emerging Meteorological Models for Driving Air Quality Prediction

NASA Astrophysics Data System (ADS)

McQueen, J.; Huang, J.; Huang, H. C.; Shafran, P.; Lee, P.; Pan, L.; Sleinkofer, A. M.; Stajner, I.; Upadhayay, S.; Tallapragada, V.

2017-12-01

Operational air quality predictions for the United States (U. S.) are provided at NOAA by the National Air Quality Forecasting Capability (NAQFC). NAQFC provides nationwide operational predictions of ozone and particulate matter twice per day (at 06 and 12 UTC cycles) at 12 km resolution and 1 hour time intervals through 48 hours and distributed at http://airquality.weather.gov. The NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) 12 km weather prediction is used to drive the Community Multiscale Air Quality (CMAQ) model. In 2017, the NAM was upgraded in part to reduce a warm 2m temperature bias in Summer (V4). At the same time CMAQ was updated to V5.0.2. Both versions of the models were run in parallel for several months. Therefore the impact of improvements from the atmospheric chemistry model versus upgrades with the weather prediction model could be assessed. . Improvements to CMAQ were related to improvements to improvements in NAM 2 m temperature bias through increasing the opacity of clouds and reducing downward shortwave radiation resulted in reduced ozone photolysis. Higher resolution operational NWP models have recently been introduced as part of the NCEP modeling suite. These include the NAM CONUS Nest (3 km horizontal resolution) run four times per day through 60 hours and the High Resolution Rapid Refresh (HRRR, 3 km) run hourly out to 18 hours. In addition, NCEP with other NOAA labs has begun to develop and test the Next Generation Global Prediction System (NGGPS) based on the FV3 global model. This presentation also overviews recent developments with operational numerical weather prediction and evaluates the ability of these models for predicting low level temperatures, clouds and capturing boundary layer processes important for driving air quality prediction in complex terrain. The assessed meteorological model errors could help determine the magnitude of possible pollutant errors from CMAQ if used for driving meteorology. The NWP models will be evaluated against standard and mesonet fields averaged for various regions during the summer 2017. An evaluation of meteorological fields important to air quality modeling (eg: near surface winds, temperatures, moisture and boundary layer heights, cloud cover) will be reported on.

Electronic clinical predictive thermometer using logarithm for temperature prediction

NASA Technical Reports Server (NTRS)

Cambridge, Vivien J. (Inventor); Koger, Thomas L. (Inventor); Nail, William L. (Inventor); Diaz, Patrick (Inventor)

1998-01-01

A thermometer that rapidly predicts body temperature based on the temperature signals received from a temperature sensing probe when it comes into contact with the body. The logarithms of the differences between the temperature signals in a selected time frame are determined. A line is fit through the logarithms and the slope of the line is used as a system time constant in predicting the final temperature of the body. The time constant in conjunction with predetermined additional constants are used to compute the predicted temperature. Data quality in the time frame is monitored and if unacceptable, a different time frame of temperature signals is selected for use in prediction. The processor switches to a monitor mode if data quality over a limited number of time frames is unacceptable. Determining the start time on which the measurement time frame for prediction is based is performed by summing the second derivatives of temperature signals over time frames. When the sum of second derivatives in a particular time frame exceeds a threshold, the start time is established.

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Automating annotation of information-giving for analysis of clinical conversation.

PubMed

Mayfield, Elijah; Laws, M Barton; Wilson, Ira B; Penstein Rosé, Carolyn

2014-02-01

Coding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters. The data were transcripts of 415 routine outpatient visits of HIV patients which had previously been coded for speech acts using the Generalized Medical Interaction Analysis System (GMIAS); 50 had also been coded for larger scale features using the Comprehensive Analysis of the Structure of Encounters System (CASES). We aggregated selected speech acts into information-giving and requesting, then trained the machine to automatically annotate using logistic regression classification. We evaluated reliability by per-speech act accuracy. We used multiple regression to predict patient reports of communication quality from post-visit surveys using the patient and provider information-giving to information-requesting ratio (briefly, information-giving ratio) and patient gender. Automated coding produces moderate reliability with human coding (accuracy 71.2%, κ=0.57), with high correlation between machine and human prediction of the information-giving ratio (r=0.96). The regression significantly predicted four of five patient-reported measures of communication quality (r=0.263-0.344). The information-giving ratio is a useful and intuitive measure for predicting patient perception of provider-patient communication quality. These predictions can be made with automated annotation, which is a practical option for studying large collections of clinical encounters with objectivity, consistency, and low cost, providing greater opportunity for training and reflection for care providers.

Information system of forest growth and productivity by site quality type and elements of forest

NASA Astrophysics Data System (ADS)

Khlyustov, V.

2012-04-01

Information system of forest growth and productivity by site quality type and elements of forest V.K. Khlustov Head of the Forestry Department of Russian State Agrarian University named after K.A.Timiryazev doctor of agricultural sciences, professor The efficiency of forest management can be improved substantially by development and introduction of principally new models of forest growth and productivity dynamics based on regionalized site specific parameters. Therefore an innovative information system was developed. It describes the current state and gives a forecast for forest stand parameters: growth, structure, commercial and biological productivity depend on type of site quality. In contrast to existing yield tables, the new system has environmental basis: site quality type. The information system contains set of multivariate statistical models and can work at the level of individual trees or at the stand level. The system provides a graphical visualization, as well as export of the emulation results. The System is able to calculate detailed description of any forest stand based on five initial indicators: site quality type, site index, stocking, composition, and tree age by elements of the forest. The results of the model run are following parameters: average diameter and height, top height, number of trees, basal area, growing stock (total, commercial with distribution by size, firewood and residuals), live biomass (stem, bark, branches, foliage). The system also provides the distribution of mentioned above forest stand parameters by tree diameter classes. To predict the future forest stand dynamics the system require in addition the time slot only. Full set of forest parameters mention above will be provided by the System. The most conservative initial parameters (site quality type and site index) can be kept in the form of geo referenced polygons. In this case the system would need only 3 dynamic initial parameters (stocking, composition and age) to simulate forest parameters and their dynamics. The system can substitute traditional processing of forest inventory field data and provide users with detailed information on the current state of forest and give a prediction. Implementation of the proposed system in combination with high resolution remote sensing is able to increase significantly the quality of forest inventory and at the same time reduce the costs. The system is a contribution to site oriented forest management. The System is registered in the Russian State Register of Computer Programs 12.07.2011, No 2011615418.

Role of future scenarios in understanding deep uncertainty in long-term air quality management

EPA Science Inventory

The environment and its interactions with human systems, whether economic, social or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of deep uncertainty presents a challenge to ...

IDMA-Based MAC Protocol for Satellite Networks with Consideration on Channel Quality

PubMed Central

2014-01-01

In order to overcome the shortcomings of existing medium access control (MAC) protocols based on TDMA or CDMA in satellite networks, interleave division multiple access (IDMA) technique is introduced into satellite communication networks. Therefore, a novel wide-band IDMA MAC protocol based on channel quality is proposed in this paper, consisting of a dynamic power allocation algorithm, a rate adaptation algorithm, and a call admission control (CAC) scheme. Firstly, the power allocation algorithm combining the technique of IDMA SINR-evolution and channel quality prediction is developed to guarantee high power efficiency even in terrible channel conditions. Secondly, the effective rate adaptation algorithm, based on accurate channel information per timeslot and by the means of rate degradation, can be realized. What is more, based on channel quality prediction, the CAC scheme, combining the new power allocation algorithm, rate scheduling, and buffering strategies together, is proposed for the emerging IDMA systems, which can support a variety of traffic types, and offering quality of service (QoS) requirements corresponding to different priority levels. Simulation results show that the new wide-band IDMA MAC protocol can make accurate estimation of available resource considering the effect of multiuser detection (MUD) and QoS requirements of multimedia traffic, leading to low outage probability as well as high overall system throughput. PMID:25126592

Stochastic modeling for river pollution of Sungai Perlis

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

Yunus, Nurul Izzaty Mohd.; Rahman, Haliza Abd.; Bahar, Arifah

2015-02-03

River pollution has been recognized as a contributor to a wide range of health problems and disorders in human. It can pose health dangers to humans who come into contact with it, either directly or indirectly. Therefore, it is most important to measure the concentration of Biochemical Oxygen Demand (BOD) as a water quality parameter since the parameter has long been the basic means for determining the degree of water pollution in rivers. In this study, BOD is used as a parameter to estimate the water quality at Sungai Perlis. It has been observed that Sungai Perlis is polluted duemore » to lack of management and improper use of resources. Therefore, it is of importance to model the Sungai Perlis water quality in order to describe and predict the water quality systems. The BOD concentration secondary data set is used which was extracted from the Drainage and Irrigation Department Perlis State website. The first order differential equation from Streeter – Phelps model was utilized as a deterministic model. Then, the model was developed into a stochastic model. Results from this study shows that the stochastic model is more adequate to describe and predict the BOD concentration and the water quality systems in Sungai Perlis by having smaller value of mean squared error (MSE)« less

Is the maturity of hospitals' quality improvement systems associated with measures of quality and patient safety?

PubMed Central

2011-01-01

Background Previous research addressed the development of a classification scheme for quality improvement systems in European hospitals. In this study we explore associations between the 'maturity' of the hospitals' quality improvement system and clinical outcomes. Methods The maturity classification scheme was developed based on survey results from 389 hospitals in eight European countries. We matched the hospitals from the Spanish sample (113 hospitals) with those hospitals participating in a nation-wide, voluntary hospital performance initiative. We then compared sample distributions and explored associations between the 'maturity' of the hospitals' quality improvement system and a range of composite outcomes measures, such as adjusted hospital-wide mortality, -readmission, -complication and -length of stay indices. Statistical analysis includes bivariate correlations for parametrically and non-parametrically distributed data, multiple robust regression models and bootstrapping techniques to obtain confidence-intervals for the correlation and regression estimates. Results Overall, 43 hospitals were included. Compared to the original sample of 113, this sample was characterized by a higher representation of university hospitals. Maturity of the quality improvement system was similar, although the matched sample showed less variability. Analysis of associations between the quality improvement system and hospital-wide outcomes suggests significant correlations for the indicator adjusted hospital complications, borderline significance for adjusted hospital readmissions and non-significance for the adjusted hospital mortality and length of stay indicators. These results are confirmed by the bootstrap estimates of the robust regression model after adjusting for hospital characteristics. Conclusions We assessed associations between hospitals' quality improvement systems and clinical outcomes. From this data it seems that having a more developed quality improvement system is associated with lower rates of adjusted hospital complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should aim at identifying the latent dimensions of quality improvement systems that predict quality and safety outcomes. Such research would add pertinent knowledge regarding the implementation of organizational strategies related with quality of care outcomes. PMID:22185479

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China.

PubMed

Xu, Yunzhen; Du, Pei; Wang, Jianzhou

2017-04-01

As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants. Therefore, in this paper a novel robust early warning system was successfully developed, which consists of three modules: evaluation module, forecasting module and characteristics estimating module. In this system, a new dynamic fuzzy synthetic evaluation is proposed and applied to determine air quality levels and primary pollutants, which can be regarded as the research objectives; Moreover, to further mine and analyze the characteristics of air pollutants, four different distribution functions and interval forecasting method are also employed that can not only provide predictive range, confidence level and the other uncertain information of the pollutants future values, but also assist decision-makers in reducing and controlling the emissions of atmospheric pollutants. Case studies utilizing hourly PM 2.5 , PM 10 and SO 2 data collected from Tianjin and Shanghai in China are applied as illustrative examples to estimate the effectiveness and efficiency of the proposed system. Experimental results obviously indicated that the developed novel early warning system is much suitable for analyzing and monitoring air pollution, which can also add a novel viable option for decision-makers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Plate versus bulk trolley food service in a hospital: comparison of patients' satisfaction.

PubMed

Hartwell, Heather J; Edwards, John S A; Beavis, John

2007-03-01

The aim of this research was to compare plate with bulk trolley food service in hospitals in terms of patient satisfaction. Key factors distinguishing satisfaction with each system would also be identified. A consumer opinion card (n = 180), concentrating on the quality indicators of core foods, was used to measure patient satisfaction and compare two systems of delivery, plate and trolley. Binary logistic regression analysis was used to build a model that would predict food service style on the basis of the food attributes measured. Further investigation used multinomial logistic regression to predict opinion for the assessment of each food attribute within food service style. Results showed that the bulk trolley method of food distribution enables all foods to have a more acceptable texture, and for some foods (potato, P = 0.007; poached fish, P = 0.001; and minced beef, P < or = 0.0005) temperature, and for other foods (broccoli, P < or = 0.0005; carrots, P < or = 0.0005; and poached fish, P = 0.001) flavor, than the plate system of delivery, where flavor is associated with bad opinion or dissatisfaction. A model was built indicating patient satisfaction with the two service systems. This research confirms that patient satisfaction is enhanced by choice at the point of consumption (trolley system); however, portion size was not the controlling dimension. Temperature and texture were the most important attributes that measure patient satisfaction with food, thus defining the focus for hospital food service managers. To date, a model predicting patient satisfaction with the quality of food as served has not been proposed, and as such this work adds to the body of knowledge in this field. This report brings new information about the service style of dishes for improving the quality of food and thus enhancing patient satisfaction.

Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

NASA Astrophysics Data System (ADS)

Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

2017-12-01

Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

Longitudinal Prediction of Quality-of-Life Scores and Locomotion in Individuals With Traumatic Spinal Cord Injury.

PubMed

Hiremath, Shivayogi V; Hogaboom, Nathan S; Roscher, Melissa R; Worobey, Lynn A; Oyster, Michelle L; Boninger, Michael L

2017-12-01

To examine (1) differences in quality-of-life scores for groups based on transitions in locomotion status at 1, 5, and 10 years postdischarge in a sample of people with spinal cord injury (SCI); and (2) whether demographic factors and transitions in locomotion status can predict quality-of-life measures at these time points. Retrospective case study of the National SCI Database. Model SCI Systems Centers. Individuals with SCI (N=10,190) from 21 SCI Model Systems Centers, identified through the National SCI Model Systems Centers database between the years 1985 and 2012. Subjects had FIM (locomotion mode) data at discharge and at least 1 of the following: 1, 5, or 10 years postdischarge. Not applicable. FIM-locomotion mode; Severity of Depression Scale; Satisfaction With Life Scale; and Craig Handicap Assessment and Reporting Technique. Participants who transitioned from ambulation to wheelchair use reported lower participation and life satisfaction, and higher depression levels (P<.05) than those who maintained their ambulatory status. Participants who transitioned from ambulation to wheelchair use reported higher depression levels (P<.05) and no difference for participation (P>.05) or life satisfaction (P>.05) compared with those who transitioned from wheelchair to ambulation. Demographic factors and locomotion transitions predicted quality-of-life scores at all time points (P<.05). The results of this study indicate that transitioning from ambulation to wheelchair use can negatively impact psychosocial health 10 years after SCI. Clinicians should be aware of this when deciding on ambulation training. Further work to characterize who may be at risk for these transitions is needed. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Incorporating the Wind Erosion Prediction System (WEPS) Into a Regional Air Quality Modeling System for the Pacific Northwest

USDA-ARS?s Scientific Manuscript database

In the Pacific Northwest, wind storms intermittently cause massive dust events that reduce visibility along roadways and jeopardize health as a result of extremely high concentrations of PM10 (particulate matter less than or equal to 10µm in diameter). An early warning dust forecast system is needed...

A model to predict stream water temperature across the conterminous USA

Treesearch

Catalina Segura; Peter Caldwell; Ge Sun; Steve McNulty; Yang Zhang

2014-01-01

Stream water temperature (ts) is a critical water quality parameter for aquatic ecosystems. However, ts records are sparse or nonexistent in many river systems. In this work, we present an empirical model to predict ts at the site scale across the USA. The model, derived using data from 171 reference sites selected from the Geospatial Attributes of Gages for Evaluating...

Modelling and control for laser based welding processes: modern methods of process control to improve quality of laser-based joining methods

NASA Astrophysics Data System (ADS)

Zäh, Ralf-Kilian; Mosbach, Benedikt; Hollwich, Jan; Faupel, Benedikt

2017-02-01

To ensure the competitiveness of manufacturing companies it is indispensable to optimize their manufacturing processes. Slight variations of process parameters and machine settings have only marginally effects on the product quality. Therefore, the largest possible editing window is required. Such parameters are, for example, the movement of the laser beam across the component for the laser keyhole welding. That`s why it is necessary to keep the formation of welding seams within specified limits. Therefore, the quality of laser welding processes is ensured, by using post-process methods, like ultrasonic inspection, or special in-process methods. These in-process systems only achieve a simple evaluation which shows whether the weld seam is acceptable or not. Furthermore, in-process systems use no feedback for changing the control variables such as speed of the laser or adjustment of laser power. In this paper the research group presents current results of the research field of Online Monitoring, Online Controlling and Model predictive controlling in laser welding processes to increase the product quality. To record the characteristics of the welding process, tested online methods are used during the process. Based on the measurement data, a state space model is ascertained, which includes all the control variables of the system. Depending on simulation tools the model predictive controller (MPC) is designed for the model and integrated into an NI-Real-Time-System.

IMPACT OF AN UPDATED CARBON BOND MECHANISM ON PREDICTIONS FROM THE CMAQ MODELING SYSTEM: PRELIMINARY ASSESSMENT

EPA Science Inventory

An updated and expanded Carbon Bond mechanism (CB05) has been incorporated into the Community Multiscale Air Quality modeling system to more accurately simulate wintertime, pristine, and high altitude situations. The CB05 mechanism has nearly twice the number of reactions compare...

A Learning Progression for Water in Socio-Ecological Systems

ERIC Educational Resources Information Center

Gunckel, Kristin L.; Covitt, Beth A.; Salinas, Ivan; Anderson, Charles W.

2012-01-01

Providing model-based accounts (explanations and predictions) of water and substances in water moving through environmental systems is an important practice for environmental science literacy and necessary for citizens confronting global and local water quantity and quality issues. In this article we present a learning progression for water in…

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Speed and Delay Prediction Models for Planning Applications

DOT National Transportation Integrated Search

1999-01-01

Estimation of vehicle speed and delay is fundamental to many forms of : transportation planning analyses including air quality, long-range travel : forecasting, major investment studies, and congestion management systems. : However, existing planning...

Relation among HPA and HPG neuroendocrine systems, transmissible risk and neighborhood quality on development of substance use disorder: results of a 10-year prospective study.

PubMed

Tarter, Ralph E; Kirisci, Levent; Kirillova, Galina; Reynolds, Maureen; Gavaler, Judy; Ridenour, Ty; Horner, Michelle; Clark, Duncan; Vanyukov, Michael

2013-01-01

Research has shown involvement of hormones of the hypothalamic pituitary adrenal (HPA) axis and hypothalamic pituitary gonadal (HPG) axis in the regulation of behaviors that contribute to SUD risk and its intergenerational transmission. Neighborhood environment has also been shown to relate to hormones of these two neuroendocrine systems and behaviors associated with SUD liability. Accordingly, it was hypothesized that (1) parental SUD severity and neighborhood quality correlate with activity of the HPG axis (testosterone level) and HPA axis (cortisol stability), and (2) transmissible risk during childhood mediates these hormone variables on development of SUD measured in adulthood. Transmissible risk for SUD measured by the transmissible liability index (TLI; Vanyukov et al., 2009) along with saliva cortisol and plasma testosterone were prospectively measured in boys at ages 10-12 and 16. Neighborhood quality was measured using a composite score encompassing indicators of residential instability and economic disadvantage. SUD was assessed at age 22. Neither hormone variable cross-sectionally correlated with transmissible risk measured at ages 10-12 and 16. However, the TLI at age 10-12 predicted testosterone level and cortisol stability at age 16. Moreover, testosterone level, correlated with cortisol stability at age 16, predicted SUD at age 22. HPA and HPG axes activity do not underlie variation in TLI, however, high transmissible risk in childhood predicts neuroendocrine system activity presaging development of SUD. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

PubMed Central

Boyd, Roslyn N; Davies, Peter SW; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-01-01

Objectives Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. Trial registration number ACTRN: 12616001488493 PMID:28706091

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

USGS Publications Warehouse

Hanna, R. Blair; Campbell, Sharon G.

2000-01-01

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

Understanding Intention to Use Electronic Information Resources: A Theoretical Extension of the Technology Acceptance Model (TAM)

PubMed Central

Tao, Donghua

2008-01-01

This study extended the Technology Acceptance Model (TAM) by examining the roles of two aspects of e-resource characteristics, namely, information quality and system quality, in predicting public health students’ intention to use e-resources for completing research paper assignments. Both focus groups and a questionnaire were used to collect data. Descriptive analysis, data screening, and Structural Equation Modeling (SEM) techniques were used for data analysis. The study found that perceived usefulness played a major role in determining students’ intention to use e-resources. Perceived usefulness and perceived ease of use fully mediated the impact that information quality and system quality had on behavior intention. The research model enriches the existing technology acceptance literature by extending TAM. Representing two aspects of e-resource characteristics provides greater explanatory information for diagnosing problems of system design, development, and implementation. PMID:18999300

Understanding intention to use electronic information resources: A theoretical extension of the technology acceptance model (TAM).

PubMed

Tao, Donghua

2008-11-06

This study extended the Technology Acceptance Model (TAM) by examining the roles of two aspects of e-resource characteristics, namely, information quality and system quality, in predicting public health students' intention to use e-resources for completing research paper assignments. Both focus groups and a questionnaire were used to collect data. Descriptive analysis, data screening, and Structural Equation Modeling (SEM) techniques were used for data analysis. The study found that perceived usefulness played a major role in determining students' intention to use e-resources. Perceived usefulness and perceived ease of use fully mediated the impact that information quality and system quality had on behavior intention. The research model enriches the existing technology acceptance literature by extending TAM. Representing two aspects of e-resource characteristics provides greater explanatory information for diagnosing problems of system design, development, and implementation.

The European water framework directive: water quality classification and implications to engineering planning.

PubMed

Achleitner, Stefan; De Toffol, Sara; Engelhard, Carolina; Rauch, Wolfgang

2005-04-01

The European Water framework directive (WFD) is probably the most important environmental management directive that has been enacted over the last decade in the European Union. The directive aims at achieving an overall good ecological status in all European water bodies. In this article, we discuss the implementation steps of the WFD and their implications for environmental engineering practice while focusing on rivers as the main receiving waters. Arising challenges for engineers and scientists are seen in the quantitative assessment of water quality, where standardized systems are needed to estimate the biological status. This is equally of concern in engineering planning, where the prediction of ecological impacts is required. Studies dealing with both classification and prediction of the ecological water quality are reviewed. Further, the combined emission-water quality approach is discussed. Common understanding of this combined approach is to apply the most stringent of either water quality or emission standard to a certain case. In contrast, for example, the Austrian water act enables the application of only the water quality based approach--at least on a temporary basis.

Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams

USGS Publications Warehouse

Balistrieri, Laurie S.; Nimick, David A.; Mebane, Christopher A.

2012-01-01

Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.

Use of a continuous twin screw granulation and drying system during formulation development and process optimization.

PubMed

Vercruysse, J; Peeters, E; Fonteyne, M; Cappuyns, P; Delaet, U; Van Assche, I; De Beer, T; Remon, J P; Vervaet, C

2015-01-01

Since small scale is key for successful introduction of continuous techniques in the pharmaceutical industry to allow its use during formulation development and process optimization, it is essential to determine whether the product quality is similar when small quantities of materials are processed compared to the continuous processing of larger quantities. Therefore, the aim of this study was to investigate whether material processed in a single cell of the six-segmented fluid bed dryer of the ConsiGma™-25 system (a continuous twin screw granulation and drying system introduced by GEA Pharma Systems, Collette™, Wommelgem, Belgium) is predictive of granule and tablet quality during full-scale manufacturing when all drying cells are filled. Furthermore, the performance of the ConsiGma™-1 system (a mobile laboratory unit) was evaluated and compared to the ConsiGma™-25 system. A premix of two active ingredients, powdered cellulose, maize starch, pregelatinized starch and sodium starch glycolate was granulated with distilled water. After drying and milling (1000 μm, 800 rpm), granules were blended with magnesium stearate and compressed using a Modul™ P tablet press (tablet weight: 430 mg, main compression force: 12 kN). Single cell experiments using the ConsiGma™-25 system and ConsiGma™-1 system were performed in triplicate. Additionally, a 1h continuous run using the ConsiGma™-25 system was executed. Process outcomes (torque, barrel wall temperature, product temperature during drying) and granule (residual moisture content, particle size distribution, bulk and tapped density, hausner ratio, friability) as well as tablet (hardness, friability, disintegration time and dissolution) quality attributes were evaluated. By performing a 1h continuous run, it was detected that a stabilization period was needed for torque and barrel wall temperature due to initial layering of the screws and the screw chamber walls with material. Consequently, slightly deviating granule and tablet quality attributes were obtained during the start-up phase of the 1h run. For the single cell runs, granule and tablet properties were comparable with results obtained during the second part of the 1h run (after start-up). Although deviating granule quality (particle size distribution and Hausner ratio) was observed due to the divergent design of the ConsiGma™-1 unit and the ConsiGma™-25 system (horizontal set-up) used in this study, tablet quality produced from granules processed with the ConsiGma™-1 system was predictive for tablet quality obtained during continuous production using the ConsiGma™-25 system. Copyright © 2014 Elsevier B.V. All rights reserved.

The Relationship of Previous Training and Experience of Journal Peer Reviewers to Subsequent Review Quality

PubMed Central

Callaham, Michael L; Tercier, John

2007-01-01

Background Peer review is considered crucial to the selection and publication of quality science, but very little is known about the previous experiences and training that might identify high-quality peer reviewers. The reviewer selection processes of most journals, and thus the qualifications of their reviewers, are ill defined. More objective selection of peer reviewers might improve the journal peer review process and thus the quality of published science. Methods and Findings 306 experienced reviewers (71% of all those associated with a specialty journal) completed a survey of past training and experiences postulated to improve peer review skills. Reviewers performed 2,856 reviews of 1,484 separate manuscripts during a four-year study period, all prospectively rated on a standardized quality scale by editors. Multivariable analysis revealed that most variables, including academic rank, formal training in critical appraisal or statistics, or status as principal investigator of a grant, failed to predict performance of higher-quality reviews. The only significant predictors of quality were working in a university-operated hospital versus other teaching environment and relative youth (under ten years of experience after finishing training). Being on an editorial board and doing formal grant (study section) review were each predictors for only one of our two comparisons. However, the predictive power of all variables was weak. Conclusions Our study confirms that there are no easily identifiable types of formal training or experience that predict reviewer performance. Skill in scientific peer review may be as ill defined and hard to impart as is “common sense.” Without a better understanding of those skills, it seems unlikely journals and editors will be successful in systematically improving their selection of reviewers. This inability to predict performance makes it imperative that all but the smallest journals implement routine review ratings systems to routinely monitor the quality of their reviews (and thus the quality of the science they publish). PMID:17411314

Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models.

PubMed

Zhu, Hongyan; Chu, Bingquan; Fan, Yangyang; Tao, Xiaoya; Yin, Wenxin; He, Yong

2017-08-10

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre  = 0.9812, RPD = 5.17) and SSC (R pre  = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Automatic documentation system extension to multi-manufacturers' computers and to measure, improve, and predict software reliability

NASA Technical Reports Server (NTRS)

Simmons, D. B.

1975-01-01

The DOMONIC system has been modified to run on the Univac 1108 and the CDC 6600 as well as the IBM 370 computer system. The DOMONIC monitor system has been implemented to gather data which can be used to optimize the DOMONIC system and to predict the reliability of software developed using DOMONIC. The areas of quality metrics, error characterization, program complexity, program testing, validation and verification are analyzed. A software reliability model for estimating program completion levels and one on which to base system acceptance have been developed. The DAVE system which performs flow analysis and error detection has been converted from the University of Colorado CDC 6400/6600 computer to the IBM 360/370 computer system for use with the DOMONIC system.

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

PubMed

Qin, Xusong; Gao, Furong; Chen, Guohua

2012-03-15

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Alternator insulation evaluation tests

NASA Technical Reports Server (NTRS)

Penn, W. B.; Schaefer, R. F.; Balke, R. L.

1972-01-01

Tests were conducted to predict the remaining electrical insulation life of a 60 KW homopolar inductor alternator following completion of NASA turbo-alternator endurance tests for SNAP-8 space electrical power systems application. The insulation quality was established for two alternators following completion of these tests. A step-temperature aging test procedure was developed for insulation life prediction and applied to one of the two alternators. Armature winding insulation life of over 80,000 hours for an average winding temperature of 248 degrees C was predicted using the developed procedure.

Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches

USGS Publications Warehouse

Dawn Shively,; Nevers, Meredith; Cathy Breitenbach,; Phanikumar, Mantha S.; Kasia Przybyla-Kelly,; Ashley M. Spoljaric,; Richard L. Whitman,

2015-01-01

Predictive empirical modeling is used in many locations worldwide as a rapid, alternative recreational water quality management tool to eliminate delayed notifications associated with traditional fecal indicator bacteria (FIB) culturing (referred to as the persistence model, PM) and to prevent errors in releasing swimming advisories. The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R2 of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70–97%; specificity, 71–100%; and sensitivity, 0–64% and in 2013 accuracy was 68–97%; specificity, 73–100%; and sensitivity 0–36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.

White light emitting diode as potential replacement of tungsten-halogen lamp for visible spectroscopy system: a case study in the measurement of mango qualities

NASA Astrophysics Data System (ADS)

Chiong, W. L.; Omar, A. F.

2017-07-01

Non-destructive technique based on visible (VIS) spectroscopy using light emitting diode (LED) as lighting was used for evaluation of the internal quality of mango fruit. The objective of this study was to investigate feasibility of white LED as lighting in spectroscopic instrumentation to predict the acidity and soluble solids content of intact Sala Mango. The reflectance spectra of the mango samples were obtained and measured in the visible range (400-700 nm) using VIS spectroscopy illuminated under different white LEDs and tungsten-halogen lamp (pro lamp). Regression models were developed by multiple linear regression to establish the relationship between spectra and internal quality. Direct calibration transfer procedure was then applied between master and slave lighting to check on the acidity prediction results after transfer. Determination of mango acidity under white LED lighting was successfully performed through VIS spectroscopy using multiple linear regression but otherwise for soluble solids content. Satisfactory results were obtained for calibration transfer between LEDs with different correlated colour temperature indicated this technique was successfully used in spectroscopy measurement between two similar light sources in prediction of internal quality of mango.

Assessing the Hydrologic Performance of the EPA's Nonpoint Source Water Quality Assessment Decision Support Tool Using North American Land Data Assimilation System (Products)

NASA Technical Reports Server (NTRS)

Lee, S.; Ni-Meister, W.; Toll, D.; Nigro, J.; Guiterrez-Magness, A.; Engman, T.

2010-01-01

The accuracy of streamflow predictions in the EPA's BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) decision support tool is affected by the sparse meteorological data contained in BASINS. The North American Land Data Assimilation System (NLDAS) data with high spatial and temporal resolutions provide an alternative to the NOAA National Climatic Data Center (NCDC)'s station data. This study assessed the improvement of streamflow prediction of the Hydrological Simulation Program-FORTRAN (HSPF) model contained within BASINS using the NLDAS 118 degree hourly precipitation and evapotranspiration estimates in seven watersheds of the Chesapeake Bay region. Our results demonstrated consistent improvements of daily streamflow predictions in five of the seven watersheds when NLDAS precipitation and evapotranspiration data was incorporated into BASINS. The improvement of using the NLDAS data is significant when watershed's meteorological station is either far away or not in a similar climatic region. When the station is nearby, using the NLDAS data produces similar results. The correlation coefficients of the analyses using the NLDAS data were greater than 0.8, the Nash-Sutcliffe (NS) model fit efficiency greater than 0.6, and the error in the water balance was less than 5%. Our analyses also showed that the streamflow improvements were mainly contributed by the NLDAS's precipitation data and that the improvement from using NLDAS's evapotranspiration data was not significant; partially due to the constraints of current BASINS-HSPF settings. However, NLDAS's evapotranspiration data did improve the baseflow prediction. This study demonstrates the NLDAS data has the potential to improve stream flow predictions, thus aid the water quality assessment in the EPA nonpoint water quality assessment decision tool.

Cytokine-induced depression during IFN-α treatment: the role of IL-6 and sleep quality

PubMed Central

Prather, Aric A.; Rabinovitz, Mordechai; Pollock, Bruce G.; Lotrich, Francis E.

2009-01-01

Depressive symptoms, poor sleep quality, and systemic markers of inflammation (e.g. interleukin (IL)-6) are frequently associated. Interferon-alpha (IFN-α) therapy results in major depressive disorder (MDD) in some people, offering the possibility to elucidate the relationship of MDD to sleep and inflammation during treatment. In particular, delineating the temporal relations among these factors could help inform their causal relationships. To this end, a cohort of 95 non-depressed hepatitis C patients was followed prospectively for four consecutive months during IFN-α therapy. We found that higher pre-treatment levels of circulating IL-6 predicted incidence of MDD (X2(1)=7.7; p<0.05). Time-lagged mixed-effect analyses supported uni-directional associations in which IL-6 predicted next month’s PSQI scores (F(47, 11.6) = 78.4; p<0.0005), and PSQI scores predicted next month’s depressive Beck Depression Inventory-II (BDI) scores (F(16,22.6) = 3.4; p<0.005). In addition, on any given month of treatment, IL-6 levels predicted BDI symptoms the following month (F(16,97.5) = 7.3; p<0.0005), and conversely BDI predicted next month’s IL-6 (F(14,7.4) = 5.2; p<0.05) – providing evidence for a positive feedback relationship between depressive symptoms and systemic inflammation. These data provide further evidence that high levels of inflammation and poor sleep quality may be risk factors for IFN-α induced depression. Furthermore, these findings highlight the complex temporal relationships that exist among sleep, depression, and inflammation, and support the need for further prospective investigations to elucidate the dynamics that underlie depression during IFN-α treatment. PMID:19615438

Estimation of Handling Qualities Parameters of the Tu-144 Supersonic Transport Aircraft from Flight Test Data

NASA Technical Reports Server (NTRS)

Curry, Timothy J.; Batterson, James G. (Technical Monitor)

2000-01-01

Low order equivalent system (LOES) models for the Tu-144 supersonic transport aircraft were identified from flight test data. The mathematical models were given in terms of transfer functions with a time delay by the military standard MIL-STD-1797A, "Flying Qualities of Piloted Aircraft," and the handling qualities were predicted from the estimated transfer function coefficients. The coefficients and the time delay in the transfer functions were estimated using a nonlinear equation error formulation in the frequency domain. Flight test data from pitch, roll, and yaw frequency sweeps at various flight conditions were used for parameter estimation. Flight test results are presented in terms of the estimated parameter values, their standard errors, and output fits in the time domain. Data from doublet maneuvers at the same flight conditions were used to assess the predictive capabilities of the identified models. The identified transfer function models fit the measured data well and demonstrated good prediction capabilities. The Tu-144 was predicted to be between level 2 and 3 for all longitudinal maneuvers and level I for all lateral maneuvers. High estimates of the equivalent time delay in the transfer function model caused the poor longitudinal rating.

Capturing the Complexity: Content, Type, and Amount of Instruction and Quality of the Classroom Learning Environment Synergistically Predict Third Graders' Vocabulary and Reading Comprehension Outcomes

ERIC Educational Resources Information Center

Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L.; Giuliani, Sarah; Ingebrand, Sarah W.; McLean, Leigh; Morrison, Frederick J.

2014-01-01

We examined classrooms as complex systems that affect students' literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom learning environment. We observed 27 3rd-grade classrooms serving 315 target students using 2 different…

Estimating the probability of survival of individual shortleaf pine (Pinus echinata mill.) trees

Treesearch

Sudip Shrestha; Thomas B. Lynch; Difei Zhang; James M. Guldin

2012-01-01

A survival model is needed in a forest growth system which predicts the survival of trees on individual basis or on a stand basis (Gertner, 1989). An individual-tree modeling approach is one of the better methods available for predicting growth and yield as it provides essential information about particular tree species; tree size, tree quality and tree present status...

Sensory Information Processing

DTIC Science & Technology

1975-12-31

system noise . To see how this is avoided, note that zeroes in the blur spectrum become sharp, spike-like negative «*»• Page impulses when the...Synthetic Speech Quality Using Binaural Reverberation-- Boll 12 13 Section 4. Noise Suppression with Linear Prediction Filtering—Peterson 24 Section...5. Speech Processing to Reduce Noise and Improve Intelligibility— Callahan 28 Section 6. Linear Predictive Coding with a Glottal 36 Section 7

Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods.

PubMed

Mustonen, Satu M; Tissari, Soile; Huikko, Laura; Kolehmainen, Mikko; Lehtola, Markku J; Hirvonen, Arja

2008-05-01

The distribution of drinking water generates soft deposits and biofilms in the pipelines of distribution systems. Disturbances in water distribution can detach these deposits and biofilms and thus deteriorate the water quality. We studied the effects of simulated pressure shocks on the water quality with online analysers. The study was conducted with copper and composite plastic pipelines in a pilot distribution system. The online data gathered during the study was evaluated with Self-Organising Map (SOM) and Sammon's mapping, which are useful methods in exploring large amounts of multivariate data. The objective was to test the usefulness of these methods in pinpointing the abnormal water quality changes in the online data. The pressure shocks increased temporarily the number of particles, turbidity and electrical conductivity. SOM and Sammon's mapping were able to separate these situations from the normal data and thus make those visible. Therefore these methods make it possible to detect abrupt changes in water quality and thus to react rapidly to any disturbances in the system. These methods are useful in developing alert systems and predictive applications connected to online monitoring.

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

PubMed Central

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user's N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user's N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues. PMID:24982999

SENSITIVITY OF OZONE AND AEROSOL PREDICTIONS TO THE TRANSPORT ALGORITHMS IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

EPA's Models-3 CMAQ system is intended to provide a community modeling paradigm that allows continuous improvement of the one-atmosphere modeling capability in a unified fashion. CMAQ's modular design promotes incorporation of several sets of science process modules representing ...

Vocational Training in the Federal Republic of Germany.

ERIC Educational Resources Information Center

Bednarz-Braun, Iris; And Others

1990-01-01

Reports on two studies about vocational training in the Federal Republic of Germany (FRG). Finds that there is no uniform system of vocational training, but there are distinct segments with different objectives and different quality. Predicts that the West German system of vocational training will experience a loss of effectiveness unless it…

Analysis of the Transport and Fate of Metals Released From ...

EPA Pesticide Factsheets

This project’s objectives were to provide analysis of water quality following the release of acid mine drainage in the Animas and San Juan Rivers in a timely manner to 1) generate a comprehensive picture of the plume at the river system level, 2) help inform future monitoring efforts and 3) to predict potential secondary effects that could occur from materials that may remain stored within the system. The project focuses on assessing metals contamination during the plume and in the first month following the event. This project’s objectives were to provide analysis of water quality following the release of acid mine drainage from the Gold King Mine in the Animas and San Juan Rivers in a timely manner to 1) generate a comprehensive picture of the plume at the river system level, 2) help inform future monitoring efforts and 3) to predict potential secondary effects that could occur from materials that may remain stored within the system. The project focuses on assessing metals contamination during the plume and in the first month following the event.

Integration of Satellite, Modeled, and Ground Based Aerosol Data for use in Air Quality and Public Health Applications

NASA Astrophysics Data System (ADS)

Garcia, V.; Kondragunta, S.; Holland, D.; Dimmick, F.; Boothe, V.; Szykman, J.; Chu, A.; Kittaka, C.; Al-Saadi, J.; Engel-Cox, J.; Hoff, R.; Wayland, R.; Rao, S.; Remer, L.

2006-05-01

Advancements in remote sensing over the past decade have been recognized by governments around the world and led to the development of the international Global Earth Observation System of Systems 10-Year Implementation Plan. The plan for the U.S. contribution to GEOSS has been put forth in The Strategic Plan for the U.S. Integrated Earth Observation System (IEOS) developed under IWGEO-CENR. The approach for the development of the U.S. IEOS is to focus on specific societal benefits that can be achieved by integrating the nation's Earth observation capabilities. One such challenge is our ability to understand the impact of poor air quality on human health and well being. Historically, the air monitoring networks put in place for the Nations air quality programs provided the only aerosol air quality data on an ongoing and systematic basis at national levels. However, scientific advances in the remote sensing of aerosols from space have improved dramatically. The MODIS sensor and GOES Imager aboard NASA and NOAA satellites, respectively, provide synoptic-scale measurements of aerosol optical depth (AOD) which have been demonstrated to correlate with high levels of PM10 and PM2.5 at the surface. The MODIS sensor has been shown to be capable of a 1 km x 1 km (at nadir) AOD product, while the GOES Imager can provide AOD at 4 km x 4 km every 30 minutes. Within the next several years NOAA and EPA will begin to issue PM2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of PM2.5 on a daily basis. A multi-agency collaborative project among government and academia is underway to improve the spatial prediction of fine particulate matter through the integration of multi-sensor and multi-platform aerosol observations (MODIS and GOES), numerical model output, and air monitoring data. By giving more weight to monitoring data in monitored areas and relying on adjusted model output and satellite data in non-monitored areas, a Bayesian hierarchical space-time model will be used to improve the accuracy of prediction and associated prediction errors. The improved spatial predictions will be tested as estimates of exposure for input to modeling relationships between air quality and asthma/other respiratory diseases through CDC under the Environmental Public Health Tracking Network. We will also focus on the use of the predictive spatial maps within the EPA AIRNow program which provides near real-time spatial maps of daily average PM2.5 concentrations across the US. We will present the overall project plan and preliminary results with emphasis on how GEOSS framework is facilitating this effort.

SeTES, a Self-Teaching Expert System for the analysis, design and prediction of gas production from shales and a prototype for a new generation of Expert Systems in the Earth Sciences

NASA Astrophysics Data System (ADS)

Kuzma, H. A.; Boyle, K.; Pullman, S.; Reagan, M. T.; Moridis, G. J.; Blasingame, T. A.; Rector, J. W.; Nikolaou, M.

2010-12-01

A Self Teaching Expert System (SeTES) is being developed for the analysis, design and prediction of gas production from shales. An Expert System is a computer program designed to answer questions or clarify uncertainties that its designers did not necessarily envision which would otherwise have to be addressed by consultation with one or more human experts. Modern developments in computer learning, data mining, database management, web integration and cheap computing power are bringing the promise of expert systems to fruition. SeTES is a partial successor to Prospector, a system to aid in the identification and evaluation of mineral deposits developed by Stanford University and the USGS in the late 1970s, and one of the most famous early expert systems. Instead of the text dialogue used in early systems, the web user interface of SeTES helps a non-expert user to articulate, clarify and reason about a problem by navigating through a series of interactive wizards. The wizards identify potential solutions to queries by retrieving and combining together relevant records from a database. Inferences, decisions and predictions are made from incomplete and noisy inputs using a series of probabilistic models (Bayesian Networks) which incorporate records from the database, physical laws and empirical knowledge in the form of prior probability distributions. The database is mainly populated with empirical measurements, however an automatic algorithm supplements sparse data with synthetic data obtained through physical modeling. This constitutes the mechanism for how SeTES self-teaches. SeTES’ predictive power is expected to grow as users contribute more data into the system. Samples are appropriately weighted to favor high quality empirical data over low quality or synthetic data. Finally, a set of data visualization tools digests the output measurements into graphical outputs.

Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

NASA Astrophysics Data System (ADS)

Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

2014-12-01

Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

Predictive display design for the vehicles with time delay in dynamic response

NASA Astrophysics Data System (ADS)

Efremov, A. V.; Tiaglik, M. S.; Irgaleev, I. H.; Efremov, E. V.

2018-02-01

The two ways for the improvement of flying qualities are considered: the predictive display (PD) and the predictive display integrated with the flight control system (FCS). The both ways allow to transforming the controlled element dynamics in the crossover frequency range, to improve the accuracy of tracking and to suppress the effect of time delay in the vehicle response too. The technique for optimization of the predictive law is applied to the landing task. The results of the mathematical modeling and experimental investigations carried out for this task are considered in the paper.

The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

PubMed

Dėdelė, Audrius; Miškinytė, Auksė

2015-09-01

In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

Subjective evaluation of two stereoscopic imaging systems exploiting visual attention to improve 3D quality of experience

NASA Astrophysics Data System (ADS)

Hanhart, Philippe; Ebrahimi, Touradj

2014-03-01

Crosstalk and vergence-accommodation rivalry negatively impact the quality of experience (QoE) provided by stereoscopic displays. However, exploiting visual attention and adapting the 3D rendering process on the fly can reduce these drawbacks. In this paper, we propose and evaluate two different approaches that exploit visual attention to improve 3D QoE on stereoscopic displays: an offline system, which uses a saliency map to predict gaze position, and an online system, which uses a remote eye tracking system to measure real time gaze positions. The gaze points were used in conjunction with the disparity map to extract the disparity of the object-of-interest. Horizontal image translation was performed to bring the fixated object on the screen plane. The user preference between standard 3D mode and the two proposed systems was evaluated through a subjective evaluation. Results show that exploiting visual attention significantly improves image quality and visual comfort, with a slight advantage for real time gaze determination. Depth quality is also improved, but the difference is not significant.

Predicting the Effect of Changing Precipitation Extremes and Land Cover Change on Urban Water Quality

NASA Astrophysics Data System (ADS)

SUN, N.; Yearsley, J. R.; Lettenmaier, D. P.

2013-12-01

Recent research shows that precipitation extremes in many of the largest U.S. urban areas have increased over the last 60 years. These changes have important implications for stormwater runoff and water quality, which in urban areas are dominated by the most extreme precipitation events. We assess the potential implications of changes in extreme precipitation and changing land cover in urban and urbanizing watersheds at the regional scale using a combination of hydrology and water quality models. Specifically, we describe the integration of a spatially distributed hydrological model - the Distributed Hydrology Soil Vegetation Model (DHSVM), the urban water quality model in EPA's Storm Water Management Model (SWMM), the semi-Lagrangian stream temperature model RBM10, and dynamical and statistical downscaling methods applied to global climate predictions. Key output water quality parameters include total suspended solids (TSS), toal nitrogen, total phosphorous, fecal coliform bacteria and stream temperature. We have evaluated the performance of the modeling system in the highly urbanized Mercer Creek watershed in the rapidly growing Bellevue urban area in WA, USA. The results suggest that the model is able to (1) produce reasonable streamflow predictions at fine temporal and spatial scales; (2) provide spatially distributed water temperature predictions that mostly agree with observations throughout a complex stream network, and characterize impacts of climate, landscape, near-stream vegetation change on stream temperature at local and regional scales; and (3) capture plausibly the response of water quality constituents to varying magnitude of precipitation events in urban environments. Next we will extend the scope of the study from the Mercer Creek watershed to include the entire Puget Sound Basin, WA, USA.

A Self-Assessment Stereo Capture Model Applicable to the Internet of Things

PubMed Central

Lin, Yancong; Yang, Jiachen; Lv, Zhihan; Wei, Wei; Song, Houbing

2015-01-01

The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting. PMID:26308004

NIR spectroscopic measurement of moisture content in Scots pine seeds.

PubMed

Lestander, Torbjörn A; Geladi, Paul

2003-04-01

When tree seeds are used for seedling production it is important that they are of high quality in order to be viable. One of the factors influencing viability is moisture content and an ideal quality control system should be able to measure this factor quickly for each seed. Seed moisture content within the range 3-34% was determined by near-infrared (NIR) spectroscopy on Scots pine (Pinus sylvestris L.) single seeds and on bulk seed samples consisting of 40-50 seeds. The models for predicting water content from the spectra were made by partial least squares (PLS) and ordinary least squares (OLS) regression. Different conditions were simulated involving both using less wavelengths and going from samples to single seeds. Reflectance and transmission measurements were used. Different spectral pretreatment methods were tested on the spectra. Including bias, the lowest prediction errors for PLS models based on reflectance within 780-2280 nm from bulk samples and single seeds were 0.8% and 1.9%, respectively. Reduction of the single seed reflectance spectrum to 850-1048 nm gave higher biases and prediction errors in the test set. In transmission (850-1048 nm) the prediction error was 2.7% for single seeds. OLS models based on simulated 4-sensor single seed system consisting of optical filters with Gaussian transmission indicated more than 3.4% error in prediction. A practical F-test based on test sets to differentiate models is introduced.

Prediction of global and local model quality in CASP8 using the ModFOLD server.

PubMed

McGuffin, Liam J

2009-01-01

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0--an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/. Copyright 2009 Wiley-Liss, Inc.

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Quality optimization of H.264/AVC video transmission over noisy environments using a sparse regression framework

NASA Astrophysics Data System (ADS)

Pandremmenou, K.; Tziortziotis, N.; Paluri, S.; Zhang, W.; Blekas, K.; Kondi, L. P.; Kumar, S.

2015-03-01

We propose the use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression method in order to predict the Cumulative Mean Squared Error (CMSE), incurred by the loss of individual slices in video transmission. We extract a number of quality-relevant features from the H.264/AVC video sequences, which are given as input to the LASSO. This method has the benefit of not only keeping a subset of the features that have the strongest effects towards video quality, but also produces accurate CMSE predictions. Particularly, we study the LASSO regression through two different architectures; the Global LASSO (G.LASSO) and Local LASSO (L.LASSO). In G.LASSO, a single regression model is trained for all slice types together, while in L.LASSO, motivated by the fact that the values for some features are closely dependent on the considered slice type, each slice type has its own regression model, in an e ort to improve LASSO's prediction capability. Based on the predicted CMSE values, we group the video slices into four priority classes. Additionally, we consider a video transmission scenario over a noisy channel, where Unequal Error Protection (UEP) is applied to all prioritized slices. The provided results demonstrate the efficiency of LASSO in estimating CMSE with high accuracy, using only a few features. les that typically contain high-entropy data, producing a footprint that is far less conspicuous than existing methods. The system uses a local web server to provide a le system, user interface and applications through an web architecture.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Performance of biometric quality measures.

PubMed

Grother, Patrick; Tabassi, Elham

2007-04-01

We document methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality. We are motivated by a need to test claims that quality measures are predictive of matching performance. We regard a quality measurement algorithm as a black box that converts an input sample to an output scalar. We evaluate it by quantifying the association between those values and observed matching results. We advance detection error trade-off and error versus reject characteristics as metrics for the comparative evaluation of sample quality measurement algorithms. We proceed this with a definition of sample quality, a description of the operational use of quality measures. We emphasize the performance goal by including a procedure for annotating the samples of a reference corpus with quality values derived from empirical recognition scores.

Digital contract approach for consistent and predictable multimedia information delivery in electronic commerce

NASA Astrophysics Data System (ADS)

Konana, Prabhudev; Gupta, Alok; Whinston, Andrew B.

1997-01-01

A pure 'technological' solution to network quality problems is incomplete since any benefits from new technologies are offset by the demand from exponentially growing electronic commerce ad data-intensive applications. SInce an economic paradigm is implicit in electronic commerce, we propose a 'market-system' approach to improve quality of service. Quality of service for digital products takes on a different meaning since users view quality of service differently and value information differently. We propose a framework for electronic commerce that is based on an economic paradigm and mass-customization, and works as a wide-area distributed management system. In our framework, surrogate-servers act as intermediaries between information provides and end- users, and arrange for consistent and predictable information delivery through 'digital contracts.' These contracts are negotiated and priced based on economic principles. Surrogate servers pre-fetched, through replication, information from many different servers and consolidate based on demand expectations. In order to recognize users' requirements and process requests accordingly, real-time databases are central to our framework. We also propose that multimedia information be separated into slowly changing and rapidly changing data streams to improve response time requirements. Surrogate- servers perform the tasks of integration of these data streams that is transparent to end-users.

Quality of Education Predicts Performance on the Wide Range Achievement Test-4th Edition Word Reading Subtest

PubMed Central

Sayegh, Philip; Arentoft, Alyssa; Thaler, Nicholas S.; Dean, Andy C.; Thames, April D.

2014-01-01

The current study examined whether self-rated education quality predicts Wide Range Achievement Test-4th Edition (WRAT-4) Word Reading subtest and neurocognitive performance, and aimed to establish this subtest's construct validity as an educational quality measure. In a community-based adult sample (N = 106), we tested whether education quality both increased the prediction of Word Reading scores beyond demographic variables and predicted global neurocognitive functioning after adjusting for WRAT-4. As expected, race/ethnicity and education predicted WRAT-4 reading performance. Hierarchical regression revealed that when including education quality, the amount of WRAT-4's explained variance increased significantly, with race/ethnicity and both education quality and years as significant predictors. Finally, WRAT-4 scores, but not education quality, predicted neurocognitive performance. Results support WRAT-4 Word Reading as a valid proxy measure for education quality and a key predictor of neurocognitive performance. Future research should examine these findings in larger, more diverse samples to determine their robust nature. PMID:25404004

The Analysis, Numerical Simulation, and Diagnosis of Extratropical Weather Systems

DTIC Science & Technology

2003-09-30

The Analysis, Numerical Simulation, and Diagnosis of Extratropical Weather Systems Dr. Melvyn A. Shapiro NOAA/Office of Weather and Air Quality...predictability of extratropical cyclones. APPROACH My approach toward achieving the above objectives has been to foster national and...TITLE AND SUBTITLE The Analysis, Numerical Simulation, and Diagnosis of Extratropical Weather Systems 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM

Ground-water models for water resources planning

USGS Publications Warehouse

Moore, John E.

1980-01-01

In the past decade hydrologists have emphasized the development of computer-based mathematical models to aid in the understanding of flow, the transport of solutes, transport of heat, and deformation in the groundwater system. These models have been used to provide information and predictions for water managers. Too frequently, groundwater was neglected in water-resource planning because managers believed that it could not be adequately evaluated in terms of availability, quality, and effect of development on surface water supplies. Now, however, with newly developed digital groundwater models, effects of development can be predicted. Such models have been used to predict hydrologic and quality changes under different stresses. These models have grown in complexity over the last 10 years from simple one-layer flow models to three-dimensional simulations of groundwater flow which may include solute transport, heat transport, effects of land subsidence, and encroachment of salt water. This paper illustrates, through case histories, how predictive groundwater models have provided the information needed for the sound planning and management of water resources in the United States. (USGS)

a System Dynamics Model to Study the Importance of Infrastructure Facilities on Quality of Primary Education System in Developing Countries

NASA Astrophysics Data System (ADS)

Pedamallu, Chandra Sekhar; Ozdamar, Linet; Weber, Gerhard-Wilhelm; Kropat, Erik

2010-06-01

The system dynamics approach is a holistic way of solving problems in real-time scenarios. This is a powerful methodology and computer simulation modeling technique for framing, analyzing, and discussing complex issues and problems. System dynamics modeling and simulation is often the background of a systemic thinking approach and has become a management and organizational development paradigm. This paper proposes a system dynamics approach for study the importance of infrastructure facilities on quality of primary education system in developing nations. The model is proposed to be built using the Cross Impact Analysis (CIA) method of relating entities and attributes relevant to the primary education system in any given community. We offer a survey to build the cross-impact correlation matrix and, hence, to better understand the primary education system and importance of infrastructural facilities on quality of primary education. The resulting model enables us to predict the effects of infrastructural facilities on the access of primary education by the community. This may support policy makers to take more effective actions in campaigns.

Detailed design of a Ride Quality Augmentation System for commuter aircraft

NASA Technical Reports Server (NTRS)

Suikat, Reiner; Donaldson, Kent E.; Downing, David R.

1989-01-01

The design of a Ride Quality Augmentation System (RQAS) for commuter aircraft is documented. The RQAS is designed for a Cessna 402B, an 8 passenger prop twin representative to this class of aircraft. The purpose of the RQAS is the reduction of vertical and lateral accelerations of the aircraft due to atmospheric turbulence by the application of active control. The detailed design of the hardware (the aircraft modifications, the Ride Quality Instrumentation System (RQIS), and the required computer software) is examined. The aircraft modifications, consisting of the dedicated control surfaces and the hydraulic actuation system, were designed at Cessna Aircraft by Kansas University-Flight Research Laboratory. The instrumentation system, which consist of the sensor package, the flight computer, a Data Acquisition System, and the pilot and test engineer control panels, was designed by NASA-Langley. The overall system design and the design of the software, both for flight control algorithms and ground system checkout are detailed. The system performance is predicted from linear simulation results and from power spectral densities of the aircraft response to a Dryden gust. The results indicate that both accelerations are possible.

An analytical approach for predicting pilot induced oscillations

NASA Technical Reports Server (NTRS)

Hess, R. A.

1981-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion or determining the susceptability of an aircraft to pilot induced oscillations (PIO) is formulated. Finally, a model-based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

A simple next-best alternative to seasonal predictions in Europe

NASA Astrophysics Data System (ADS)

Buontempo, Carlo; De Felice, Matteo

2016-04-01

In order to build a climate proof society, we need to learn how to best use the climate information we have. Having spent time and resources in developing complex numerical models has often blinded us on the value some of this information really has in the eyes of a decision maker. An effective way to assess this is to check the quality of the forecast (and its cost) to the quality of the forecast from a prediction system based on simpler assumption (and thus cheaper to run). Such a practice is common in marketing analysis where it is often referred to as the next-best alternative. As a way to facilitate such an analysis, climate service providers should always provide alongside the predictions a set of skill scores. These are usually based on climatological means, anomaly persistence or more recently multiple linear regressions. We here present an equally simple benchmark based on a Markov chain process locally trained at a monthly or seasonal time-scale. We demonstrate that in spite of its simplicity the model easily outperforms not only the standard benchmark but also most of the seasonal predictions system at least in EUROPE. We suggest that a benchmark of this kind could represent a useful next-best alternative for a number of users.

Quantification of Water Quality Parameters for the Wabash River Using Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Tan, J.; Cherkauer, K. A.; Chaubey, I.

2011-12-01

Increasingly impaired water bodies in the agriculturally dominated Midwestern United States pose a risk to water supplies, aquatic ecology and contribute to the eutrophication of the Gulf of Mexico. Improving regional water quality calls for new techniques for monitoring and managing water quality over large river systems. Optical indicators of water quality enable a timely and cost-effective method for observing and quantifying water quality conditions by remote sensing. Compared to broad spectral sensors such as Landsat, which observe reflectance over limited spectral bands, hyperspectral sensors should have significant advantages in their ability to estimate water quality parameters because they are designed to split the spectral signature into hundreds of very narrow spectral bands increasing their ability to resolve optically sensitive water quality indicators. Two airborne hyperspectral images were acquired over the Wabash River using a ProSpecTIR-VS2 sensor system on May 15th, 2010. These images were analyzed together with concurrent in-stream water quality data collected to assess our ability to extract optically sensitive constituents. Utilizing the correlation between in-stream data and reflectance from the hyperspectral images, models were developed to estimate the concentrations of chlorophyll a, dissolved organic carbon and total suspended solids. Models were developed using the full array of hyperspectral bands, as well as Landsat bands synthesized by averaging hyperspectral bands within the Landsat spectral range. Higher R2 and lower RMSE values were found for the models taking full advantage of the hyperspectral sensor, supporting the conclusion that the hyperspectral sensor was better at predicting the in-stream concentrations of chlorophyll a, dissolved organic carbon and total suspended solids in the Wabash River. Results also suggest that predictive models may not be the same for the Wabash River as for its tributaries.

Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

PubMed Central

McCoy, Andrea

2017-01-01

Introduction Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach. Methods In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow. Results Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%. Conclusion The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC. PMID:29450295

No-Reference Image Quality Assessment by Wide-Perceptual-Domain Scorer Ensemble Method.

PubMed

Liu, Tsung-Jung; Liu, Kuan-Hsien

2018-03-01

A no-reference (NR) learning-based approach to assess image quality is presented in this paper. The devised features are extracted from wide perceptual domains, including brightness, contrast, color, distortion, and texture. These features are used to train a model (scorer) which can predict scores. The scorer selection algorithms are utilized to help simplify the proposed system. In the final stage, the ensemble method is used to combine the prediction results from selected scorers. Two multiple-scale versions of the proposed approach are also presented along with the single-scale one. They turn out to have better performances than the original single-scale method. Because of having features from five different domains at multiple image scales and using the outputs (scores) from selected score prediction models as features for multi-scale or cross-scale fusion (i.e., ensemble), the proposed NR image quality assessment models are robust with respect to more than 24 image distortion types. They also can be used on the evaluation of images with authentic distortions. The extensive experiments on three well-known and representative databases confirm the performance robustness of our proposed model.

Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

PubMed

Ding, Jinliang; Chai, Tianyou; Wang, Hong

2011-03-01

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Proceedings of the First National Workshop on the Global Weather Experiment: Current Achievements and Future Directions, volume 2, part 1

NASA Technical Reports Server (NTRS)

1985-01-01

Topics covered include: data systems and quality; analysis and assimilation techniques; impacts on forecasts; tropical forecasts; analysis intercomparisons; improvements in predictability; and heat sources and sinks.

PICASSO: an end-to-end image simulation tool for space and airborne imaging systems

NASA Astrophysics Data System (ADS)

Cota, Steve A.; Bell, Jabin T.; Boucher, Richard H.; Dutton, Tracy E.; Florio, Chris J.; Franz, Geoffrey A.; Grycewicz, Thomas J.; Kalman, Linda S.; Keller, Robert A.; Lomheim, Terrence S.; Paulson, Diane B.; Willkinson, Timothy S.

2008-08-01

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

PICASSO: an end-to-end image simulation tool for space and airborne imaging systems

NASA Astrophysics Data System (ADS)

Cota, Stephen A.; Bell, Jabin T.; Boucher, Richard H.; Dutton, Tracy E.; Florio, Christopher J.; Franz, Geoffrey A.; Grycewicz, Thomas J.; Kalman, Linda S.; Keller, Robert A.; Lomheim, Terrence S.; Paulson, Diane B.; Wilkinson, Timothy S.

2010-06-01

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

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.

AN EXPERT SYSTEM FOR HYDRODYNAMIC MIXING ZONE ANAYLSIS OF CONVENTIONAL AND TOXIC SUBMERGED SINGLE PORT DISCHARGES (CORMIX1)

EPA Science Inventory

U.S. water quality policy includes the concept of a mixing zone, a limited area or volume of water where the initial dilution of a discharge occurs. he Cornell Mixing Zone Expert System (CORMIX1) was developed to predict the dilution and trajectory of a submerged single port disc...

Adaptation and validation of the REGEN expert system for the Central Appalachians

Treesearch

Lance A. Vickers; Thomas R. Fox; David L. Loftis; David A. Boucugnani

2011-01-01

REGEN is an expert system that predicts future species composition at the onset of stem exclusion using preharvest stand conditions. To extend coverage into hardwood stands of the Central Appalachians, we developed REGEN knowledge bases for four site qualities (xeric, subxeric, submesic, mesic) based on relevant literature and expert opinion. Data were collected from...

Climate impact on airborne particulate matter concentrations in California using seven year analysis periods

NASA Astrophysics Data System (ADS)

Mahmud, A.; Hixson, M.; Hu, J.; Zhao, Z.; Chen, S.-H.; Kleeman, M. J.

2010-11-01

The effect of global climate change on the annual average concentration of fine particulate matter (PM2.5) in California was studied using a climate-air quality modeling system composed of global through regional models. Output from the NCAR/DOE Parallel Climate Model (PCM) generated under the "business as usual" global emissions scenario was downscaled using the Weather Research and Forecasting (WRF) model followed by air quality simulations using the UCD/CIT airshed model. The system represents major atmospheric processes acting on gas and particle phase species including meteorological effects on emissions, advection, dispersion, chemical reaction rates, gas-particle conversion, and dry/wet deposition. The air quality simulations were carried out for the entire state of California with a resolution of 8-km for the years 2000-2006 (present climate with present emissions) and 2047-2053 (future climate with present emissions). Each of these 7-year analysis periods was analyzed using a total of 1008 simulated days to span a climatologically relevant time period with a practical computational burden. The 7-year windows were chosen to properly account for annual variability with the added benefit that the air quality predictions under the present climate could be compared to actual measurements. The climate-air quality modeling system successfully predicted the spatial pattern of present climate PM2.5 concentrations in California but the absolute magnitude of the annual average PM2.5 concentrations were under-predicted by ~4-39% in the major air basins. The majority of this under-prediction was caused by excess ventilation predicted by PCM-WRF that should be present to the same degree in the current and future time periods so that the net bias introduced into the comparison is minimized. Surface temperature, relative humidity (RH), rain rate, and wind speed were predicted to increase in the future climate while the ultra violet (UV) radiation was predicted to decrease in major urban areas in the San Joaquin Valley (SJV) and South Coast Air Basin (SoCAB). These changes lead to a predicted decrease in PM2.5 mass concentrations of ~0.3-0.7 μg m-3 in the southern portion of the SJV and ~0.3-1.1 μg m-3 along coastal regions of California including the heavily populated San Francisco Bay Area and the SoCAB surrounding Los Angeles. Annual average PM2.5 concentrations were predicted to increase at certain locations within the SJV and the Sacramento Valley (SV) due to the effects of climate change, but a corresponding analysis of the annual variability showed that these predictions are not statistically significant (i.e. the choice of a different 7-year period could produce a different outcome for these regions). Overall, virtually no region in California outside of coastal + central Los Angeles, and a small region around the port of Oakland in the San Francisco Bay Area experienced a statistically significant change in annual average PM2.5 concentrations due to the effects of climate change in the present~study. The present study employs the highest spatial resolution (8 km) and the longest analysis windows (7 years) of any climate-air quality analysis conducted for California to date, but the results still have some degree of uncertainty. Most significantly, GCM calculations have inherent uncertainty that is not fully represented in the current study since a single GCM was used as the starting point for all calculations. The PCM results used in the current study predicted greater wintertime increases in air temperature over the Pacific Ocean than over land, further motivating comparison to other GCM results. Ensembles of GCM results are usually employed to build confidence in climate calculations. The current results provide a first data-point for the climate-air quality analysis that simultaneously employ the fine spatial resolution and long time scales needed to capture the behavior of climate-PM2.5 interactions in California. Future downscaling studies should follow up with a full ensemble of GCMs as their starting point, and include aerosol feedback effects on local meteorology.

ATLAS trigger operations: Upgrades to ``Xmon'' rate prediction system

NASA Astrophysics Data System (ADS)

Myers, Ava; Aukerman, Andrew; Hong, Tae Min; Atlas Collaboration

2017-01-01

We present ``Xmon,'' a tool to monitor trigger rates in the Control Room of the ATLAS Experiment. We discuss Xmon's recent (1) updates, (2) upgrades, and (3) operations. (1) Xmon was updated to modify the tool written for the three-level trigger architecture in Run-1 (2009-2012) to adapt to the new two-level system for Run-2 (2015-current). The tool takes as input the beam luminosity to make a rate prediction, which is compared with incoming rates to detect anomalies that occur both globally throughout a run and locally within a run. Global offsets are more commonly caught by the predictions based upon past runs, where offline processing allows for function adjustments and fit quality through outlier rejection. (2) Xmon was upgraded to detect local offsets using on-the-fly predictions, which uses a sliding window of in-run rates to make predictions. (3) Xmon operations examples are given. Future work involves further automation of the steps to provide the predictive functions and for alerting shifters.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

PubMed

Bedoya, David; Manolakos, Elias S; Novotny, Vladimir

2011-03-01

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. Copyright © 2011 Elsevier Ltd. All rights reserved.

Is there a preference for linearity when viewing natural images?

NASA Astrophysics Data System (ADS)

Kane, David; Bertamío, Marcelo

2015-01-01

The system gamma of the imaging pipeline, defined as the product of the encoding and decoding gammas, is typically greater than one and is stronger for images viewed with a dark background (e.g. cinema) than those viewed in lighter conditions (e.g. office displays).1-3 However, for high dynamic range (HDR) images reproduced on a low dynamic range (LDR) monitor, subjects often prefer a system gamma of less than one,4 presumably reflecting the greater need for histogram equalization in HDR images. In this study we ask subjects to rate the perceived quality of images presented on a LDR monitor using various levels of system gamma. We reveal that the optimal system gamma is below one for images with a HDR and approaches or exceeds one for images with a LDR. Additionally, the highest quality scores occur for images where a system gamma of one is optimal, suggesting a preference for linearity (where possible). We find that subjective image quality scores can be predicted by computing the degree of histogram equalization of the lightness distribution. Accordingly, an optimal, image dependent system gamma can be computed that maximizes perceived image quality.

Progress and lessons learned from water-quality monitoring networks

USGS Publications Warehouse

Myers, Donna N.; Ludtke, Amy S.

2017-01-01

Stream-quality monitoring networks in the United States were initiated and expanded after passage of successive federal water-pollution control laws from 1948 to 1972. The first networks addressed information gaps on the extent and severity of stream pollution and served as early warning systems for spills. From 1965 to 1972, monitoring networks expanded to evaluate compliance with stream standards, track emerging issues, and assess water-quality status and trends. After 1972, concerns arose regarding the ability of monitoring networks to determine if water quality was getting better or worse and why. As a result, monitoring networks adopted a hydrologic systems approach targeted to key water-quality issues, accounted for human and natural factors affecting water quality, innovated new statistical methods, and introduced geographic information systems and models that predict water quality at unmeasured locations. Despite improvements, national-scale monitoring networks have declined over time. Only about 1%, or 217, of more than 36,000 US Geological Survey monitoring sites sampled from 1975 to 2014 have been operated throughout the four decades since passage of the 1972 Clean Water Act. Efforts to sustain monitoring networks are important because these networks have collected information crucial to the description of water-quality trends over time and are providing information against which to evaluate future trends.

Questions and Answers About the Effects of Septic Systems on Water Quality in the La Pine Area, Oregon

USGS Publications Warehouse

Williams, John S.; Morgan, David S.; Hinkle, Stephen R.

2007-01-01

Nitrate levels in the ground-water aquifer underlying the central Oregon city of La Pine and the surrounding area are increasing due to contamination from residential septic systems. This contamination has public health implications because ground water is the sole source of drinking water for area residents. The U.S. Geological Survey, in cooperation with Deschutes County and the Oregon Department of Environmental Quality, studied the movement and chemistry of nitrate in the aquifer and developed computer models that can be used to predict future nitrate levels and to evaluate alternatives for protecting water quality. This fact sheet summarizes the results of that study in the form of questions and answers.

Space Weather Effects on Spacecraft Systems

NASA Technical Reports Server (NTRS)

Barth, Janet L.

2003-01-01

Space-based systems are developing into critical infrastructure required to support the quality of life on Earth. Hence, spacecraft reliability is a serious issue that is complicated by exposure to the space environment. Complex mission designs along with rapidly evolving technologies have outpaced efforts to accommodate detrimental space environment impacts on systems. Hazardous space environments, the effects on systems, and the accommodation of the effects are described with a focus on the need to predict space environments.

Evaluating the impact of AMDAR data quality control in China on the short-range convection forecasts using the WRF model

NASA Astrophysics Data System (ADS)

Wang, Xiaofeng; Jiang, Qin; Zhang, Lei

2016-04-01

A quality control system for the Aircraft Meteorological Data Relay (AMDAR) data has been implemented in China. This system is an extension to the AMDAR quality control system used at the US National Centers for Environmental Prediction. We present a study in which the characteristics of each AMDAR data quality type were examined and the impact of the AMDAR data quality system on short-range convective weather forecasts using the WRF model was investigated. The main results obtained from this study are as follows. (1) The hourly rejection rate of AMDAR data during 2014 was 5.79%, and most of the rejections happened in Near Duplicate Check. (2) There was a significant diurnal variation for both quantity and quality of AMDAR data. Duplicated reports increased with the increase of data quantity, while suspicious and disorderly reports decreased with the increase of data quantity. (3) The characteristics of the data quality were different in each model layer, with the quality problems occurring mainly at the surface as well as at the height where the power or the flight mode of the aircraft underwent adjustment. (4) Assimilating the AMDAR data improved the forecast accuracy, particularly over the region where strong convection occurred. (5) Significant improvements made by assimilating AMDAR data were found after six hours into the model forecast. The conclusion from this study is that the newly implemented AMDAR data quality system can help improve the accuracy of short-range convection forecasts using the WRF model.

Efficient depth intraprediction method for H.264/AVC-based three-dimensional video coding

NASA Astrophysics Data System (ADS)

Oh, Kwan-Jung; Oh, Byung Tae

2015-04-01

We present an intracoding method that is applicable to depth map coding in multiview plus depth systems. Our approach combines skip prediction and plane segmentation-based prediction. The proposed depth intraskip prediction uses the estimated direction at both the encoder and decoder, and does not need to encode residual data. Our plane segmentation-based intraprediction divides the current block into biregions, and applies a different prediction scheme for each segmented region. This method avoids incorrect estimations across different regions, resulting in higher prediction accuracy. Simulation results demonstrate that the proposed scheme is superior to H.264/advanced video coding intraprediction and has the ability to improve the subjective rendering quality.

The Effects of Organization Design and Patient Perceptions of Care on Switching Behavior and Reliance on a Health Care System Across Time.

PubMed

Labonte, Alan J; Benzer, Justin K; Burgess, James F; Cramer, Irene E; Meterko, Mark; Pogoda, Terri K; Charns, Martin P

2016-04-01

Sustaining ongoing relationships with patients is a strategic, clinically relevant goal of health care systems. This study develops and tests a conceptual model that aims to account for the influence of organization design, perceptions of quality of patient care, and other patient-level factors on the extent to which patients sustain reliance on a health care system. We use a longitudinal survey design and structural equation modeling to predict increases or decreases in patient reliance on the Department of Veterans Affairs health care system across a 4-year period for Veterans with Parkinson's Disease. Our findings show that specialized and integrated clinical practices have a positive association with the quality of patient care. Health care systems may be able to foster long-term relations with patients and improve service quality by allocating resources to form integrated, specialized, disease-specific centers of care designed for patients with chronic illnesses. © The Author(s) 2016.

Observing system simulations using synthetic radiances and atmospheric retrievals derived for the AMSU and HIRS in a mesoscale model. [Advanced Microwave Sounding Unit

NASA Technical Reports Server (NTRS)

Diak, George R.; Huang, Hung-Lung; Kim, Dongsoo

1990-01-01

The paper addresses the concept of synthetic satellite imagery as a visualization and diagnostic tool for understanding satellite sensors of the future and to detail preliminary results on the quality of soundings from the current sensors. Preliminary results are presented on the quality of soundings from the combination of the High-Resolution Infrared Radiometer Sounder and the Advanced Microwave Sounding Unit. Results are also presented on the first Observing System Simulation Experiment using this data in a mesoscale numerical prediction model.

GenePRIMP: A Gene Prediction Improvement Pipeline For Prokaryotic Genomes

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

Kyrpides, Nikos C.; Ivanova, Natalia N.; Pati, Amrita

2010-07-08

GenePRIMP (Gene Prediction Improvement Pipeline, Http://geneprimp.jgi-psf.org), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missing genes, and split genes. We show that manual curation of gene models using the anomaly reports generated by GenePRIMP improves their quality and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome sequencing and annotation technologies. Keywords in context: Gene model, Quality Control, Translation start sites, Automatic correction. Hardware requirements; PC, MAC; Operating System: UNIX/LINUX; Compiler/Version: Perl 5.8.5 or higher; Special requirements: NCBI Blast and nr installation; File Types:more » Source Code, Executable module(s), Sample problem input data; installation instructions other; programmer documentation. Location/transmission: http://geneprimp.jgi-psf.org/gp.tar.gz« less

The use of the general image quality equation in the design and evaluation of imaging systems

NASA Astrophysics Data System (ADS)

Cota, Steve A.; Florio, Christopher J.; Duvall, David J.; Leon, Michael A.

2009-08-01

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. The National Imagery Interpretability Rating Scale (NIIRS) is a useful measure of image quality, because, by characterizing the overall interpretability of an image, it combines into one metric those contributors to image quality to which a human interpreter is most sensitive. The main drawback to using a NIIRS rating as a measure of image quality in engineering trade studies is the fact that it is tied to the human observer and cannot be predicted from physical principles and engineering parameters alone. The General Image Quality Equation (GIQE) of Leachtenauer et al. 1997 [Appl. Opt. 36, 8322-8328 (1997)] is a regression of actual image analyst NIIRS ratings vs. readily calculable engineering metrics, and provides a mechanism for using the expected NIIRS rating of an imaging system in the design and evaluation process. In this paper, we will discuss how we use the GIQE in conjunction with The Aerospace Corporation's Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) to evaluate imager designs, taking a hypothetical high resolution commercial imaging system as an example.

Synthesized view comparison method for no-reference 3D image quality assessment

NASA Astrophysics Data System (ADS)

Luo, Fangzhou; Lin, Chaoyi; Gu, Xiaodong; Ma, Xiaojun

2018-04-01

We develop a no-reference image quality assessment metric to evaluate the quality of synthesized view rendered from the Multi-view Video plus Depth (MVD) format. Our metric is named Synthesized View Comparison (SVC), which is designed for real-time quality monitoring at the receiver side in a 3D-TV system. The metric utilizes the virtual views in the middle which are warped from left and right views by Depth-image-based rendering algorithm (DIBR), and compares the difference between the virtual views rendered from different cameras by Structural SIMilarity (SSIM), a popular 2D full-reference image quality assessment metric. The experimental results indicate that our no-reference quality assessment metric for the synthesized images has competitive prediction performance compared with some classic full-reference image quality assessment metrics.

Perceptual quality prediction on authentically distorted images using a bag of features approach

PubMed Central

Ghadiyaram, Deepti; Bovik, Alan C.

2017-01-01

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417

Development of a Simulation Capability for the Space Station Active Rack Isolation System

NASA Technical Reports Server (NTRS)

Johnson, Terry L.; Tolson, Robert H.

1998-01-01

To realize quality microgravity science on the International Space Station, many microgravity facilities will utilize the Active Rack Isolation System (ARIS). Simulation capabilities for ARIS will be needed to predict the microgravity environment. This paper discusses the development of a simulation model for use in predicting the performance of the ARIS in attenuating disturbances with frequency content between 0.01 Hz and 10 Hz. The derivation of the model utilizes an energy-based approach. The complete simulation includes the dynamic model of the ISPR integrated with the model for the ARIS controller so that the entire closed-loop system is simulated. Preliminary performance predictions are made for the ARIS in attenuating both off-board disturbances as well as disturbances from hardware mounted onboard the microgravity facility. These predictions suggest that the ARIS does eliminate resonant behavior detrimental to microgravity experimentation. A limited comparison is made between the simulation predictions of ARIS attenuation of off-board disturbances and results from the ARIS flight test. These comparisons show promise, but further tuning of the simulation is needed.

Comparison of National Operative Mortality in Gastroenterological Surgery Using Web-based Prospective Data Entry Systems.

PubMed

Anazawa, Takayuki; Paruch, Jennifer L; Miyata, Hiroaki; Gotoh, Mitsukazu; Ko, Clifford Y; Cohen, Mark E; Hirahara, Norimichi; Zhou, Lynn; Konno, Hiroyuki; Wakabayashi, Go; Sugihara, Kenichi; Mori, Masaki

2015-12-01

International collaboration is important in healthcare quality evaluation; however, few international comparisons of general surgery outcomes have been accomplished. Furthermore, predictive model application for risk stratification has not been internationally evaluated. The National Clinical Database (NCD) in Japan was developed in collaboration with the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), with a goal of creating a standardized surgery database for quality improvement. The study aimed to compare the consistency and impact of risk factors of 3 major gastroenterological surgical procedures in Japan and the United States (US) using web-based prospective data entry systems: right hemicolectomy (RH), low anterior resection (LAR), and pancreaticoduodenectomy (PD).Data from NCD and ACS-NSQIP, collected over 2 years, were examined. Logistic regression models were used for predicting 30-day mortality for both countries. Models were exchanged and evaluated to determine whether the models built for one population were accurate for the other population.We obtained data for 113,980 patients; 50,501 (Japan: 34,638; US: 15,863), 42,770 (Japan: 35,445; US: 7325), and 20,709 (Japan: 15,527; US: 5182) underwent RH, LAR, and, PD, respectively. Thirty-day mortality rates for RH were 0.76% (Japan) and 1.88% (US); rates for LAR were 0.43% versus 1.08%; and rates for PD were 1.35% versus 2.57%. Patient background, comorbidities, and practice style were different between Japan and the US. In the models, the odds ratio for each variable was similar between NCD and ACS-NSQIP. Local risk models could predict mortality using local data, but could not accurately predict mortality using data from other countries.We demonstrated the feasibility and efficacy of the international collaborative research between Japan and the US, but found that local risk models remain essential for quality improvement.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

Quality of work life as a predictor of nurses' intention to leave units, organisations and the profession.

PubMed

Lee, Ya-Wen; Dai, Yu-Tzu; McCreary, Linda L

2015-05-01

To examine the relationships between quality of work life (QWL) and nurses' intention to leave their unit (ITLunit), organisation (ITLorg) and profession (ITLpro). The high turnover rate among nurses presents a major challenge to health care systems across the globe. QWL plays a significant role in nurses' turnover. A descriptive cross-sectional survey design was conducted via purposive sampling of 1283 hospital nurses and administering the Chinese version of the Quality of Nursing Work Life scale (C-QNWL), a three-ITL-type scale questionnaire, and a demographic questionnaire for individual- and work-related variables. Descriptive data, correlations, and ordinal regression models were analyzed. QWL predicted ITLpro and ITLorg better than ITLunit. Three QWL dimensions (work arrangement and workload, nursing staffing and patient care, and work-home life balance) were significantly predictive of all three ITL measures. However, the dimension of teamwork and communication was only predictive for ITLunit, not for ITLorg and ITLpro. Different patterns of QWL dimensions are predictive of ITLunit, ITLorg, and ITLpro. The study provides important information to nurse administrators about the aspects of QWL that most commonly lead nurses to leave their units, organisations, and even the profession itself. © 2013 John Wiley & Sons Ltd.

FSO and quality of service software prediction

NASA Astrophysics Data System (ADS)

Bouchet, O.; Marquis, T.; Chabane, M.; Alnaboulsi, M.; Sizun, H.

2005-08-01

Free-space optical (FSO) communication links constitute an alternative option to radio relay links and to optical cables facing growth needs in high-speed telecommunications (abundance of unregulated bandwidth, rapid installation, availability of low-cost optical components offering a high data rate, etc). Their operationalisation requires a good knowledge of the atmospheric effects which can negatively affect role propagation and the availability of the link, and thus to the quality of service (QoS). Better control of these phenomena will allow for the evaluation of system performance and thus assist with improving reliability. The aim of this paper is to compare the behavior of a FSO link located in south of France (Toulouse: with the following parameters: around 270 meters (0.2 mile) long, 34 Mbps data rate, 850 nm wavelength and PDH frame) with airport meteorological data. The second aim of the paper is to assess in-house FSO quality of service prediction software, through comparing simulations with the optical link data and the weather data. The analysis uses in-house software FSO quality of service prediction software ("FSO Prediction") developed by France Telecom Research & Development, which integrates news fog fading equations (compare to Kim & al.) and includes multiple effects (geometrical attenuation, atmospheric fading, rain, snow, scintillation and refraction attenuation due to atmospheric turbulence, optical mispointing attenuation). The FSO link field trial, intended to enable the demonstration and evaluation of these different effects, is described; and preliminary results of the field trial, from December 2004 to May 2005, are then presented.

The role of non-financial performance measures in predicting hospital financial performance: the case of for-profit system hospitals.

PubMed

Vélez-González, Heltie; Pradhan, Rohit; Weech-Maldonado, Robert

2011-01-01

Non-financial measures have found increasing acceptance in the business world--however, their application in the health care industry remains limited. The purpose of this article is to understand the influence of non-financial measures (efficiency, productivity, and quality) on the financial performance of for-profit system hospitals. The sample consists of 499 for-profit system hospitals in the United States from 1999 to 2002. Data analyzed include the American Hospital Association's Annual Survey, Medicare Cost Reports, Joint Commission's quality scores, and the Centers for Medicare & Medicaid Services' Hospital Case Mix Index. Dependent variables consist of financial measures (operating and total margins), while independent variables include measures of efficiency, productivity, and quality. Our results suggest the influence of non-financial performance measures on financial performance; occupancy rate positively influences financial performance while greater labor intensity may have negative implications for financial performance. In addition, we show that quality positively influences financial performance thereby offering a potential business case for quality. This result has important managerial and policy implications as it may incentivize capital and human resource investments required to improve hospital quality of care.

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

PubMed

Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-07-12

Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. ACTRN: 12616001488493. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The effect of texture granularity on texture synthesis quality

NASA Astrophysics Data System (ADS)

Golestaneh, S. Alireza; Subedar, Mahesh M.; Karam, Lina J.

2015-09-01

Natural and artificial textures occur frequently in images and in video sequences. Image/video coding systems based on texture synthesis can make use of a reliable texture synthesis quality assessment method in order to improve the compression performance in terms of perceived quality and bit-rate. Existing objective visual quality assessment methods do not perform satisfactorily when predicting the synthesized texture quality. In our previous work, we showed that texture regularity can be used as an attribute for estimating the quality of synthesized textures. In this paper, we study the effect of another texture attribute, namely texture granularity, on the quality of synthesized textures. For this purpose, subjective studies are conducted to assess the quality of synthesized textures with different levels (low, medium, high) of perceived texture granularity using different types of texture synthesis methods.

Investigating attachment, caregiving, and mental health: a model of maternal-fetal relationships.

PubMed

Walsh, Judi; Hepper, Erica G; Marshall, Benjamin J

2014-11-19

Maternal-fetal relationships have been associated with psychosocial outcomes for women and children, but there has been a lack of conceptual clarity about the nature of the maternal relationship with the unborn child, and inconsistent findings assessing its predictors. We proposed and tested a model whereby maternal-fetal relationship quality was predicted by factors relating to the quality of the couple relationship and psychological health. We hypothesized that the contribution of individual differences in romantic attachment shown in past research would be mediated by romantic caregiving responsiveness, as maternal-fetal relationships reflect the beginnings of the caregiving system. 258 women in pregnancy (13, 23, and 33-weeks gestation) completed online measures of attachment to partner, caregiving responsiveness to partner, mental health, and thoughts about their unborn baby. Structural equation modeling was used to test a model of maternal-fetal relationships. Maternal-fetal relationship quality was higher for women at 23-weeks than 13-weeks gestation. Women in first pregnancies had higher self-reported scores of psychological functioning and quality of maternal-fetal relationships than women in subsequent pregnancies. Structural equation models indicated that the quality of the maternal-fetal relationship was best predicted by romantic caregiving responsiveness to partner and women's own psychological health, and that the association between adult romantic attachment avoidance and maternal-fetal relationships was fully mediated by caregiving responsiveness to partner, even after controlling for other factors. These data support the hypothesis that maternal-fetal relationships better reflect the operation of the caregiving system than the care-seeking (i.e., attachment) system. Models of maternal-fetal relationships and interventions with couples should consider the role of caregiving styles of mothers to partners and the relationship between expectant parents alongside other known predictors, particularly psychological health.

Significance of chick quality score in broiler production.

PubMed

van de Ven, L J F; van Wagenberg, A V; Uitdehaag, K A; Groot Koerkamp, P W G; Kemp, B; van den Brand, H

2012-10-01

The quality of day old chicks is crucial for profitable broiler production, but a difficult trait to define. In research, both qualitative and quantitative measures are used with variable predictive value for subsequent performance. In hatchery practice, chick quality is judged on a binomial scale, as chicks are divided into first grade (Q1-saleable) and second grade (Q2) chicks right after hatch. Incidences and reasons for classifying chicks as Q2, and potential of these chicks for survival and post-hatch performance have hardly been investigated, but may provide information for flock performance. We conducted an experiment to investigate (1) the quality of a broiler flock and the relation with post-hatch flock performance based on a qualitative score (Pasgar©score) of Q1 chicks and based on the incidence of Q2 chicks and (2) the reasons for classifying chicks as Q2, and the potential of these chicks for survival and post-hatch growth. The performance was followed of Q1 and Q2 chicks obtained from two breeder flocks that hatched in two different hatching systems (a traditional hatcher or a combined hatching and brooding system, named Patio). Eggs were incubated until embryo day 18, when they were transferred to one of the two hatching systems. At embryo day 21/post-hatch day 0, all chicks from the hatcher (including Q2 chicks) were brought to Patio, where the hatchery manager marked the Q2 chicks from both flocks and hatching systems and registered apparent reasons for classifying these chicks as Q2. Chick quality was assessed of 100 Q1 chicks from each flock and hatching system. Weights of all chicks were determined at days 0, 7, 21 and 42. There were no correlations between mean Pasgar©score and post-hatch growth or mortality, and suboptimal navel quality was the only quality trait associated with lower post-hatch growth. Growth was clearly affected by breeder flock and hatching system, which could not be linked to mean Pasgar©score or incidence of Q2 chicks. Q2 chicks showed lower post-hatch growth compared to Q1 chicks but effects on flock performance at slaughter weight were limited because early mortality in Q2 chicks was high (62.50% at 7 days). We concluded that chick qualitative scores and the incidence of Q2 chicks may be informative for the quality of incubation, but are not predictive for post-hatch flock performance. Culling Q2 chicks after hatch is well-founded in terms of both animal welfare and profitability.

Software Requirements Analysis as Fault Predictor

NASA Technical Reports Server (NTRS)

Wallace, Dolores

2003-01-01

Waiting until the integration and system test phase to discover errors leads to more costly rework than resolving those same errors earlier in the lifecycle. Costs increase even more significantly once a software system has become operational. WE can assess the quality of system requirements, but do little to correlate this information either to system assurance activities or long-term reliability projections - both of which remain unclear and anecdotal. Extending earlier work on requirements accomplished by the ARM tool, measuring requirements quality information against code complexity and test data for the same system may be used to predict specific software modules containing high impact or deeply embedded faults now escaping in operational systems. Such knowledge would lead to more effective and efficient test programs. It may enable insight into whether a program should be maintained or started over.

Nonlinear Dynamic Inversion Baseline Control Law: Flight-Test Results for the Full-scale Advanced Systems Testbed F/A-18 Airplane

NASA Technical Reports Server (NTRS)

Miller, Christopher J.

2011-01-01

A model reference nonlinear dynamic inversion control law has been developed to provide a baseline controller for research into simple adaptive elements for advanced flight control laws. This controller has been implemented and tested in a hardware-in-the-loop simulation and in flight. The flight results agree well with the simulation predictions and show good handling qualities throughout the tested flight envelope with some noteworthy deficiencies highlighted both by handling qualities metrics and pilot comments. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as simple as possible to easily allow the addition of the adaptive elements. The flight-test results and how they compare to the simulation predictions are discussed, along with a discussion about how each element affected pilot opinions. Additionally, aspects of the design that performed better than expected are presented, as well as some simple improvements that will be suggested for follow-on work.

Personal and macro-systemic factors as predictors of quality of life in chronic schizophrenia.

PubMed

Fontanil-Gómez, Yolanda; Alcedo Rodríguez, María A; Gutiérrez López, María I

2017-05-01

The goal of this research was to establish possible predictive factors for both subjective and externally assessed quality of life in people with chronic schizophrenia. Sixty-eight people with schizophrenia took part in the study and were assessed using the World Health Organisation Quality of Life Assessment - Brief Version (WHOQOL-BREF), the Quality of Life Scale (QLS), the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), the Global Assessment of Functioning (GAF), the Social Functioning Scale (SFS) tests. Correlations and multiple regression analysis were conducted to determine possible predictors of quality of life. The residential environment (rural/urban), diagnosis, age at onset of disorder, global functioning and social functioning explained 68% of the total variance based on proxies’ assessment quality of life. Living arrangements and social functioning emerged as predictor variables for subjective quality of life, explaining a 47.3% of the total variance. Socio-cultural factors, such as social integration or the quality of interpersonal relationships, have more influence on these peoples’ physical and psychological health than certain personal factors, such as psychopathology. It is therefore advisable to pay attention to the environment and macro-systemic variables when developing intervention plans to improve their quality of life.

Infrared Imagery of Shuttle (IRIS). Task 2, summary report

NASA Technical Reports Server (NTRS)

Chocol, C. J.

1978-01-01

End-to-end tests of a 16 element indium antimonide sensor array and 10 channels of associated electronic signal processing were completed. Quantitative data were gathered on system responsivity, frequency response, noise, stray capacitance effects, and sensor paralleling. These tests verify that the temperature accuracies, predicted in the Task 1 study, can be obtained with a very carefully designed electro-optical flight system. Pre-flight and inflight calibration of a high quality are mandatory to obtain these accuracies. Also, optical crosstalk in the array-dewar assembly must be carefully eliminated by its design. Tests of the scaled up tracking system reticle also demonstrate that the predicted tracking system accuracies can be met in the flight system. In addition, improvements in the reticle pattern and electronics are possible, which will reduce the complexity of the flight system and increase tracking accuracy.

Psychological factors determining success in a medical career: a 10-year longitudinal study.

PubMed

Tartas, Malgorzata; Walkiewicz, Maciej; Majkowicz, Mikolaj; Budzinski, Waldemar

2011-01-01

Systemic review of predictors of success in medical career is an important tool to recognize the indicators of proper training. To determine psychological factors that predict success in a medical career. The success is defined as professional competence, satisfaction with medicine as a career, occupational stress and burnout and quality of life (QOF). Part I (1999-2005), medical students were examined each subsequent year, beginning with admission. Assessment included academic achievement (high school final examination results, entrance exam results, academic results during medical school) and psychological characteristics (sense of coherence (SOC), depression, anxiety, coping styles, value system and need for social approval). Part II (2008-2009), the same participants completed an Internet survey 4 years after graduation. Results of the postgraduate medical exam were taken under consideration. Academic achievement predicts only professional competence. Coping styles are significant indicators of satisfaction with medicine as a career. SOC, while assessed with anxiety and depression during studies, enabled us to recognize future QOF of medical graduates. Professional stress is not predictable to such an extent as other success indicators. There are significant psychological qualities useful to draw the outline of the future job and life performance of medical graduates.

Auralization of NASA N+2 Aircraft Concepts from System Noise Predictions

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Burley, Casey L.; Thomas, Russel H.

2016-01-01

Auralization of aircraft flyover noise provides an auditory experience that complements integrated metrics obtained from system noise predictions. Recent efforts have focused on auralization methods development, specifically the process by which source noise information obtained from semi-empirical models, computational aeroacoustic analyses, and wind tunnel and flight test data, are used for simulated flyover noise at a receiver on the ground. The primary focus of this work, however, is to develop full vehicle auralizations in order to explore the distinguishing features of NASA's N+2 aircraft vis-à-vis current fleet reference vehicles for single-aisle and large twin-aisle classes. Some features can be seen in metric time histories associated with aircraft noise certification, e.g., tone-corrected perceived noise level used in the calculation of effective perceived noise level. Other features can be observed in sound quality metrics, e.g., loudness, sharpness, roughness, fluctuation strength and tone-to-noise ratio. A psychoacoustic annoyance model is employed to establish the relationship between sound quality metrics and noise certification metrics. Finally, the auralizations will serve as the basis for a separate psychoacoustic study aimed at assessing how well aircraft noise certification metrics predict human annoyance for these advanced vehicle concepts.

Gaining Control and Predictability of Software-Intensive Systems Development and Sustainment

DTIC Science & Technology

2015-02-04

implementation of the baselines, audits , and technical reviews within an overarching systems engineering process (SEP; Defense Acquisition University...warfighters’ needs. This management and metrics effort supplements and supports the system’s technical development through the baselines, audits and...other areas that could be researched and added into the nine-tier model. Areas including software metrics, quality assurance , software-oriented

An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems

USDA-ARS?s Scientific Manuscript database

Interannual variation of forage quantity and quality driven by precipitation events influence beef livestock production systems within the Southern and Northern Plains and Pacific West which combined represents 60% (approximately 17.5 million) of total beef cows in the United States. The beef NRC is...

Redox Conditions in Selected Principal Aquifers of the United States

USGS Publications Warehouse

McMahon, P.B.; Cowdery, T.K.; Chapelle, F.H.; Jurgens, B.C.

2009-01-01

Reduction/oxidation (redox) processes affect the quality of groundwater in all aquifer systems. Redox processes can alternately mobilize or immobilize potentially toxic metals associated with naturally occurring aquifer materials, contribute to the degradation or preservation of anthropogenic contami-nants, and generate undesirable byproducts, such as dissolved manganese (Mn2+), ferrous iron (Fe2+), hydrogen sulfide (H2S), and methane (CH4). Determining the kinds of redox processes that occur in an aquifer system, documenting their spatial distribution, and understanding how they affect concentrations of natural or anthropogenic contaminants are central to assessing and predicting the chemical quality of groundwater. This Fact Sheet extends the analysis of U.S. Geological Survey authors to additional principal aquifer systems by applying a framework developed by the USGS to a larger set of water-quality data from the USGS national water databases. For a detailed explanation, see the 'Introduction' in the Fact Sheet.

Post-audits of Three Groundwater Models for Evaluating Plume Containment

NASA Astrophysics Data System (ADS)

Andersen, P. F.

2003-12-01

Groundwater extraction systems were designed using numerical models at three sites within a U.S. Army Ammunition Plant in Tennessee. Each site, and hence model, has unique qualities such as boundary conditions, extensiveness of the contaminant plume, and quantity and quality of hydrogeologic data. Performance of each of these extraction systems has been evaluated throughout their operation, providing an opportunity to perform post-audits on the accuracy of the groundwater models that were used in their design. Areas of comparison between the models and the observed response in the natural systems include hydraulic head, drawdown, horizontal and vertical gradients, and extent of capture zones. The results of the post-audits show the importance of using all available data in the construction and calibration of the models, the importance of having sufficient data, and the critical nature of an accurate conceptual model. The post-audits also show that although it may be possible to assess the accuracy of the model predictions, it is often not possible to explain the reasons for discrepancies between predicted and observed results. From a practical perspective, parameter uncertainty is important to account for in the development of the models and subsequent design of the extraction systems.

Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

PubMed

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2015-02-01

To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences. Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort. Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions. None. We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models. Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.

Inverse modeling with RZWQM2 to predict water quality

USDA-ARS?s Scientific Manuscript database

Agricultural systems models such as RZWQM2 are complex and have numerous parameters that are unknown and difficult to estimate. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals...

UTILIZATION OF TREATABILITY AND PILOT TESTS TO PREDICT CAH BIOREMEDIATION

EPA Science Inventory

Multiple tools have been suggested to help in the design of enhanced anaerobic bioremediation systems for CAHs:
- Extensive high quality microcosm testing followed by small-scale, thoroughly observed field pilot tests (i.e., RABITT Protocol, Morse 1998)
- More limited ...

Fatty acid profiles and antioxidants of organic and conventional milk from low- and high-input systems during outdoor period.

PubMed

Kusche, Daniel; Kuhnt, Katrin; Ruebesam, Karin; Rohrer, Carsten; Nierop, Andreas F M; Jahreis, Gerhard; Baars, Ton

2015-02-01

Intensification of organic dairy production leads to the question of whether the implementation of intensive feeding incorporating maize silage and concentrates is altering milk quality. Therefore the fatty acid (FA) and antioxidant (AO) profiles of milk on 24 farms divided into four system groups in three replications (n = 71) during the outdoor period were analyzed. In this system comparison, a differentiation of the system groups and the effects of the main system factors 'intensification level' (high-input versus low-input) and 'origin' (organic versus conventional) were evaluated in a multivariate statistical approach. Consistent differentiation of milk from the system groups due to feeding-related impacts was possible in general and on the basis of 15 markers. The prediction of the main system factors was based on four or five markers. The prediction of 'intensification level' was based mainly on CLA c9,t11 and C18:1 t11, whereas that of 'origin' was based on n-3 PUFA. It was possible to demonstrate consistent differences in the FA and AO profiles of organic and standard conventional milk samples. Highest concentrations of nutritionally beneficial compounds were found in the low-input organic system. Adapted grass-based feeding strategies including pasture offer the potential to produce a distinguishable organic milk product quality. © 2014 Society of Chemical Industry.

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2014-11-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS Package for Observation Processing (KPOP) system for data assimilation, preprocessing and quality control modules for bending angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending angle operator and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model-Spectral Element (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS-LETKF data assimilation system, which has been successfully implemented to a cubed-sphere model with fully unstructured quadrilateral meshes. As a result of data processing, the bending angle departure statistics between observation and background shows significant improvement. Also, the first experiment in assimilating GPS-RO bending angle resulting from KPOP within KIAPS-LETKF shows encouraging results.

Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes.

PubMed

Spence, Richard T; Chang, David C; Kaafarani, Haytham M A; Panieri, Eugenio; Anderson, Geoffrey A; Hutter, Matthew M

2018-02-01

Despite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high-income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle-income country (MIC) context. A 5-step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in-hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer-based score that reliably predicts in-hospital death in the 2012 ACS-NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement. Following step one-three above, the 13 point Codman's score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions ≥4 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score ≥3 (3) and operative procedure (1-3). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in-hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths. We have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision-making and (2) benchmarking the quality of surgical care in MIC's.

Effect of time delay on flying qualities: An update

NASA Technical Reports Server (NTRS)

Smith, R. E.; Sarrafian, S. K.

1986-01-01

Flying qualities problems of modern, full-authority electronic flight control systems are most often related to the introduction of additional time delay in aircraft response to a pilot input. These delays can have a significant effect on the flying qualities of the aircraft. Time delay effects are reexamined in light of recent flight test experience with aircraft incorporating new technology. Data from the X-29A forward-swept-wing demonstrator, a related preliminary in-flight experiment, and other flight observations are presented. These data suggest that the present MIL-F-8785C allowable-control system time delay specifications are inadequate or, at least, incomplete. Allowable time delay appears to be a function of the shape of the aircraft response following the initial delay. The cockpit feel system is discussed as a dynamic element in the flight control system. Data presented indicate that the time delay associated with a significant low-frequency feel system does not result in the predicted degradation in aircraft flying qualities. The impact of the feel system is discussed from two viewpoints: as a filter in the control system which can alter the initial response shape and, therefore, the allowable time delay, and as a unique dynamic element whose delay contribution can potentially be discounted by special pilot loop closures.

Rapid determination of sugar level in snack products using infrared spectroscopy.

PubMed

Wang, Ting; Rodriguez-Saona, Luis E

2012-08-01

Real-time spectroscopic methods can provide a valuable window into food manufacturing to permit optimization of production rate, quality and safety. There is a need for cutting edge sensor technology directed at improving efficiency, throughput and reliability of critical processes. The aim of the research was to evaluate the feasibility of infrared systems combined with chemometric analysis to develop rapid methods for determination of sugars in cereal products. Samples were ground and spectra were collected using a mid-infrared (MIR) spectrometer equipped with a triple-bounce ZnSe MIRacle attenuated total reflectance accessory or Fourier transform near infrared (NIR) system equipped with a diffuse reflection-integrating sphere. Sugar contents were determined using a reference HPLC method. Partial least squares regression (PLSR) was used to create cross-validated calibration models. The predictability of the models was evaluated on an independent set of samples and compared with reference techniques. MIR and NIR spectra showed characteristic absorption bands for sugars, and generated excellent PLSR models (sucrose: SEP < 1.7% and r > 0.96). Multivariate models accurately and precisely predicted sugar level in snacks allowing for rapid analysis. This simple technique allows for reliable prediction of quality parameters, and automation enabling food manufacturers for early corrective actions that will ultimately save time and money while establishing a uniform quality. The U.S. snack food industry generates billions of dollars in revenue each year and vibrational spectroscopic methods combined with pattern recognition analysis could permit optimization of production rate, quality, and safety of many food products. This research showed that infrared spectroscopy is a powerful technique for near real-time (approximately 1 min) assessment of sugar content in various cereal products. © 2012 Institute of Food Technologists®

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

A focused ultrasound treatment system for moving targets (part I): generic system design and in-silico first-stage evaluation.

PubMed

Schwenke, Michael; Strehlow, Jan; Demedts, Daniel; Haase, Sabrina; Barrios Romero, Diego; Rothlübbers, Sven; von Dresky, Caroline; Zidowitz, Stephan; Georgii, Joachim; Mihcin, Senay; Bezzi, Mario; Tanner, Christine; Sat, Giora; Levy, Yoav; Jenne, Jürgen; Günther, Matthias; Melzer, Andreas; Preusser, Tobias

2017-01-01

Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging. A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives. With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator. The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice.

Why do Reservoir Computing Networks Predict Chaotic Systems so Well?

NASA Astrophysics Data System (ADS)

Lu, Zhixin; Pathak, Jaideep; Girvan, Michelle; Hunt, Brian; Ott, Edward

Recently a new type of artificial neural network, which is called a reservoir computing network (RCN), has been employed to predict the evolution of chaotic dynamical systems from measured data and without a priori knowledge of the governing equations of the system. The quality of these predictions has been found to be spectacularly good. Here, we present a dynamical-system-based theory for how RCN works. Basically a RCN is thought of as consisting of three parts, a randomly chosen input layer, a randomly chosen recurrent network (the reservoir), and an output layer. The advantage of the RCN framework is that training is done only on the linear output layer, making it computationally feasible for the reservoir dimensionality to be large. In this presentation, we address the underlying dynamical mechanisms of RCN function by employing the concepts of generalized synchronization and conditional Lyapunov exponents. Using this framework, we propose conditions on reservoir dynamics necessary for good prediction performance. By looking at the RCN from this dynamical systems point of view, we gain a deeper understanding of its surprising computational power, as well as insights on how to design a RCN. Supported by Army Research Office Grant Number W911NF1210101.

Perceived Health Competence Predicts Health Behavior and Health-Related Quality of Life in Patients with Cardiovascular Disease

PubMed Central

Bachmann, Justin M.; Goggins, Kathryn M.; Nwosu, Samuel K.; Schildcrout, Jonathan S.; Kripalani, Sunil; Wallston, Kenneth A.

2017-01-01

Objective Evaluate the effect of perceived health competence, a patient’s belief in his or her ability to achieve health-related goals, on health behavior and health-related quality of life. Methods We analyzed 2063 patients hospitalized with acute coronary syndrome and/or congestive heart failure at a large academic hospital in the United States. Multivariable linear regression models investigated associations between the two-item perceived health competence scale (PHCS-2) and positive health behaviors such as medication adherence and exercise (Health Behavior Index) as well as health-related quality of life (5-item Patient Reported Outcome Information Measurement System Global Health Scale). Results After multivariable adjustment, perceived health competence was highly associated with health behaviors (p<0.001) and health-related quality of life (p<0.001). Low perceived health competence was associated with a decrease in health-related quality of life between hospitalization and 90 days after discharge (p<0.001). Conclusions Perceived health competence predicts health behavior and health-related quality of life in patients hospitalized with cardiovascular disease as well as change in health-related quality of life after discharge. Practice implications Patients with low perceived health competence may be at risk for a decline in health-related quality of life after hospitalization and thus a potential target for counseling and other behavioral interventions. PMID:27450479

Perceived health competence predicts health behavior and health-related quality of life in patients with cardiovascular disease.

PubMed

Bachmann, Justin M; Goggins, Kathryn M; Nwosu, Samuel K; Schildcrout, Jonathan S; Kripalani, Sunil; Wallston, Kenneth A

2016-12-01

Evaluate the effect of perceived health competence, a patient's belief in his or her ability to achieve health-related goals, on health behavior and health-related quality of life. We analyzed 2063 patients hospitalized with acute coronary syndrome and/or congestive heart failure at a large academic hospital in the United States. Multivariable linear regression models investigated associations between the two-item perceived health competence scale (PHCS-2) and positive health behaviors such as medication adherence and exercise (Health Behavior Index) as well as health-related quality of life (5-item Patient Reported Outcome Information Measurement System Global Health Scale). After multivariable adjustment, perceived health competence was highly associated with health behaviors (p<0.001) and health-related quality of life (p<0.001). Low perceived health competence was associated with a decrease in health-related quality of life between hospitalization and 90days after discharge (p<0.001). Perceived health competence predicts health behavior and health-related quality of life in patients hospitalized with cardiovascular disease as well as change in health-related quality of life after discharge. Patients with low perceived health competence may be at risk for a decline in health-related quality of life after hospitalization and thus a potential target for counseling and other behavioral interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Water Quality Improvement through Reductions of Pollutant Loads on Small Scale of Bioretention System

NASA Astrophysics Data System (ADS)

Elyza Muha, Norshafa; Mohd Sidek, Lariyah; Jajarmizadeh, Milad

2016-03-01

Bioretention system is introduced as an important topic namely Urban Storm Water Management Manual for Malaysia (MSMA) by the Department of Irrigation and Drainage Malaysia (DID) in May 2012. The main objective of this paper is to evaluate the performance of water quality for small scale bioretention system under tropical climate via MUSIC model. Two bioretention systems 1 and 2 are observed based on the difference media depth. The result of bioretention system is compared with a reference model which has infrastructure with Urban Stormwater Improvement Conceptualisation (MUSIC) for pollutants load reduction and water quality results. Assessment of results via MUSIC software indicates a significant percentage of reduction for Total Suspended Solid (TSS), Total Phosphorus (TP) and Total Nitrogen (TN). The prediction of pollutant reduction via using MUSIC has the harmony for requirement in MSMA. TSS pollutant reduction is more than 80%, while for TP and TN more than 50%. The outcome of this study can be helpful for improvement of the existing MSMA guidelines for application of bioretention systems in Malaysia.

The economic implications of case-mix Medicaid reimbursement for nursing home care.

PubMed

Grabowski, David C

2002-01-01

In recent years, there has been large growth in the nursing home industry in the use of case-mix adjusted Medicaid payment systems that employ resident characteristics to predict the relative use of resources in setting payment levels. Little attention has been paid to the access and quality incentives that these systems provide in the presence of excess demand conditions due to certificate-of-need (CON) and construction moratoria. Using 1991 to 1998 panel data for all certified U.S. nursing homes, a fixed-effects model indicates that adoption of a case-mix payment system led to increased access for more dependent residents, but the effect was modified in excess demand markets. Quality remained relatively stable with the introduction of case-mix reimbursement, regardless of the presence of excess demand conditions. These results suggest that CON and construction moratoria are still important barriers within the nursing home market, and recent quality assurance activities related to the introduction of case-mix payment systems may have been effective.

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure.

PubMed

Garvin, Jennifer H; DuVall, Scott L; South, Brett R; Bray, Bruce E; Bolton, Daniel; Heavirland, Julia; Pickard, Steve; Heidenreich, Paul; Shen, Shuying; Weir, Charlene; Samore, Matthew; Goldstein, Mary K

2012-01-01

Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). An EF value of <40% can be accurately identified in VA echocardiogram reports. An automated information extraction system can be used to accurately extract EF for quality measurement.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

PubMed

Jacobs, Richard H A H; Haak, Koen V; Thumfart, Stefan; Renken, Remco; Henson, Brian; Cornelissen, Frans W

2016-01-01

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2010-09-30

oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or...from Aqua and NASA Tropical Rainfall Measuring Mission (TRMM), 2) developing mesoscale data assimilation techniques to assimilate satellite, radar

Investigating the impact of pain, age, Gross Motor Function Classification System, and sex on health-related quality of life in children with cerebral palsy.

PubMed

Findlay, Briar; Switzer, Lauren; Narayanan, Unni; Chen, Shiyi; Fehlings, Darcy

2016-03-01

To explore whether health-related quality of life (HRQOL) can be predicted by pain, age, Gross Motor Function Classification System (GMFCS) level, and sex in children with cerebral palsy (CP) and whether different pain etiologies have varying effects on HRQOL. Children with CP aged 3 to 19 years and their caregivers were consecutively recruited. Caregivers reported their child's pain (Health Utilities Index 3 [HUI3] pain subset) and HRQOL (DISABKIDS questionnaires). Physicians identified pain etiologies. A multiple linear regression model determined whether pain, GMFCS level, sex, and age predicted HRQOL. An ANOVA evaluated the effects of pain etiologies on HRQOL. Three hundred and forty-four participants were approached and 87% (n=300) participated. Sufficient data were available on 248 (72% of total sample). Sixty-six participants (27%) formed the pain group with HUI3 pain scores of at least 3. The presence of pain and increasing age significantly negatively predicted HRQOL (p<0.001, R(2) =0.141), while GMFCS and sex did not. Musculoskeletal deformity (24%) and hypertonia (18%) were the most frequent pain causes. HRQOL statistically differed depending on the pain etiology (p=0.028) with musculoskeletal deformity showing the lowest mean HRQOL. The presence of pain and increasing age negatively predict HRQOL in CP. musculoskeletal deformity has the greatest negative impact on HRQOL. © 2015 Mac Keith Press.

Profile and predictors of service needs for families of children with autism spectrum disorders

PubMed Central

Zwaigenbaum, Lonnie; Nicholas, David

2015-01-01

Purpose: Increasing demand for autism services is straining service systems. Tailoring services to best meet families’ needs could improve their quality of life and decrease burden on the system. We explored overall, best, and worst met service needs, and predictors of those needs, for families of children with autism spectrum disorders. Methods: Parents of 143 children with autism spectrum disorders (2–18 years) completed a survey including demographic and descriptive information, the Family Needs Survey–Revised, and an open-ended question about service needs. Descriptive statistics characterize the sample and determine the degree to which items were identified and met as needs. Predictors of total and unmet needs were modeled with regression or generalized linear model. Qualitative responses were thematically analyzed. Results: The most frequently identified overall and unmet service needs were information on services, family support, and respite care. The funding and quality of professional support available were viewed positively. Decreased child’s age and income and being an older mother predicted more total needs. Having an older child or mother, lower income, and disruptive behaviors predicted more total unmet needs, yet only disruptive behaviors predicted proportional unmet need. Child’s language or intellectual abilities did not predict needs. Conclusion: Findings can help professionals, funders, and policy-makers tailor services to best meet families’ needs. PMID:25073749

Handling qualities effects of display latency

NASA Technical Reports Server (NTRS)

King, David W.

1993-01-01

Display latency is the time delay between aircraft response and the corresponding response of the cockpit displays. Currently, there is no explicit specification for allowable display lags to ensure acceptable aircraft handling qualities in instrument flight conditions. This paper examines the handling qualities effects of display latency between 70 and 400 milliseconds for precision instrument flight tasks of the V-22 Tiltrotor aircraft. Display delay effects on the pilot control loop are analytically predicted through a second order pilot crossover model of the V-22 lateral axis, and handling qualities trends are evaluated through a series of fixed-base piloted simulation tests. The results show that the effects of display latency for flight path tracking tasks are driven by the stability characteristics of the attitude control loop. The data indicate that the loss of control damping due to latency can be simply predicted from knowledge of the aircraft's stability margins, control system lags, and required control bandwidths. Based on the relationship between attitude control damping and handling qualities ratings, latency design guidelines are presented. In addition, this paper presents a design philosophy, supported by simulation data, for using flight director display augmentation to suppress the effects of display latency for delays up to 300 milliseconds.

[Rancidness of Armeniacae Semen Amarum involving Bianzhuang Lunzhi].

PubMed

Gong, Jian-Ting; Zhao, Li-Ying; Rudolf, Bauer; Mi, Wen-Juan; Li, Yang; Li, Jia-Hui; Ren, Zhi-Yu; Xu, Dong; Zhao, Ting; Yan, Yong-Hong

2016-12-01

This article aims to compare the qualities of Armeniacae Semen Amarum before and after rancidness, in order to study the rancidness of Armeniacae Semen Amarum. In the experiment, content of fatty oil, acid value and peroxide value were determined before and after rancidness,respectively. Meanwhile, HPLC, GC-MS were utilized to analyze laetrile and fatty acid components. Besides, colorimeter and e-nose were introduced to quantify and compare "color and odor". A correlation analysis was conducted on the above results. The results showed that color of post-rancidness Armeniacae Semen Amarum changed from yellow to brown, with sour and lower content of laetrile. On the contrary, acid and peroxide values increased significantly, with changes in fatty acid component. There was a considerable correlation between appearance characteristics and changes in internal quality. The "sensory analysis-quality identification system" can provide a certain scientific basis for prediction of the content of chemical components in traditional Chinese medicine, preliminary judgment of quality of traditional Chinese medicine and real-time quality monitoring, which offers us novel ideas and reference for storage principles of traditional Chinese medicines of "pre-event prediction, during-event intervention and post-event identification". Copyright© by the Chinese Pharmaceutical Association.

Robustness and cognition in stabilization problem of dynamical systems based on asymptotic methods

NASA Astrophysics Data System (ADS)

Dubovik, S. A.; Kabanov, A. A.

2017-01-01

The problem of synthesis of stabilizing systems based on principles of cognitive (logical-dynamic) control for mobile objects used under uncertain conditions is considered. This direction in control theory is based on the principles of guaranteeing robust synthesis focused on worst-case scenarios of the controlled process. The guaranteeing approach is able to provide functioning of the system with the required quality and reliability only at sufficiently low disturbances and in the absence of large deviations from some regular features of the controlled process. The main tool for the analysis of large deviations and prediction of critical states here is the action functional. After the forecast is built, the choice of anti-crisis control is the supervisory control problem that optimizes the control system in a normal mode and prevents escape of the controlled process in critical states. An essential aspect of the approach presented here is the presence of a two-level (logical-dynamic) control: the input data are used not only for generating of synthesized feedback (local robust synthesis) in advance (off-line), but also to make decisions about the current (on-line) quality of stabilization in the global sense. An example of using the presented approach for the problem of development of the ship tilting prediction system is considered.

UTILIZATION OF TREATABILITY AND PILOT TESTS TO PREDICT CAH BIOREMEDIATION (Battelle)

EPA Science Inventory

Multiple tools have been suggested to help in the design of enhanced anaerobic bioremediation systems for CAHs:
Extensive high quality microcosm testing followed by small-scale, thoroughly observed, induced flow field pilot tests (i.e. RABITT Protocol, Morse 1998)
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Software reliability models for fault-tolerant avionics computers and related topics

NASA Technical Reports Server (NTRS)

Miller, Douglas R.

1987-01-01

Software reliability research is briefly described. General research topics are reliability growth models, quality of software reliability prediction, the complete monotonicity property of reliability growth, conceptual modelling of software failure behavior, assurance of ultrahigh reliability, and analysis techniques for fault-tolerant systems.

The role of future scenarios to understand deep uncertainty for air quality management.

EPA Science Inventory

The environment and its interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt systemdynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents a challeng...

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...

A 4.8 kbps code-excited linear predictive coder

NASA Technical Reports Server (NTRS)

Tremain, Thomas E.; Campbell, Joseph P., Jr.; Welch, Vanoy C.

1988-01-01

A secure voice system STU-3 capable of providing end-to-end secure voice communications (1984) was developed. The terminal for the new system will be built around the standard LPC-10 voice processor algorithm. The performance of the present STU-3 processor is considered to be good, its response to nonspeech sounds such as whistles, coughs and impulse-like noises may not be completely acceptable. Speech in noisy environments also causes problems with the LPC-10 voice algorithm. In addition, there is always a demand for something better. It is hoped that LPC-10's 2.4 kbps voice performance will be complemented with a very high quality speech coder operating at a higher data rate. This new coder is one of a number of candidate algorithms being considered for an upgraded version of the STU-3 in late 1989. The problems of designing a code-excited linear predictive (CELP) coder to provide very high quality speech at a 4.8 kbps data rate that can be implemented on today's hardware are considered.

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care.

PubMed

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables-training, self-efficacy, system quality and information quality-on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time.

Persistent Urban Impacts on Surface Water Quality Mediated by Stormwater Recharge

NASA Astrophysics Data System (ADS)

Gabor, R. S.; Brooks, P. D.; Neilson, B. T.; Bowen, G. J.; Jameel, M. Y.; Hall, S. J.; Eiriksson, D.; Millington, M. R.; Gelderloos, A.

2016-12-01

Growing population centers along mountain watersheds put added stress on sensitive hydrologic systems and create water quality impacts downstream. We examined the mountain-to-urban transition in watersheds on Utah's Wasatch Front to identify mechanisms by which urbanization impacts water resources. Rivers in the Wasatch flow from the mountains directly into an urban landscape, where they are subject to channelization, stormwater runoff systems, and urban inputs to water quality from sources such as road salt and fertilizer. As part of an interdisciplinary effort within the iUTAH project, multiple synoptic surveys were performed and a variety of measurements were made, including basic water chemistry along with discharge, water isotopes, and nutrients. Red Butte Creek, a stream in Salt Lake City, does not show significant urban impact to water quality until several kilometers after it enters the city where concentrations of solutes such as chloride and nitrate more than triple in a gaining reach. Groundwater springs discharging to this gaining section demonstrate urban-impacted water chemistry, suggesting that during baseflow a contaminated alluvial aquifer significantly controls stream chemistry. By combining hydrometric and hydrochemical observations we were able to estimate that these groundwater springs were 17-20% urban runoff. We were then able to predict the chemistry of urban runoff that feeds into the alluvial aquifer. Samples collected from storm culverts, roofs, and asphalt during storms had chemistry values within the range of those predicted by the mixing model. This evidence that urbanization affects the water quality of baseflow through impacted groundwater suggests that stormwater mitigation may not be sufficient for protecting urban watersheds, and quantifying these persistent groundwater mediated impacts is necessary to evaluate the success of restoration efforts. By comparing these results from Red Butte Creek with similar studies from other rivers in the Wasatch Front and other alluvial systems, we can quantify how characteristics such as discharge patterns and land-use determine alluvial recharge controls on surface water quality.

Vector Adaptive/Predictive Encoding Of Speech

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey; Gersho, Allen

1989-01-01

Vector adaptive/predictive technique for digital encoding of speech signals yields decoded speech of very good quality after transmission at coding rate of 9.6 kb/s and of reasonably good quality at 4.8 kb/s. Requires 3 to 4 million multiplications and additions per second. Combines advantages of adaptive/predictive coding, and code-excited linear prediction, yielding speech of high quality but requires 600 million multiplications and additions per second at encoding rate of 4.8 kb/s. Vector adaptive/predictive coding technique bridges gaps in performance and complexity between adaptive/predictive coding and code-excited linear prediction.

Data Quality Monitoring in Clinical Trials: Has It Been Worth It? An Evaluation and Prediction of the Future by All Stakeholders

PubMed Central

Kalali, Amir; West, Mark; Walling, David; Hilt, Dana; Engelhardt, Nina; Alphs, Larry; Loebel, Antony; Vanover, Kim; Atkinson, Sarah; Opler, Mark; Sachs, Gary; Nations, Kari; Brady, Chris

2016-01-01

This paper summarizes the results of the CNS Summit Data Quality Monitoring Workgroup analysis of current data quality monitoring techniques used in central nervous system (CNS) clinical trials. Based on audience polls conducted at the CNS Summit 2014, the panel determined that current techniques used to monitor data and quality in clinical trials are broad, uncontrolled, and lack independent verification. The majority of those polled endorse the value of monitoring data. Case examples of current data quality methodology are presented and discussed. Perspectives of pharmaceutical companies and trial sites regarding data quality monitoring are presented. Potential future developments in CNS data quality monitoring are described. Increased utilization of biomarkers as objective outcomes and for patient selection is considered to be the most impactful development in data quality monitoring over the next 10 years. Additional future outcome measures and patient selection approaches are discussed. PMID:27413584

Ground-water models for water resource planning

USGS Publications Warehouse

Moore, J.E.

1983-01-01

In the past decade hydrogeologists have emphasized the development of computer-based mathematical models to aid in the understanding of flow, the transport of solutes, transport of heat, and deformation in the ground-water system. These models have been used to provide information and predictions for water managers. Too frequently, ground-water was neglected in water resource planning because managers believed that it could not be adequately evaluated in terms of availability, quality, and effect of development on surface-water supplies. Now, however, with newly developed digital ground-water models, effects of development can be predicted. Such models have been used to predict hydrologic and quality changes under different stresses. These models have grown in complexity over the last ten years from simple one-layer models to three-dimensional simulations of ground-water flow, which may include solute transport, heat transport, effects of land subsidence, and encroachment of saltwater. Case histories illustrate how predictive ground-water models have provided the information needed for the sound planning and management of water resources in the USA. ?? 1983 D. Reidel Publishing Company.

Does the Quality of SafeTalk Motivational Interviewing Counseling Predict Sexual Behavior Outcomes among People Living with HIV?

PubMed Central

Grodensky, Catherine; Golin, Carol; Parikh, Megha A.; Ochtera, Rebecca; Kincaid, Carlye; Groves, Jennifer; Widman, Laura; Suchindran, Chirayath; McGirt, Camille; Amola, Kemi; Bradley-Bull, Steven

2017-01-01

Objective Although past research has demonstrated a link between the quality of motivational interviewing (MI) counseling and client behavior change, this relationship has not been examined in the context of sexual risk behavior among people living with HIV/AIDS. We studied MI quality and unprotected anal/vaginal intercourse (UAVI) in the context of SafeTalk, an evidence-based secondary HIV prevention intervention. Methods We used a structured instrument (the MISC 2.0 coding system) as well as a client-reported instrument to rate intervention sessions on aspects of MI quality. Then we correlated client-reported UAVI with specific counseling behaviors and the proportion of interactions that achieved MI quality benchmarks. Results/Conclusion Higher MISC-2.0 global ratings and a higher ratio of reflections to questions both significantly predicted fewer UAVI acts at 8-month follow-up. Analysis of client ratings, which was more exploratory, showed that clients who rated their sessions higher in counselor acceptance, client disclosure, and relevance reported higher numbers of UAVIs, whereas clients who selected higher ratings for perceived benefit were more likely to have fewer UAVI episodes. Practice Implications Further research is needed to determine the best methods of translating information about MI quality into dissemination of effective MI interventions with people living with HIV. PMID:27567497

Does the quality of safetalk motivational interviewing counseling predict sexual behavior outcomes among people living with HIV?

PubMed

Grodensky, Catherine; Golin, Carol; Parikh, Megha A; Ochtera, Rebecca; Kincaid, Carlye; Groves, Jennifer; Widman, Laura; Suchindran, Chirayath; McGirt, Camille; Amola, Kemi; Bradley-Bull, Steven

2017-01-01

Although past research has demonstrated a link between the quality of motivational interviewing (MI) counseling and client behavior change, this relationship has not been examined in the context of sexual risk behavior among people living with HIV/AIDS. We studied MI quality and unprotected anal/vaginal intercourse (UAVI) in the context of SafeTalk, an evidence-based secondary HIV prevention intervention. We used a structured instrument (the MISC 2.0 coding system) as well as a client-reported instrument to rate intervention sessions on aspects of MI quality. Then we correlated client-reported UAVI with specific counseling behaviors and the proportion of interactions that achieved MI quality benchmarks. Higher MISC-2.0 global ratings and a higher ratio of reflections to questions both significantly predicted fewer UAVI acts at 8-month follow-up. Analysis of client ratings, which was more exploratory, showed that clients who rated their sessions higher in counselor acceptance, client disclosure, and relevance reported higher numbers of UAVIs, whereas clients who selected higher ratings for perceived benefit were more likely to have fewer UAVI episodes. Further research is needed to determine the best methods of translating information about MI quality into dissemination of effective MI interventions with people living with HIV. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Depression, Control, and Climate: An Examination of Factors Impacting Teaching Quality in Preschool Classrooms.

PubMed

Sandilos, Lia E; Cycyk, Lauren M; Hammer, Carol Scheffner; Sawyer, Brook E; López, Lisa; Blair, Clancy

This study investigated the relationship of preschool teachers' self-reported depressive symptomatology, perception of classroom control, and perception of school climate to classroom quality as measured by the Classroom Assessment Scoring System Pre-K. The sample consisted of 59 urban preschool classrooms serving low-income and linguistically diverse students in the northeastern and southeastern United States. Results of hierarchical linear modeling revealed that teachers' individual reports of depressive symptomatology were significantly and negatively predictive of the observed quality of their instructional support and classroom organization. The findings of this study have implications for increasing access to mental health supports for teachers in an effort to minimize depressive symptoms and potentially improve classroom quality.

Biological indicators for monitoring water quality of MTF canals system

NASA Technical Reports Server (NTRS)

Sethi, S. L.

1975-01-01

Biological models, diversity indexes, were developed to predict environmental effects of NASA's Mississippi test facility (MTF) chemical operations on canal systems in the area. To predict the effects on local streams, a physical model of unpolluted streams was established. The model is fed by artesian well water free of background levels of pollutants. The species diversity and biota composition of unpolluted MTF stream was determined; resulting information will be used to form baseline data for future comparisons. Biological modeling was accomplished by adding controlled quantities or kinds of chemical pollutants and evaluating the effects of these chemicals on the biological life of the stream.

The role of quality measurement in a competitive marketplace.

PubMed

Epstein, A M

1996-01-01

Quality measurement is not a new idea. However, in recent years, several new trends have gained prominence: greater interest in publicly reported information on quality of care, access to care, and patient satisfaction; an increased focus on health plans and integrated systems of care rather than on institutional providers and practitioners as the unit of observation; wide adoption of the techniques of continuous quality improvement within the health care sector; increased use of clinical practice guidelines to improve care for a broad range of medical conditions; incorporation of computer technology into the clinical setting; and greater appreciation for health outcomes as a measure of quality of care. This chapter first reviews the changes in the medical landscape that have seeded these trends and the distinction between quality assurance and quality improvement. It then focuses on public policy concerns, in particular on the emergence of publicly disseminated information about quality of care, now often called "quality report cards." The major prototypes of these reports developed to date, the responses to quality reporting by different members of the delivery system, and the major criticisms of this approach are reviewed. The chapter concludes by predicting probable developments and the strategies most likely to move health care forward in a productive direction.

Nondestructive detection of pork comprehensive quality based on spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Liu, Yuanyuan; Peng, Yankun; Zhang, Leilei; Dhakal, Sagar; Wang, Caiping

2014-05-01

Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork. Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH24. Total of 66 samples were used in the experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model was developed to classify the samples based on their comprehensive qualities. The results showed that the classification model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system developed in this study being simple and easy to use, results being promising, the system can be used in meat processing industry for real time, non-destructive and rapid detection of pork qualities in future.

Towards the Next Generation Air Quality Modeling System ...

EPA Pesticide Factsheets

The community multiscale air quality (CMAQ) model of the U.S. Environmental Protection Agency is one of the most widely used air quality model worldwide; it is employed for both research and regulatory applications at major universities and government agencies for improving understanding of the formation and transport of air pollutants. It is noted, however, that air quality issues and climate change assessments need to be addressed globally recognizing the linkages and interactions between meteorology and atmospheric chemistry across a wide range of scales. Therefore, an effort is currently underway to develop the next generation air quality modeling system (NGAQM) that will be based on a global integrated meteorology and chemistry system. The model for prediction across scales-atmosphere (MPAS-A), a global fully compressible non-hydrostatic model with seamlessly refined centroidal Voronoi grids, has been chosen as the meteorological driver of this modeling system. The initial step of adapting MPAS-A for the NGAQM was to implement and test the physics parameterizations and options that are preferred for retrospective air quality simulations (see the work presented by R. Gilliam, R. Bullock, and J. Herwehe at this workshop). The next step, presented herein, would be to link the chemistry from CMAQ to MPAS-A to build a prototype for the NGAQM. Furthermore, the techniques to harmonize transport processes between CMAQ and MPAS-A, methodologies to connect the chemis

Atmospheric Boundary Layer Wind Data During the Period January 1, 1998 Through January 31, 1999 at the Dallas-Fort Worth Airport. Volume 1; Quality Assessment

NASA Technical Reports Server (NTRS)

Zak, J. Allen; Rodgers, William G., Jr.

2000-01-01

The quality of the Aircraft Vortex Spacing System (AVOSS) is critically dependent on representative wind profiles in the atmospheric boundary layer. These winds observed from a number of sensor systems around the Dallas-Fort Worth airport were combined into single vertical wind profiles by an algorithm developed and implemented by MIT Lincoln Laboratory. This process, called the AVOSS Winds Analysis System (AWAS), is used by AVOSS for wake corridor predictions. During times when AWAS solutions were available, the quality of the resultant wind profiles and variance was judged from a series of plots combining all sensor observations and AWAS profiles during the period 1200 to 0400 UTC daily. First, input data was evaluated for continuity and consistency from criteria established. Next, the degree of agreement among all wind sensor systems was noted and cases of disagreement identified. Finally, the resultant AWAS solution was compared to the quality-assessed input data. When profiles differed by a specified amount from valid sensor consensus winds, times and altitudes were flagged. Volume one documents the process and quality of input sensor data. Volume two documents the data processing/sorting process and provides the resultant flagged files.

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

PubMed

Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

2015-06-12

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

Operational seasonal and interannual predictions of ocean conditions

NASA Technical Reports Server (NTRS)

Leetmaa, Ants

1992-01-01

Dr. Leetmaa described current work at the U.S. National Meteorological Center (NMC) on coupled systems leading to a seasonal prediction system. He described the way in which ocean thermal data is quality controlled and used in a four dimensional data assimilation system. This consists of a statistical interpolation scheme, a primitive equation ocean general circulation model, and the atmospheric fluxes that are required to force this. This whole process generated dynamically consist thermohaline and velocity fields for the ocean. Currently routine weekly analyses are performed for the Atlantic and Pacific oceans. These analyses are used for ocean climate diagnostics and as initial conditions for coupled forecast models. Specific examples of output products were shown both in the Pacific and the Atlantic Ocean.

Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions

NASA Astrophysics Data System (ADS)

Qiao, T.; Ren, J.; Craigie, C.; Zabalza, J.; Maltin, Ch.; Marshall, S.

2015-03-01

It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.

Should Non-Cognitive Skills Be Included in School Accountability Systems? Preliminary Evidence from California's CORE Districts. Evidence Speaks Reports, Vol 1, #13

ERIC Educational Resources Information Center

West, Martin R.

2016-01-01

Evidence confirms that student skills other than academic achievement and ability predict a broad range of academic and life outcomes. This evidence, along with a new federal requirement that state accountability systems include an indicator of school quality or student success not based on test scores, has sparked interest in incorporating such…

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Solidarity-conflict and ambivalence: testing two conceptual frameworks and their impact on quality of life for older family members.

PubMed

Lowenstein, Ariela

2007-03-01

The purpose of this study was to test empirically two major conceptualizations of parent-child relations in later adulthood-intergenerational solidarity-conflict and ambivalence paradigms-and their predictive validity on elders' quality of life using comparative cross-national data. Data were from a sample of 2,064 elders (aged 75 and older) from the five-country OASIS study (Old Age and Autonomy: The Role of Service Systems and Intergenerational Family Solidarity; Norway, England, Germany, Spain, and Israel). Multivariate and block-recursive regression models estimated the predictivity of the two conceptualizations of family dynamics on quality of life controlling for country, personal characteristics, and activity of daily living functioning. Descriptive analyses indicated that family solidarity, especially the affective/cognitive component (called Solidarity A), was high in all five countries, whereas conflict and ambivalence were low. When I entered all three constructs into the regression Solidarity A, reciprocal intergenerational support and ambivalence predicted quality of life. Controlling for activity of daily living functioning, socioeconomics status, and country, intergenerational relations had only a weak explanatory power, and personal resources explained most of the variance. The data suggest that the three constructs exist simultaneously but in varying combinations, confirming that in cross-cultural contexts family cohesion predominates, albeit with low degrees of conflict and ambivalence. The solidarity construct evidenced relatively robust measurement. More work is required to enhance the ambivalence measurement.

The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: a systematic review.

PubMed

Wuytack, Francesca; Meskell, Pauline; Conway, Aislinn; McDaid, Fiona; Santesso, Nancy; Hickey, Fergal G; Gillespie, Paddy; Raymakers, Adam J N; Smith, Valerie; Devane, Declan

2017-12-06

Changes to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs. We searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables. We identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified. Early warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

An analytical study of aircraft lateral-directional handling qualities using pilot models

NASA Technical Reports Server (NTRS)

Adams, J. J.; Moore, F. L.

1976-01-01

A procedure for predicting lateral-directional pilot ratings on the basis of the characteristics of the pilot model and the closed-loop system characteristics is demonstrated. A correlation is shown to exist between experimentally obtained pilot ratings and the computed pilot ratings.

Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

NASA Astrophysics Data System (ADS)

Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

2012-12-01

The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets relevant for fire and smoke management.

A novel air quality analysis and prediction system for São Paulo, Brazil to support decision-making

NASA Astrophysics Data System (ADS)

Hoshyaripour, Gholam Ali; Brasseur, Guy; Andrade, Maria Fatima; Gavidia-Calderón, Mario; Bouarar, Idir

2016-04-01

The extensive economic development and urbanization in southeastern Brazil (SEB) in recent decades have notably degraded the air quality with adverse impacts on human health. Since the Metropolitan Area of São Paulo (MASP) accommodates the majority of the economic growth in SEB, it overwhelmingly suffers from the air pollution. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality and to assist the decision makers to mitigate the air pollution in MASP. Here we present the results of an air quality modeling system designed for SEB with focuses on MASP. The Weather Research and Forecast model with Chemistry (WRF-Chem) is used considering the anthropogenic, biomass-burning and biogenic emissions within a 1000×1500 km domain with resolution of 10 km. FINN and MEGAN are used for the biomass-burning and biogenic emissions, respectively. For the anthropogenic emissions we use a local bottom-up inventory for the transport sector and the HTAPv2 global inventory for all other sectors. The bottom-up inventory accounts for the traffic patterns, vehicle types and their emission factors in the area and thus could be used to evaluate the effect of changes in these parameters on air quality in MASP. The model outputs are compered to the satellite and ground-based observations for O3 and NOx. The results show that using the bottom-up or top-down inventories individually can result in a huge deviation between the predictions and observations. On the other hand, combining the inventories significantly enhances the forecast accuracy. It also provides a powerful tool to quantify the effects of traffic and vehicle emission policies on air quality in MASP.

Implementation of a WRF-CMAQ Air Quality Modeling System in Bogotá, Colombia

NASA Astrophysics Data System (ADS)

Nedbor-Gross, R.; Henderson, B. H.; Pachon, J. E.; Davis, J. R.; Baublitz, C. B.; Rincón, A.

2014-12-01

Due to a continuous economic growth Bogotá, Colombia has experienced air pollution issues in recent years. The local environmental authority has implemented several strategies to curb air pollution that have resulted in the decrease of PM10 concentrations since 2010. However, more activities are necessary in order to meet international air quality standards in the city. The University of Florida Air Quality and Climate group is collaborating with the Universidad de La Salle to prioritize regulatory strategies for Bogotá using air pollution simulations. To simulate pollution, we developed a modeling platform that combines the Weather Research and Forecasting Model (WRF), local emissions, and the Community Multi-scale Air Quality model (CMAQ). This platform is the first of its kind to be implemented in the megacity of Bogota, Colombia. The presentation will discuss development and evaluation of the air quality modeling system, highlight initial results characterizing photochemical conditions in Bogotá, and characterize air pollution under proposed regulatory strategies. The WRF model has been configured and applied to Bogotá, which resides in a tropical climate with complex mountainous topography. Developing the configuration included incorporation of local topography and land-use data, a physics sensitivity analysis, review, and systematic evaluation. The threshold, however, was set based on synthesis of model performance under less mountainous conditions. We will evaluate the impact that differences in autocorrelation contribute to the non-ideal performance. Air pollution predictions are currently under way. CMAQ has been configured with WRF meteorology, global boundary conditions from GEOS-Chem, and a locally produced emission inventory. Preliminary results from simulations show promising performance of CMAQ in Bogota. Anticipated results include a systematic performance evaluation of ozone and PM10, characterization of photochemical sensitivity, and air quality predictions under proposed regulatory scenarios.

Putting density back into the habitat-quality equation: case study of an open-nesting forest bird.

PubMed

Pérot, Aurore; Villard, Marc-André

2009-12-01

Ecological traps and other cases of apparently maladaptive habitat selection cast doubt on the relevance of density as an indicator of habitat quality. Nevertheless, the prevalence of these phenomena remains poorly known, and density may still reflect habitat quality in most systems. We examined the relationship between density and two other parameters of habitat quality in an open-nesting passerine species: the Ovenbird (Seiurus aurocapilla). We hypothesized that the average individual bird makes a good decision when selecting its breeding territory and that territory spacing reflects site productivity or predation risk. Therefore, we predicted that density would be positively correlated with productivity (number of young fledged per unit area). Because individual performance is sensitive to events partly determined by chance, such as nest predation, we further predicted density would be weakly correlated or uncorrelated with the proportion of territories fledging young. We collected data in 23 study sites (25 ha each), 16 of which were located in untreated mature northern hardwood forest and seven in stands partially harvested (treated) 1-7 years prior to the survey. Density explained most of the variability in productivity (R(2)= 0.73), and there was no apparent decoupling between density and productivity in treated plots. In contrast, there was no significant relationship between density and the proportion of territories fledging >or=1 young over the entire breeding season. These results suggest that density reflects habitat quality at the plot scale in this study system. To our knowledge this is one of the few studies testing the value of territory density as an indicator of habitat quality in an open-nesting bird species on the basis of a relatively large number of sizeable study plots.

Using a Data-Driven Approach to Understand the Interaction between Catchment Characteristics and Water Quality Responses

NASA Astrophysics Data System (ADS)

Western, A. W.; Lintern, A.; Liu, S.; Ryu, D.; Webb, J. A.; Leahy, P.; Wilson, P.; Waters, D.; Bende-Michl, U.; Watson, M.

2016-12-01

Many streams, lakes and estuaries are experiencing increasing concentrations and loads of nutrient and sediments. Models that can predict the spatial and temporal variability in water quality of aquatic systems are required to help guide the management and restoration of polluted aquatic systems. We propose that a Bayesian hierarchical modelling framework could be used to predict water quality responses over varying spatial and temporal scales. Stream water quality data and spatial data of catchment characteristics collected throughout Victoria and Queensland (in Australia) over two decades will be used to develop this Bayesian hierarchical model. In this paper, we present the preliminary exploratory data analysis required for the development of the Bayesian hierarchical model. Specifically, we present the results of exploratory data analysis of Total Nitrogen (TN) concentrations in rivers in Victoria (in South-East Australia) to illustrate the catchment characteristics that appear to be influencing spatial variability in (1) mean concentrations of TN; and (2) the relationship between discharge and TN throughout the state. These important catchment characteristics were identified using: (1) monthly TN concentrations measured at 28 water quality gauging stations and (2) climate, land use, topographic and geologic characteristics of the catchments of these 28 sites. Spatial variability in TN concentrations had a positive correlation to fertiliser use in the catchment and average temperature. There were negative correlations between TN concentrations and catchment forest cover, annual runoff, runoff perenniality, soil erosivity and catchment slope. The relationship between discharge and TN concentrations showed spatial variability, possibly resulting from climatic and topographic differences between the sites. The results of this study will feed into the hierarchical Bayesian model of river water quality.

Predicting Mountainous Watershed Biogeochemical Dynamics, Including Response to Droughts and Early Snowmelt

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Williams, K. H.; Long, P.; Agarwal, D.; Banfield, J. F.; Beller, H. R.; Bouskill, N.; Brodie, E.; Maxwell, R. M.; Nico, P. S.; Steefel, C. I.; Steltzer, H.; Tokunaga, T. K.; Wainwright, H. M.

2016-12-01

Climate change, extreme weather, land-use change, and other perturbations are significantly reshaping interactions with in watersheds throughout the world. While mountainous watersheds are recognized as the water towers for the world, hydrological processes in watersheds also mediate biogeochemical processes that support all terrestrial life. Developing predictive understanding of watershed hydrological and biogeochemical functioning is challenging, as complex interactions occurring within a heterogeneous watershed can lead to a cascade of effects on downstream water availability and quality. Although these interactions can have significant implications for energy production, agriculture, water quality, and other benefits valued by society, uncertainty associated with predicting watershed function is high. The Watershed Function project aims to substantially reduce this uncertainty through developing a predictive understanding of how mountainous watersheds retain and release downgradient water, nutrients, carbon, and metals. In particular, the project is exploring how early snowmelt, drought, and other disturbances will influence mountainous watershed dynamics at seasonal to decadal timescales. The Watershed Function project is being carried out in a headwater mountainous catchment of the Upper Colorado River Basin, within a watershed characterized by significant gradients in elevation, vegetation and hydrogeology. A system-within system project perspective posits that the integrated watershed response to disturbances can be adequately predicted through consideration of interactions and feedbacks occurring within a limited number of subsystems, each having distinct vegetation-subsurface biogeochemical-hydrological characteristics. A key technological goal is the development of scale-adaptive simulation capabilities that can incorporate genomic information where and when it is useful for predicting the overall watershed response to disturbance. Through developing and integrating new microbial ecology, geochemical, hydrological, ecohydrological, computational and geophysical approaches, the project is developing new insights about biogeochemical dynamics from genome to watershed scales.

Inverse simulation system for evaluating handling qualities during rendezvous and docking

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Thomson, Douglas; Tang, Guojin; Zhang, Fan

2017-08-01

The traditional method used for handling qualities assessment of manned space vehicles is too time-consuming to meet the requirements of an increasingly fast design process. In this study, a rendezvous and docking inverse simulation system to assess the handling qualities of spacecraft is proposed using a previously developed model-predictive-control architecture. By considering the fixed discrete force of the thrusters of the system, the inverse model is constructed using the least squares estimation method with a hyper-ellipsoidal restriction, the continuous control outputs of which are subsequently dispersed by pulse width modulation with sensitivity factors introduced. The inputs in every step are deemed constant parameters, and the method could be considered as a general method for solving nominal, redundant, and insufficient inverse problems. The rendezvous and docking inverse simulation is applied to a nine-degrees-of-freedom platform, and a novel handling qualities evaluation scheme is established according to the operation precision and astronauts' workload. Finally, different nominal trajectories are scored by the inverse simulation and an established evaluation scheme. The scores can offer theoretical guidance for astronaut training and more complex operation missions.

An integrated weather and sea-state forecasting system for the Arabian Peninsula (WASSF)

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Spyrou, Christos; Mitsakou, Christina; Solomos, Stavros; Bartsotas, Nikolaos; Kalogrei, Christina; Athanaselis, Ioannis; Sofianos, Sarantis; Vervatis, Vassios; Axaopoulos, Panagiotis; Papapostolou, Alexandros; Qahtani, Jumaan Al; Alaa, Elyas; Alexiou, Ioannis; Beard, Daniel

2013-04-01

Nowadays, large industrial conglomerates such as the Saudi ARAMCO, require a series of weather and sea state forecasting products that cannot be found in state meteorological offices or even commercial data providers. The two major objectives of the system is prevention and mitigation of environmental problems and of course early warning of local conditions associated with extreme weather events. The management and operations part is related to early warning of weather and sea-state events that affect operations of various facilities. The environmental part is related to air quality and especially the desert dust levels in the atmosphere. The components of the integrated system include: (i) a weather and desert dust prediction system with forecasting horizon of 5 days, (ii) a wave analysis and prediction component for Red Sea and Arabian Gulf, (iii) an ocean circulation and tidal analysis and prediction of both Red Sea and Arabian Gulf and (iv) an Aviation part specializing in the vertical structure of the atmosphere and extreme events that affect air transport and other operations. Specialized data sets required for on/offshore operations are provided ate regular basis. State of the art modeling components are integrated to a unique system that distributes the produced analysis and forecasts to each department. The weather and dust prediction system is SKIRON/Dust, the wave analysis and prediction system is based on WAM cycle 4 model from ECMWF, the ocean circulation model is MICOM while the tidal analysis and prediction is a development of the Ocean Physics and Modeling Group of University of Athens, incorporating the Tidal Model Driver. A nowcasting subsystem is included. An interactive system based on Google Maps gives the capability to extract and display the necessary information for any location of the Arabian Peninsula, the Red Sea and Arabian Gulf.

The effect of bedding system selected by manual muscle testing on sleep-related cardiovascular functions.

PubMed

Kuo, Terry B J; Li, Jia-Yi; Lai, Chun-Ting; Huang, Yu-Chun; Hsu, Ya-Chuan; Yang, Cheryl C H

2013-01-01

Different types of mattresses affect sleep quality and waking muscle power. Whether manual muscle testing (MMT) predicts the cardiovascular effects of the bedding system was explored using ten healthy young men. For each participant, two bedding systems, one inducing the strongest limb muscle force (strong bedding system) and the other inducing the weakest limb force (weak bedding system), were identified using MMT. Each bedding system, in total five mattresses and eight pillows of different firmness, was used for two continuous weeks at the participant's home in a random and double-blind sequence. A sleep log, a questionnaire, and a polysomnography were used to differentiate the two bedding systems. Heart rate variability and arterial pressure variability analyses showed that the strong bedding system resulted in decreased cardiovascular sympathetic modulation, increased cardiac vagal activity, and increased baroreceptor reflex sensitivity during sleep as compared to the weak bedding system. Different bedding systems have distinct cardiovascular effects during sleep that can be predicted by MMT.

The Effect of Bedding System Selected by Manual Muscle Testing on Sleep-Related Cardiovascular Functions

PubMed Central

Kuo, Terry B. J.; Li, Jia-Yi; Lai, Chun-Ting; Huang, Yu-Chun; Hsu, Ya-Chuan; Yang, Cheryl C. H.

2013-01-01

Background. Different types of mattresses affect sleep quality and waking muscle power. Whether manual muscle testing (MMT) predicts the cardiovascular effects of the bedding system was explored using ten healthy young men. Methods. For each participant, two bedding systems, one inducing the strongest limb muscle force (strong bedding system) and the other inducing the weakest limb force (weak bedding system), were identified using MMT. Each bedding system, in total five mattresses and eight pillows of different firmness, was used for two continuous weeks at the participant's home in a random and double-blind sequence. A sleep log, a questionnaire, and a polysomnography were used to differentiate the two bedding systems. Results and Conclusion. Heart rate variability and arterial pressure variability analyses showed that the strong bedding system resulted in decreased cardiovascular sympathetic modulation, increased cardiac vagal activity, and increased baroreceptor reflex sensitivity during sleep as compared to the weak bedding system. Different bedding systems have distinct cardiovascular effects during sleep that can be predicted by MMT. PMID:24371836

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2011-09-30

assimilating satellite, radar and in-situ observations for improved numerical simulations of major Typhoons (Jiangmi, Sinlaku, Nuri and Hagupit) during T- PARC ...oceans from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or

Models for the indices of thermal comfort

PubMed Central

Adrian, Streinu-Cercel; Sergiu, Costoiu; Maria, Mârza; Anca, Streinu-Cercel; Monica, Mârza

2008-01-01

The current paper propose the analysis and extension formulation required for establishing decision in the management of the medical national system from the point of view of quality and efficiency such as: conceiving models for the indices of thermal comfort, defining the predicted mean vote (on the thermal sensation scale) „PMV”, defining the metabolism „M”, heat transfer between the human body and the environment, defining the predicted percent of dissatisfied people „PPD”, defining all indices of thermal comfort. PMID:20108461

Modeling Benthic Sediment Processes to Predict Water ...

EPA Pesticide Factsheets

The benthic sediment acts as a huge reservoir of particulate and dissolved material (within interstitial water) which can contribute to loading of contaminants and nutrients to the water column. A benthic sediment model is presented in this report to predict spatial and temporal benthic fluxes of nutrients and chemicals in Narragansett Bay. A benthic sediment model is presented in this report to identify benthic flux into the water column in Narragansett Bay. Benthic flux is essential to properly model water quality and ecology in estuarine and coastal systems.

Classifying environmental pollutants: Part 3. External validation of the classification system.

PubMed

Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L

2000-04-01

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Development and analysis of air quality modeling simulations for hazardous air pollutants

NASA Astrophysics Data System (ADS)

Luecken, D. J.; Hutzell, W. T.; Gipson, G. L.

The concentrations of five hazardous air pollutants were simulated using the community multi-scale air quality (CMAQ) modeling system. Annual simulations were performed over the continental United States for the entire year of 2001 to support human exposure estimates. Results are shown for formaldehyde, acetaldehyde, benzene, 1,3-butadiene and acrolein. Photochemical production in the atmosphere is predicted to dominate ambient formaldehyde and acetaldehyde concentrations, and to account for a significant fraction of ambient acrolein concentrations. Spatial and temporal variations are large throughout the domain over the year. Predicted concentrations are compared with observations for formaldehyde, acetaldehyde, benzene and 1,3-butadiene. Although the modeling results indicate an overall slight tendency towards underprediction, they reproduce episodic and seasonal behavior of pollutant concentrations at many monitors with good skill.

Setting the vision: applied patient-reported outcomes and smart, connected digital healthcare systems to improve patient-centered outcomes prediction in critical illness.

PubMed

Wysham, Nicholas G; Abernethy, Amy P; Cox, Christopher E

2014-10-01

Prediction models in critical illness are generally limited to short-term mortality and uncommonly include patient-centered outcomes. Current outcome prediction tools are also insensitive to individual context or evolution in healthcare practice, potentially limiting their value over time. Improved prognostication of patient-centered outcomes in critical illness could enhance decision-making quality in the ICU. Patient-reported outcomes have emerged as precise methodological measures of patient-centered variables and have been successfully employed using diverse platforms and technologies, enhancing the value of research in critical illness survivorship and in direct patient care. The learning health system is an emerging ideal characterized by integration of multiple data sources into a smart and interconnected health information technology infrastructure with the goal of rapidly optimizing patient care. We propose a vision of a smart, interconnected learning health system with integrated electronic patient-reported outcomes to optimize patient-centered care, including critical care outcome prediction. A learning health system infrastructure integrating electronic patient-reported outcomes may aid in the management of critical illness-associated conditions and yield tools to improve prognostication of patient-centered outcomes in critical illness.

A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.

PubMed

Chen, Lei; Lu, Jing; Zhang, Ning; Huang, Tao; Cai, Yu-Dong

2014-04-01

In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.

Stress and Negative Relationship Quality among Older Couples: Implications for Blood Pressure

PubMed Central

Newton, Nicky J.; Cranford, James A.; Ryan, Lindsay H.

2016-01-01

Objectives: The cardiovascular system may represent a significant pathway by which marriage and stress influence health, but research has focused on married individuals cross-sectionally. This study examined associations among chronic stress, negative spousal relationship quality, and systolic blood pressure over time among middle-aged and older husbands and wives. Method: Participants were from the nationally representative longitudinal Health and Retirement Study. A total of 1,356 (N = 2,712) married and cohabitating couples completed psychosocial and biomeasure assessments in waves 2006 and 2010. Analyses examined whether Wave 1 (2006) relationship quality and stress were associated with changes in blood pressure over time. Results: The effects of stress and negative relationship quality were dyadic and varied by gender. Husbands had increased blood pressure when wives reported greater stress, and this link was exacerbated by negative spousal relationship quality. Negative relationship quality predicted increased blood pressure when both members of the couple reported negative quality relations. Discussion: Findings support the dyadic biopsychosocial model of marriage and health indicating: (a) stress and relationship quality directly effect the cardiovascular system, (b) relationship quality moderates the effect of stress, and (c) the dyad rather than only the individual should be considered when examining marriage and health. PMID:25852106

Simulating the Response of Urban Water Quality to Climate and Land Use Change in Partially Urbanized Basins

NASA Astrophysics Data System (ADS)

Sun, N.; Yearsley, J. R.; Nijssen, B.; Lettenmaier, D. P.

2014-12-01

Urban stream quality is particularly susceptible to extreme precipitation events and land use change. Although the projected effects of extreme events and land use change on hydrology have been resonably well studied, the impacts on urban water quality have not been widely examined due in part to the scale mismatch between global climate models and the spatial scales required to represent urban hydrology and water quality signals. Here we describe a grid-based modeling system that integrates the Distributed Hydrology Soil Vegetation Model (DHSVM) and urban water quality module adpated from EPA's Storm Water Management Model (SWMM) and Soil and water assessment tool (SWAT). Using the model system, we evaluate, for four partially urbanized catchments within the Puget Sound basin, urban water quality under current climate conditions, and projected potential changes in urban water quality associated with future changes in climate and land use. We examine in particular total suspended solids, toal nitrogen, total phosphorous, and coliform bacteria, with catchment representations at the 150-meter spatial resolution and the sub-daily timestep. We report long-term streamflow and water quality predictions in response to extreme precipitation events of varying magnitudes in the four partially urbanized catchments. Our simulations show that urban water quality is highly sensitive to both climatic and land use change.

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.

Highway runoff quality models for the protection of environmentally sensitive areas

NASA Astrophysics Data System (ADS)

Trenouth, William R.; Gharabaghi, Bahram

2016-11-01

This paper presents novel highway runoff quality models using artificial neural networks (ANN) which take into account site-specific highway traffic and seasonal storm event meteorological factors to predict the event mean concentration (EMC) statistics and mean daily unit area load (MDUAL) statistics of common highway pollutants for the design of roadside ditch treatment systems (RDTS) to protect sensitive receiving environs. A dataset of 940 monitored highway runoff events from fourteen sites located in five countries (Canada, USA, Australia, New Zealand, and China) was compiled and used to develop ANN models for the prediction of highway runoff suspended solids (TSS) seasonal EMC statistical distribution parameters, as well as the MDUAL statistics for four different heavy metal species (Cu, Zn, Cr and Pb). TSS EMCs are needed to estimate the minimum required removal efficiency of the RDTS needed in order to improve highway runoff quality to meet applicable standards and MDUALs are needed to calculate the minimum required capacity of the RDTS to ensure performance longevity.

Predicting risk for medical malpractice claims using quality-of-care characteristics.

PubMed Central

Charles, S C; Gibbons, R D; Frisch, P R; Pyskoty, C E; Hedeker, D; Singha, N K

1992-01-01

The current fault-based tort system assumes that claims made against physicians are inversely related to the quality of care they provide. In this study we identified physician characteristics associated with elements of medical care that make physicians vulnerable to malpractice claims. A sample of physicians (n = 248) thought to be at high or low risk for claims was surveyed on various personal and professional characteristics. Statistical analysis showed that 9 characteristics predicted risk group. High risk was associated with increased age, surgical specialty, emergency department coverage, increased days away from practice, and the feeling that the litigation climate was "unfair." Low risk was associated with scheduling enough time to talk with patients, answering patients' telephone calls directly, feeling "satisfied" with practice arrangements, and acknowledging greater emotional distress. Prediction was more accurate for physicians in practice 15 years or less. We conclude that a relationship exists between a history of malpractice claims and selected physician characteristics. PMID:1462538

Incorporating metacognition into morbidity and mortality rounds: The next frontier in quality improvement.

PubMed

Katz, David; Detsky, Allan S

2016-02-01

This Perspective proposes the introduction of metacognition (thinking about thinking) into the existing format of hospital-based morbidity and mortality rounds. It is placed in the context of historical movements to advance quality improvement by expanding the spectrum of the causes of medical error from systems-based issues to flawed human decision-making capabilities. We suggest that the current approach that focuses on systems-based issues can be improved by exploiting the opportunities to educate physicians about predictable errors committed by reliance on cognitive heuristics. In addition, because the field of educating clinicians about cognitive heuristics has shown mixed results, this proposal represents fertile ground for further research. Educating clinicians about cognitive heuristics may improve metacognition and perhaps be the next frontier in quality improvement. © 2015 Society of Hospital Medicine.

A decision support system for delivering optimal quality peach and tomato

NASA Technical Reports Server (NTRS)

Thai, C. N.; Pease, J. N.; Shewfelt, R. L.

1990-01-01

Several studies have indicated that color and firmness are the two quality attributes most important to consumers in making purchasing decisions of fresh peaches and tomatoes. However, at present, retail produce managers do not have the proper information for handling fresh produce so it has the most appealing color and firmness when it reaches the consumer. This information should help them predict the consumer color and firmness perception and preference for produce from various storage conditions. Since 1987, for 'Redglobe' peach and 'Sunny' tomato, we have been generating information about their physical quality attributes (firmness and color) and their corresponding consumer sensory scores. This article reports on our current progress toward the goal of integrating such information into a model-based decision support system for retail level managers in handling fresh peaches and tomatoes.

Prediction Interval Development for Wind-Tunnel Balance Check-Loading

NASA Technical Reports Server (NTRS)

Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

2014-01-01

Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.

Differences in aquatic habitat quality as an impact of one- and two-dimensional hydrodynamic model simulated flow variables

NASA Astrophysics Data System (ADS)

Benjankar, R. M.; Sohrabi, M.; Tonina, D.; McKean, J. A.

2013-12-01

Aquatic habitat models utilize flow variables which may be predicted with one-dimensional (1D) or two-dimensional (2D) hydrodynamic models to simulate aquatic habitat quality. Studies focusing on the effects of hydrodynamic model dimensionality on predicted aquatic habitat quality are limited. Here we present the analysis of the impact of flow variables predicted with 1D and 2D hydrodynamic models on simulated spatial distribution of habitat quality and Weighted Usable Area (WUA) for fall-spawning Chinook salmon. Our study focuses on three river systems located in central Idaho (USA), which are a straight and pool-riffle reach (South Fork Boise River), small pool-riffle sinuous streams in a large meadow (Bear Valley Creek) and a steep-confined plane-bed stream with occasional deep forced pools (Deadwood River). We consider low and high flows in simple and complex morphologic reaches. Results show that 1D and 2D modeling approaches have effects on both the spatial distribution of the habitat and WUA for both discharge scenarios, but we did not find noticeable differences between complex and simple reaches. In general, the differences in WUA were small, but depended on stream type. Nevertheless, spatially distributed habitat quality difference is considerable in all streams. The steep-confined plane bed stream had larger differences between aquatic habitat quality defined with 1D and 2D flow models compared to results for streams with well defined macro-topographies, such as pool-riffle bed forms. KEY WORDS: one- and two-dimensional hydrodynamic models, habitat modeling, weighted usable area (WUA), hydraulic habitat suitability, high and low discharges, simple and complex reaches

Design, testing and validation of an innovative web-based instrument to evaluate school meal quality.

PubMed

Patterson, Emma; Quetel, Anna-Karin; Lilja, Karin; Simma, Marit; Olsson, Linnea; Elinder, Liselotte Schäfer

2013-06-01

To develop a feasible, valid, reliable web-based instrument to objectively evaluate school meal quality in Swedish primary schools. The construct 'school meal quality' was operationalized by an expert panel into six domains, one of which was nutritional quality. An instrument was drafted and pilot-tested. Face validity was evaluated by the panel. Feasibility was established via a large national study. Food-based criteria to predict the nutritional adequacy of school meals in terms of fat quality, iron, vitamin D and fibre content were developed. Predictive validity was evaluated by comparing the nutritional adequacy of school menus based on these criteria with the results from a nutritional analysis. Inter-rater reliability was also assessed. The instrument was developed between 2010 and 2012. It is designed for use in all primary schools by school catering and/or management representatives. A pilot-test of eighty schools in Stockholm (autumn 2010) and a further test of feasibility in 191 schools nationally (spring 2011). The four nutrient-specific food-based criteria predicted nutritional adequacy with sensitivity ranging from 0.85 to 1.0, specificity from 0.45 to 1.0 and accuracy from 0.67 to 1.0. The sample in the national study was statistically representative and the majority of users rated the questionnaire positively, suggesting the instrument is feasible. The inter-rater reliability was fair to almost perfect for continuous variables and agreement was ≥ 67 % for categorical variables. An innovative web-based system to comprehensively monitor school meal quality across several domains, with validated questions in the nutritional domain, is available in Sweden for the first time.

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2015-03-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS package for observation processing (KPOP) system for data assimilation, preprocessing, and quality control modules for bending-angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. The GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending-angle operator, and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS local ensemble transform Kalman filter (LETKF) data assimilation system, which has been successfully implemented to a cubed-sphere model with unstructured quadrilateral meshes. As a result of data processing, the bending-angle departure statistics between observation and background show significant improvement. Also, the first experiment in assimilating GPS-RO bending angle from KPOP within KIAPS-LETKF shows encouraging results.

Models to predict length of stay in the Intensive Care Unit after coronary artery bypass grafting: a systematic review.

PubMed

Atashi, Alireza; Verburg, Ilona W; Karim, Hesam; Miri, Mirmohammad; Abu-Hanna, Ameen; de Jonge, Evert; de Keizer, Nicolette F; Eslami, Saeid

2018-06-01

Intensive Care Units (ICU) length of stay (LoS) prediction models are used to compare different institutions and surgeons on their performance, and is useful as an efficiency indicator for quality control. There is little consensus about which prediction methods are most suitable to predict (ICU) length of stay. The aim of this study is to systematically review models for predicting ICU LoS after coronary artery bypass grafting and to assess the reporting and methodological quality of these models to apply them for benchmarking. A general search was conducted in Medline and Embase up to 31-12-2016. Three authors classified the papers for inclusion by reading their title, abstract and full text. All original papers describing development and/or validation of a prediction model for LoS in the ICU after CABG surgery were included. We used a checklist developed for critical appraisal and data extraction for systematic reviews of prediction modeling and extended it on handling specific patients subgroups. We also defined other items and scores to assess the methodological and reporting quality of the models. Of 5181 uniquely identified articles, fifteen studies were included of which twelve on development of new models and three on validation of existing models. All studies used linear or logistic regression as method for model development, and reported various performance measures based on the difference between predicted and observed ICU LoS. Most used a prospective (46.6%) or retrospective study design (40%). We found heterogeneity in patient inclusion/exclusion criteria; sample size; reported accuracy rates; and methods of candidate predictor selection. Most (60%) studies have not mentioned the handling of missing values and none compared the model outcome measure of survivors with non-survivors. For model development and validation studies respectively, the maximum reporting (methodological) scores were 66/78 and 62/62 (14/22 and 12/22). There are relatively few models for predicting ICU length of stay after CABG. Several aspects of methodological and reporting quality of studies in this field should be improved. There is a need for standardizing outcome and risk factor definitions in order to develop/validate a multi-institutional and international risk scoring system.

Updating The Navy’s Recruit Quality Matrix: An Analysis of Educational Credentials and the Success of First-Term Sailors

DTIC Science & Technology

2004-03-01

Allison , Logistic Regression: Using the SAS System (Cary, NC: SAS Institute, Inc, 2001), 57. 23 using the likelihood ratio that SAS generates...21, respectively. 33 Jesse M. Rothstein, College Performance Predictions and the SAT ( Berkely , CA: UC

Comparison of NIR and FT-IR spectral models in the prediction of cotton fiber strength

USDA-ARS?s Scientific Manuscript database

Strength quality in cotton fibers is one of several important end-use characteristics. In routine programs, it has been mostly assessed by automation-oriented high volume instrument (HVI) system. An alternative method for cotton strength is near infrared (NIR) spectroscopy. Although previous NIR mod...

Evaluation of SWAT for estimating ET in irrigated and dryland cropping systems in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

Hydrologic models such as SWAT are used extensively for predicting water availability and water quality responses to alternative management practices. Modeling results have been used by regulatory agencies for developing remedial measures for impaired water bodies and for water planning purposes. Ho...

Predicting Contextual Informativeness for Vocabulary Learning

ERIC Educational Resources Information Center

Kapelner, Adam; Soterwood, Jeanine; Nessaiver, Shalev; Adlof, Suzanne

2018-01-01

Vocabulary knowledge is essential to educational progress. High quality vocabulary instruction requires supportive contextual examples to teach word meaning and proper usage. Identifying such contexts by hand for a large number of words can be difficult. In this work, we take a statistical learning approach to engineer a system that predicts…

Deep Learning for Space Weather Prediction

NASA Astrophysics Data System (ADS)

Pauly, M.; Shah, Y.; Cheung, C. M. M.

2016-12-01

Through the use of our current fleet of in-orbit solar observatories, we have accumulated a vast amount of high quality solar event data which has greatly helped us to understand the underlying mechanisms of how the Sun works. However, we still lack an accurate and robust system for autonomously predicting solar eruptive events, which are known to cause geomagnetic storms, disturbances in electrical grids, radio black outs, increased drag on satellites, and increased radiation exposure to astronauts. We address the need for a flare prediction system by developing deep neural networks (DNNs) trained with solar data taken by the Helioseismic & Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) instruments onboard the Solar Dynamics Observatory and X-ray flux data taken by the GOES satellites. We describe the architecture of the DNNs trained and compare the performance between different implementations.

Space and ground segment performance of the FORMOSAT-3/COSMIC mission: four years in orbit

NASA Astrophysics Data System (ADS)

Fong, C.-J.; Whiteley, D.; Yang, E.; Cook, K.; Chu, V.; Schreiner, B.; Ector, D.; Wilczynski, P.; Liu, T.-Y.; Yen, N.

2011-01-01

The FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) mission consisting of six Low-Earth-Orbit (LEO) satellites is the world's first demonstration constellation using radio occultation signals from Global Positioning System (GPS) satellites. The radio occultation signals are retrieved in near real-time for global weather/climate monitoring, numerical weather prediction, and space weather research. The mission has processed on average 1400 to 1800 high-quality atmospheric sounding profiles per day. The atmospheric radio occultation soundings data are assimilated into operational numerical weather prediction models for global weather prediction, including typhoon/hurricane/cyclone forecasts. The radio occultation data has shown a positive impact on weather predictions at many national weather forecast centers. A proposed follow-on mission transitions the program from the current experimental research system to a significantly improved real-time operational system, which will reliably provide 8000 radio occultation soundings per day. The follow-on mission as planned will consist of 12 satellites with a data latency of 45 min, which will provide greatly enhanced opportunities for operational forecasts and scientific research. This paper will address the FORMOSAT-3/COSMIC system and mission overview, the spacecraft and ground system performance after four years in orbit, the lessons learned from the encountered technical challenges and observations, and the expected design improvements for the new spacecraft and ground system.

Nondestructive detection of pork quality based on dual-band VIS/NIR spectroscopy

NASA Astrophysics Data System (ADS)

Wang, Wenxiu; Peng, Yankun; Li, Yongyu; Tang, Xiuying; Liu, Yuanyuan

2015-05-01

With the continuous development of living standards and the relative change of dietary structure, consumers' rising and persistent demand for better quality of meat is emphasized. Colour, pH value, and cooking loss are important quality attributes when evaluating meat. To realize nondestructive detection of multi-parameter of meat quality simultaneously is popular in production and processing of meat and meat products. The objectives of this research were to compare the effectiveness of two bands for rapid nondestructive and simultaneous detection of pork quality attributes. Reflectance spectra of 60 chilled pork samples were collected from a dual-band visible/near-infrared spectroscopy system which covered 350-1100 nm and 1000-2600 nm. Then colour, pH value and cooking loss were determined by standard methods as reference values. Standard normal variables transform (SNVT) was employed to eliminate the spectral noise. A spectrum connection method was put forward for effective integration of the dual-band spectrum to make full use of the whole efficient information. Partial least squares regression (PLSR) and Principal component analysis (PCA) were applied to establish prediction models using based on single-band spectrum and dual-band spectrum, respectively. The experimental results showed that the PLSR model based on dual-band spectral information was superior to the models based on single band spectral information with lower root means quare error (RMSE) and higher accuracy. The PLSR model based on dual-band (use the overlapping part of first band) yielded the best prediction result with correlation coefficient of validation (Rv) of 0.9469, 0.9495, 0.9180, 0.9054 and 0.8789 for L*, a*, b*, pH value and cooking loss, respectively. This mainly because dual-band spectrum can provide sufficient and comprehensive information which reflected the quality attributes. Data fusion from dual-band spectrum could significantly improve pork quality parameters prediction performance. The research also indicated that multi-band spectral information fusion has potential to comprehensively evaluate other quality and safety attributes of pork.

Sibling Influences on Gender Development in Middle Childhood and Early Adolescence: A Longitudinal Study.

ERIC Educational Resources Information Center

McHale, Susan M.; Updegraff, Kimberly A.; Helms- Erikson, Heather; Crouter, Ann C.

2001-01-01

Examined development of gender role qualities from middle childhood to early adolescence to determine whether children's gender role qualities predicted siblings'. Found that firstborn children's qualities in Year 1 predicted second-born children's qualities in Year 3 when Year 1 parent and child qualities were controlled. Parental influence was…

The Interaction of Spacecraft Cabin Atmospheric Quality and Water Processing System Performance

NASA Technical Reports Server (NTRS)

Perry, Jay L.; Croomes, Scott D. (Technical Monitor)

2002-01-01

Although designed to remove organic contaminants from a variety of waste water streams, the planned U.S.- and present Russian-provided water processing systems onboard the International Space Station (ISS) have capacity limits for some of the more common volatile cleaning solvents used for housekeeping purposes. Using large quantities of volatile cleaning solvents during the ground processing and in-flight operational phases of a crewed spacecraft such as the ISS can lead to significant challenges to the water processing systems. To understand the challenges facing the management of water processing capacity, the relationship between cabin atmospheric quality and humidity condensate loading is presented. This relationship is developed as a tool to determine the cabin atmospheric loading that may compromise water processing system performance. A comparison of cabin atmospheric loading with volatile cleaning solvents from ISS, Mir, and Shuttle are presented to predict acceptable limits to maintain optimal water processing system performance.

Estimated power quality for line commutated photovoltaic residential system

NASA Astrophysics Data System (ADS)

McNeill, B. W.; Mirza, M. A.

1983-10-01

A residential photovoltaic system using a line commutated inverter is modeled using a single diode model for the solar cells and a four switch model for the inverter. The model predicts power factor and total harmonic distortion as a function of solar radiation, array voltage, inverter output voltage, and inverter filter capacitor and inductor size. The model was run using parameter values appropriate for the John F. Long PV System and the predicted results compared well with measured results from the system. The model shows that improvements in total harmonic distortion are made at the expense of the power factor. The harmonic distortion is least when the inverter is operating at just continuous conduction. The total harmonic distortion can be kept to less than 0.17 all day if a variable inductor is used in the inverter's input filters.

Bringing simulation to engineers in the field: a Web 2.0 approach.

PubMed

Haines, Robert; Khan, Kashif; Brooke, John

2009-07-13

Field engineers working on water distribution systems have to implement day-to-day operational decisions. Since pipe networks are highly interconnected, the effects of such decisions are correlated with hydraulic and water quality conditions elsewhere in the network. This makes the provision of predictive decision support tools (DSTs) for field engineers critical to optimizing the engineering work on the network. We describe how we created DSTs to run on lightweight mobile devices by using the Web 2.0 technique known as Software as a Service. We designed our system following the architectural style of representational state transfer. The system not only displays static geographical information system data for pipe networks, but also dynamic information and prediction of network state, by invoking and displaying the results of simulations running on more powerful remote resources.

Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft and satellite measurements from the CalNex2010 campaign

NASA Astrophysics Data System (ADS)

Bray, Casey D.; Battye, William; Aneja, Viney P.; Tong, Daniel; Lee, Pius; Tang, Youhua; Nowak, John B.

2017-08-01

Atmospheric ammonia (NH3) is not only a major precursor gas for fine particulate matter (PM2.5), but it also negatively impacts the environment through eutrophication and acidification. As the need for agriculture, the largest contributing source of NH3, increases, NH3 emissions will also increase. Therefore, it is crucial to accurately predict ammonia concentrations. The objective of this study is to determine how well the U.S. National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC) system predicts ammonia concentrations using their Community Multiscale Air Quality (CMAQ) model (v4.6). Model predictions of atmospheric ammonia are compared against measurements taken during the NOAA California Nexus (CalNex) field campaign that took place between May and July of 2010. Additionally, the model predictions were also compared against ammonia measurements obtained from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. The results of this study showed that the CMAQ model tended to under predict concentrations of NH3. When comparing the CMAQ model with the CalNex measurements, the model under predicted NH3 by a factor of 2.4 (NMB = -58%). However, the ratio of the median measured NH3 concentration to the median of the modeled NH3 concentration was 0.8. When compared with the TES measurements, the model under predicted concentrations of NH3 by a factor of 4.5 (NMB = -77%), with a ratio of the median retrieved NH3 concentration to the median of the modeled NH3 concentration of 3.1. Because the model was the least accurate over agricultural regions, it is likely that the major source of error lies within the agricultural emissions in the National Emissions Inventory. In addition to this, the lack of the use of bidirectional exchange of NH3 in the model could also contribute to the observed bias.

Effect of transient liquid flow on retention characteristics of screen acquisition systems. [design of Space Shuttle feed system

NASA Technical Reports Server (NTRS)

Cady, E. C.

1977-01-01

A design analysis, is developed based on experimental data, to predict the effects of transient flow and pressure surges (caused either by valve or pump operation, or by boiling of liquids in warm lines) on the retention performance of screen acquisition systems. A survey of screen liquid acquisition system applications was performed to determine appropriate system environment and classification. A screen model was developed which assumed that the screen device was a uniformly distributed composite orthotropic structure, and which accounted for liquid inflow/outflow, gas ingestion quality, screen stress, and liquid spill. A series of 177 tests using 13 specimens (5 screen meshes, 4 screen device construction/backup methods, and 2 orientations) with three test fluids (isopropyl alcohol, Freon 114, and LH2) provided data which verified important features of the screen model and resulted in a design tool which could accurately predict the transient startup performance acquisition devices.

NASA's Earth Science Research and Environmental Predictions

NASA Technical Reports Server (NTRS)

Hilsenrath, E.

2004-01-01

NASA Earth Science program began in the 1960s with cloud imaging satellites used for weather observations. A fleet of satellites are now in orbit to investigate the Earth Science System to uncover the connections between land, Oceans and the atmosphere. Satellite systems using an array of active and passive remote sensors are used to search for answers on how is the Earth changing and what are the consequences for life on Earth? The answer to these questions can be used for applications to serve societal needs and contribute to decision support systems for weather, hazard, and air quality predictions and mitigation of adverse effects. Partnerships with operational agencies using NASA's observational capabilities are now being explored. The system of the future will require new technology, data assimilation systems which includes data and models that will be used for forecasts that respond to user needs.

Our changing planet: The FY 1993 US global change research program. A supplement to the US President's fiscal year 1993 budget

NASA Technical Reports Server (NTRS)

1992-01-01

An improved predictive understanding of the integrated Earth system, including human interactions, will provide direct benefits by anticipating and planning for possible impacts on commerce, agriculture, energy, resource utilization, human safety, and environmental quality. The central goal of the U.S. Global Change Research Program (USGCRP) is to help establish the scientific understanding and the basis for national and international policymaking related to natural and human-induced changes in the global Earth system. This will be accomplished through: (1) establishing an integrated, comprehensive, long-term program of documenting the Earth system on a global scale; (2) conducting a program of focused studies to improve our understanding of the physical, geological, chemical, biological, and social processes that influence the Earth system processes; and (3) developing integrated conceptual and predictive Earth system models.

Rapid monitoring of grape withering using visible near-infrared spectroscopy.

PubMed

Beghi, Roberto; Giovenzana, Valentina; Marai, Simone; Guidetti, Riccardo

2015-12-01

Wineries need new practical and quick instruments, non-destructive and able to quantitatively evaluate during withering the parameters that impact product quality. The aim of the work was to test an optical portable system (visible near-infrared (NIR) spectrophotometer) in a wavelength range of 400-1000 nm for the prediction of quality parameters of grape berries during withering. A total of 300 red grape samples (Vitis vinifera L., Corvina cultivar) harvested in vintage year 2012 from the Valpolicella area (Verona, Italy) were analyzed. Qualitative (principal component analysis, PCA) and quantitative (partial least squares regression algorithm, PLS) evaluations were performed on grape spectra. PCA showed a clear sample grouping for the different withering stages. PLS models gave encouraging predictive capabilities for soluble solids content (R(2) val  = 0.62 and ratio performance deviation, RPD = 1.87) and firmness (R(2) val  = 0.56 and RPD = 1.79). The work demonstrated the applicability of visible NIR spectroscopy as a rapid technique for the analysis of grape quality directly in barns, during withering. The sector could be provided with simple and inexpensive optical systems that could be used to monitor the withering degree of grape for better management of the wine production process. © 2014 Society of Chemical Industry.

Validating a model that predicts daily growth and feed quality of New Zealand dairy pastures.

PubMed

Woodward, S J

2001-09-01

The Pasture Quality (PQ) model is a simple, mechanistic, dynamical system model that was designed to capture the essential biological processes in grazed grass-clover pasture, and to be optimised to derive improved grazing strategies for New Zealand dairy farms. While the individual processes represented in the model (photosynthesis, tissue growth, flowering, leaf death, decomposition, worms) were based on experimental data, this did not guarantee that the assembled model would accurately predict the behaviour of the system as a whole (i.e., pasture growth and quality). Validation of the whole model was thus a priority, since any strategy derived from the model could impact a farm business in the order of thousands of dollars per annum if adopted. This paper describes the process of defining performance criteria for the model, obtaining suitable data to test the model, and carrying out the validation analysis. The validation process highlighted a number of weaknesses in the model, which will lead to the model being improved. As a result, the model's utility will be enhanced. Furthermore, validation was found to have an unexpected additional benefit, in that despite the model's poor initial performance, support was generated for the model among field scientists involved in the wider project.

Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing ratio, air temperature and overhead column ozone) used for the calculation of the linearized photochemical production and loss of ozone. Furthermore the radiative budget in the tropopause region is strongly affected by water vapor cooling, which impact requires further evaluation for the use in chemically coupled operational NWP systems.

Modeling and Mechanisms of Intercontinental Transport of Biomass-Burning Plumes

NASA Astrophysics Data System (ADS)

Reid, J. S.; Westphal, D. L.; Christopher, S. A.; Prins, E. M.; Justice, C. O.; Richardson, K. A.; Reid, E. A.; Eck, T. F.

2003-12-01

With the aid of fire products from GOES and MODIS, the NRL Aerosol Analysis and Prediction System (NAAPS) successfully monitors and predicts the formation and transport of massive smoke plumes between the continents in near real time. The goal of this system, formed under the joint Navy, NASA, and NOAA sponsored Fire Locating and Modeling of Burning Emissions (FLAMBE) project, is to provide 5 day forecasts of large biomass burning plumes and evaluate impacts on air quality, visibility, and regional radiative balance. In this paper we discuss and compare the mechanisms of intercontinental transport from the three most important sources in the world prone to long range advection: Africa, South/Central America, and Siberia. We demonstrate how these regions impact neighboring continents. As the meteorology of these three regions are distinct, differences in transport phenomenon subsequently result, particularly with respect to vertical distribution. Specific examples will be given on prediction and the impact of Siberian and Central American smoke plumes on the United States as well as transport phenomena from Africa to Australia. We present rules of thumb for radiation and air quality impacts. We also model clear sky bias (both positive and negative) with respect to MODIS data, and show the frequency to which frontal advection of smoke plumes masks remote sensing retrievals of smoke optical depth.

Application of laser to nondestructive detection of fruit quality

NASA Astrophysics Data System (ADS)

Li, Jing; Xue, Long; Liu, Muhua; Li, Zhanlong; Yang, Yong

2008-12-01

In this study, a hyperspectral imaging system using a laser source was developed and two experiments were carried out. The first experiment was detection of pesticide residue on navel orange surface. We calculated the mean intensity of regions of interest to plot the curves between 629nm to 638nm. The analysis of the mean intensity curves showed that the mean intensity can be described by a characteristic Gaussian curve equation. The coefficients a in characteristic equations of 0%, 0.1% and 0.5% fenvalerate residue images were more than 2400, 1570-2400 and less than 1570, respectively. So we suggest using equation coefficient a to detect pesticide residue on navel orange surface. The second experiment was predicting firmness, sugar content and vitamin C content of kiwi fruit. The optimal wavelength range of the kiwi fruit firmness, sugar content, vitamin C content line regressing prediction model were 680-711nm, 674-708nm, 669-701nm. The correlation coefficients (R) of prediction models for firmness, sugar content and vitamin C content were 0.898, 0.932 and 0.918. The mean errors of validation results were 0.35×105Pa, 0.32%Brix and 7mg/100g. The experimental results indicate that a hyperspectral imaging system based on a laser source can detect fruit quality effectively.

Assessment of the climate change impacts on fecal coliform contamination in a tidal estuarine system.

PubMed

Liu, Wen-Cheng; Chan, Wen-Ting

2015-12-01

Climate change is one of the key factors affecting the future microbiological water quality in rivers and tidal estuaries. A coupled 3D hydrodynamic and fecal coliform transport model was developed and applied to the Danshuei River estuarine system for predicting the influences of climate change on microbiological water quality. The hydrodynamic and fecal coliform model was validated using observational salinity and fecal coliform distributions. According to the analyses of the statistical error, predictions of the salinity and the fecal coliform concentration from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict the fecal coliform contamination as a result of climate change, including the change of freshwater discharge and the sea level rise. We found that the reduction of freshwater discharge under climate change scenarios resulted in an increase in the fecal coliform concentration. The sea level rise would decrease fecal coliform distributions because both the water level and the water volume increased. A reduction in freshwater discharge has a negative impact on the fecal coliform concentration, whereas a rising sea level has a positive influence on the fecal coliform contamination. An appropriate strategy for the effective microbiological management in tidal estuaries is required to reveal the persistent trends of climate in the future.

Vi. Marital conflict, vagal regulation, and children's sleep: a longitudinal investigation.

PubMed

El-Sheikh, Mona; Hinnant, J Benjamin; Erath, Stephen A

2015-03-01

We examined longitudinal relations between adult interpartner conflict (referred to as marital conflict) and children's subsequent sleep minutes and quality assessed objectively via actigraphy, and tested parasympathetic nervous system (PNS) activity indexed through respiratory sinus arrhythmia reactivity (RSA-R) and initial sleep as moderators of predictive associations. At Wave 1 (W1), children (85 boys, 75 girls) with a mean age of 9.43 years (SD=.69) reported on marital conflict, and their sleep was assessed with actigraphs for seven nights. Sleep minutes, sleep efficiency, sleep activity, and number of long wake episodes were derived. RSA-R was measured in response to a lab challenge. Sleep parameters were assessed again 1 year later at Wave 2 (W2; mean age=10.39; SD=.64). Analyses consistently revealed 3-way interactions among W1 marital conflict, sleep, and RSA-R as predictors of W2 sleep parameters. Sleep was stable among children with more sleep minutes and better sleep quality at W1 or low exposure to marital conflict at W1. Illustrating conditional risk, marital conflict predicted increased sleep problems (reduced sleep minutes, worse sleep quality) at W2 among children with poorer sleep at W1 in conjunction with less apt physiological regulation (i.e., lower levels of RSA-R or less vagal withdrawal) at W1. Findings build on the scant literature and underscore the importance of simultaneous consideration of bioregulatory systems (PNS and initial sleep in this study) in conjunction with family processes in the prediction of children's later sleep parameters. © 2015 The Society for Research in Child Development, Inc.

Evaluation of automated global mapping of Reference Soil Groups of WRB2015

NASA Astrophysics Data System (ADS)

Mantel, Stephan; Caspari, Thomas; Kempen, Bas; Schad, Peter; Eberhardt, Einar; Ruiperez Gonzalez, Maria

2017-04-01

SoilGrids is an automated system that provides global predictions for standard numeric soil properties at seven standard depths down to 200 cm, currently at spatial resolutions of 1km and 250m. In addition, the system provides predictions of depth to bedrock and distribution of soil classes based on WRB and USDA Soil Taxonomy (ST). In SoilGrids250m(1), soil classes (WRB, version 2006) consist of the RSG and the first prefix qualifier, whereas in SoilGrids1km(2), the soil class was assessed at RSG level. Automated mapping of World Reference Base (WRB) Reference Soil Groups (RSGs) at a global level has great advantages. Maps can be updated in a short time span with relatively little effort when new data become available. To translate soil names of older versions of FAO/WRB and national classification systems of the source data into names according to WRB 2006, correlation tables are used in SoilGrids. Soil properties and classes are predicted independently from each other. This means that the combinations of soil properties for the same cells or soil property-soil class combinations do not necessarily yield logical combinations when the map layers are studied jointly. The model prediction procedure is robust and probably has a low source of error in the prediction of RSGs. It seems that the quality of the original soil classification in the data and the use of correlation tables are the largest sources of error in mapping the RSG distribution patterns. Predicted patterns of dominant RSGs were evaluated in selected areas and sources of error were identified. Suggestions are made for improvement of WRB2015 RSG distribution predictions in SoilGrids. Keywords: Automated global mapping; World Reference Base for Soil Resources; Data evaluation; Data quality assurance References 1 Hengl T, de Jesus JM, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2016) SoilGrids250m: global gridded soil information based on Machine Learning. Earth System Science Data (ESSD), in review. 2 Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992. doi:10.1371/journal.pone.0105992

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

PubMed Central

DuVall, Scott L; South, Brett R; Bray, Bruce E; Bolton, Daniel; Heavirland, Julia; Pickard, Steve; Heidenreich, Paul; Shen, Shuying; Weir, Charlene; Samore, Matthew; Goldstein, Mary K

2012-01-01

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement. PMID:22437073

The variation in the eating quality of beef from different sexes and breed classes cannot be completely explained by carcass measurements.

PubMed

Bonny, S P F; Hocquette, J-F; Pethick, D W; Farmer, L J; Legrand, I; Wierzbicki, J; Allen, P; Polkinghorne, R J; Gardner, G E

2016-06-01

Delivering beef of consistent quality to the consumer is vital for consumer satisfaction and will help to ensure demand and therefore profitability within the beef industry. In Australia, this is being tackled with Meat Standards Australia (MSA), which uses carcass traits and processing factors to deliver an individual eating quality guarantee to the consumer for 135 different 'cut by cooking methods' from each carcass. The carcass traits used in the MSA model, such as ossification score, carcass weight and marbling explain the majority of the differences between breeds and sexes. Therefore, it was expected that the model would predict with eating quality of bulls and dairy breeds with good accuracy. In total, 8128 muscle samples from 482 carcasses from France, Poland, Ireland and Northern Ireland were MSA graded at slaughter then evaluated for tenderness, juiciness, flavour liking and overall liking by untrained consumers, according to MSA protocols. The scores were weighted (0.3, 0.1, 0.3, 0.3) and combined to form a global eating quality (meat quality (MQ4)) score. The carcasses were grouped into one of the three breed categories: beef breeds, dairy breeds and crosses. The difference between the actual and the MSA-predicted MQ4 scores were analysed using a linear mixed effects model including fixed effects for carcass hang method, cook type, muscle type, sex, country, breed category and postmortem ageing period, and random terms for animal identification, consumer country and kill group. Bulls had lower MQ4 scores than steers and females and were predicted less accurately by the MSA model. Beef breeds had lower eating quality scores than dairy breeds and crosses for five out of the 16 muscles tested. Beef breeds were also over predicted in comparison with the cross and dairy breeds for six out of the 16 muscles tested. Therefore, even after accounting for differences in carcass traits, bulls still differ in eating quality when compared with females and steers. Breed also influenced eating quality beyond differences in carcass traits. However, in this case, it was only for certain muscles. This should be taken into account when estimating the eating quality of meat. In addition, the coefficients used by the Australian MSA model for some muscles, marbling score and ultimate pH do not exactly reflect the influence of these factors on eating quality in this data set, and if this system was to be applied to Europe then the coefficients for these muscles and covariates would need further investigation.

Predictors of facial attractiveness and health in humans.

PubMed

Foo, Yong Zhi; Simmons, Leigh W; Rhodes, Gillian

2017-02-03

Facial attractiveness has been suggested to provide signals of biological quality, particularly health, in humans. The attractive traits that have been implicated as signals of biological quality include sexual dimorphism, symmetry, averageness, adiposity, and carotenoid-based skin colour. In this study, we first provide a comprehensive examination of the traits that predict attractiveness. In men, attractiveness was predicted positively by masculinity, symmetry, averageness, and negatively by adiposity. In women, attractiveness was predicted positively by femininity and negatively by adiposity. Skin colour did not predict attractiveness in either sex, suggesting that, despite recent interest in the literature, colour may play limited role in determining attractiveness. Male perceived health was predicted positively by averageness, symmetry, and skin yellowness, and negatively by adiposity. Female perceived health was predicted by femininity. We then examined whether appearance predicted actual health using measures that have been theoretically linked to sexual selection, including immune function, oxidative stress, and semen quality. In women, there was little evidence that female appearance predicted health. In men, we found support for the phenotype-linked fertility hypothesis that male masculinity signalled semen quality. However, we also found a negative relationship between averageness and semen quality. Overall, these results indicate weak links between attractive facial traits and health.

Predictors of facial attractiveness and health in humans

PubMed Central

Foo, Yong Zhi; Simmons, Leigh W.; Rhodes, Gillian

2017-01-01

Facial attractiveness has been suggested to provide signals of biological quality, particularly health, in humans. The attractive traits that have been implicated as signals of biological quality include sexual dimorphism, symmetry, averageness, adiposity, and carotenoid-based skin colour. In this study, we first provide a comprehensive examination of the traits that predict attractiveness. In men, attractiveness was predicted positively by masculinity, symmetry, averageness, and negatively by adiposity. In women, attractiveness was predicted positively by femininity and negatively by adiposity. Skin colour did not predict attractiveness in either sex, suggesting that, despite recent interest in the literature, colour may play limited role in determining attractiveness. Male perceived health was predicted positively by averageness, symmetry, and skin yellowness, and negatively by adiposity. Female perceived health was predicted by femininity. We then examined whether appearance predicted actual health using measures that have been theoretically linked to sexual selection, including immune function, oxidative stress, and semen quality. In women, there was little evidence that female appearance predicted health. In men, we found support for the phenotype-linked fertility hypothesis that male masculinity signalled semen quality. However, we also found a negative relationship between averageness and semen quality. Overall, these results indicate weak links between attractive facial traits and health. PMID:28155897

Monte Carlo modeling of spatial coherence: free-space diffraction

PubMed Central

Fischer, David G.; Prahl, Scott A.; Duncan, Donald D.

2008-01-01

We present a Monte Carlo method for propagating partially coherent fields through complex deterministic optical systems. A Gaussian copula is used to synthesize a random source with an arbitrary spatial coherence function. Physical optics and Monte Carlo predictions of the first- and second-order statistics of the field are shown for coherent and partially coherent sources for free-space propagation, imaging using a binary Fresnel zone plate, and propagation through a limiting aperture. Excellent agreement between the physical optics and Monte Carlo predictions is demonstrated in all cases. Convergence criteria are presented for judging the quality of the Monte Carlo predictions. PMID:18830335

A manufacturing quality assessment model based-on two stages interval type-2 fuzzy logic

NASA Astrophysics Data System (ADS)

Purnomo, Muhammad Ridwan Andi; Helmi Shintya Dewi, Intan

2016-01-01

This paper presents the development of an assessment models for manufacturing quality using Interval Type-2 Fuzzy Logic (IT2-FL). The proposed model is developed based on one of building block in sustainable supply chain management (SSCM), which is benefit of SCM, and focuses more on quality. The proposed model can be used to predict the quality level of production chain in a company. The quality of production will affect to the quality of product. Practically, quality of production is unique for every type of production system. Hence, experts opinion will play major role in developing the assessment model. The model will become more complicated when the data contains ambiguity and uncertainty. In this study, IT2-FL is used to model the ambiguity and uncertainty. A case study taken from a company in Yogyakarta shows that the proposed manufacturing quality assessment model can work well in determining the quality level of production.

Depression, Control, and Climate: An Examination of Factors Impacting Teaching Quality in Preschool Classrooms

PubMed Central

Sandilos, Lia E.; Cycyk, Lauren M.; Hammer, Carol Scheffner; Sawyer, Brook E.; López, Lisa; Blair, Clancy

2015-01-01

Research Findings This study investigated the relationship of preschool teachers' self-reported depressive symptomatology, perception of classroom control, and perception of school climate to classroom quality as measured by the Classroom Assessment Scoring System Pre-K. The sample consisted of 59 urban preschool classrooms serving low-income and linguistically diverse students in the northeastern and southeastern United States. Results of hierarchical linear modeling revealed that teachers' individual reports of depressive symptomatology were significantly and negatively predictive of the observed quality of their instructional support and classroom organization. Practice or Policy The findings of this study have implications for increasing access to mental health supports for teachers in an effort to minimize depressive symptoms and potentially improve classroom quality. PMID:26924914

Nutritional limitations to increased production on pasture-based systems.

PubMed

Kolver, Eric S

2003-05-01

The constraints to high levels of milk production imposed by a high-quality-pasture diet, and development of feeding strategies to overcome these limitations, were examined by modelling the nutritional status of New Zealand Friesian and North American Holstein-Friesian dairy cows grazing high-quality pasture. The Cornell Net Carbohydrate and Protein System (CNCPS) was used to predict sensitivity of milk production to a 10% change in the composition of pasture nutrients. The rate at which fibre and protein were degraded in the rumen and the value given to effective fibre and lignin content significantly affected the supply of metabolisable energy and protein, and the profile of amino acid supply. The first limiting factor in milk production when only high-quality pasture was fed was metabolisable energy supply, while specific amino acids, particularly methionine and lysine, limited milk production when > 20 g/kg diet consisted of a grain supplement. Compared with cows fed a total mixed ration in confinement, North American Holstein-Friesians grazing all pasture produced less milk (29.6 v. 44.1 kg/d). Of the difference in milk production 61% could be attributed to a lower DM intake (19 kg/d v. 23.4 kg/d). Predictions using the CNCPS indicated that supply of metabolisable energy was the first-limiting factor for milk production from high-quality pasture (251 g crude protein (N x 6.25)/kg, 432 g neutral-detergent fibre/kg, 77% in vitro DM digestibility), rather than metabolisable protein or amino acids. In addition, these nutritional limitations imposed by pasture diets will be greater for dairy cow genotypes that have not been selected for high performance within a pasture system.

Operational air quality forecast guidance for the United States

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jinaping; Djalalova, Irina; Wilczak, James; Huang, Ho-Chun; Wang, Jun; Stein, Ariel; Upadhayay, Sikchya

2016-04-01

NOAA provides operational air quality predictions for ozone and wildfire smoke over the United States (U.S.) and predictions of airborne dust over the contiguous 48 states at http://airquality.weather.gov. These predictions are produced using U.S. Environmental Protection Agency (EPA) Community Model for Air Quality (CMAQ) and NOAA's HYSPLIT model (Stein et al., 2015) with meteorological inputs from the North American Mesoscale Forecast System (NAM). The current efforts focus on improving test predictions of fine particulate matter (PM2.5) from CMAQ. Emission inputs for ozone and PM2.5 predictions include inventory information from the U.S. EPA and recently added contributions of particulate matter from intermittent wildfires and windblown dust that rely on near real-time information. Current testing includes refinement of the vertical grid structure in CMAQ and inclusion of contributions of dust transport from global sources into the U.S. domain using the NEMS Global Aerosol Capability (NGAC). The addition of wildfire smoke and dust contributions in CMAQ reduced model underestimation of PM2.5 in summertime. Wintertime overestimation of PM2.5 was reduced by suppressing emissions of soil particles when the terrain is covered by snow or ice. Nevertheless, seasonal biases and biases in the diurnal cycle of PM2.5 are still substantial. Therefore, a new bias correction procedure based on an analog ensemble approach was introduced (Djalalova et al., 2015). It virtually eliminates biases in monthly means or in the diurnal cycle, but it also reduces day-to-day variability in PM2.5 predictions. Refinements to the bias correction procedure are being developed. Upgrades for the representation of wildfire smoke emissions within the domain and from global sources are in testing. Another area of active development includes approaches to scale emission inventories for nitrogen oxides in order to reproduce recent changes observed by the AirNow surface monitoring network and by satellite instruments (Tong et al., 2015) and to use these updated emissions to improve ozone predictions (Pan et al., 2015). An overview of the impacts of these recent and ongoing efforts to improve predictions of ozone, smoke and PM2.5 will be presented. Djalalova, I. et al., 2015: PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.05.057. Pan L. et al., 2014: Assessment of NOx and O3 forecasting performances in the U.S. National Air Quality Forecasting Capability before and after the 2012 major emissions updates. Atmospheric Environment, doi: 10.1016/j.atmosenv.2014.06.020. Stein, A. et al., 2015: NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-14-00110.1. Tong, D.Q. et al., 2015: Long-term NOx trends over large cities in the United States during the great recession: Comparison of satellite retrievals, ground observations, and emission inventories. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.01.035.

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

Advancing Drug Safety Through Prospective Pharmacovigilance.

PubMed

Pitts, Peter J; Le Louet, Hervé

2018-01-01

Much has changed in a relatively short period of time. There is a raging debate over the level of evidence expected to first introduce a treatment to patients based on smaller, more adaptive data sets. Some argue for less data followed by postapproval follow-up, others for more adaptive clinical trial designs and end-point modification driven by patient-focused drug development and use of real-world evidence. The transition in both the review and postmarketing regulatory framework is happening in front of our eyes in real time. To improve the ability of patients to receive high-quality, safe, effective, and timely care, better information via pharmacovigilance must be a priority as the world's many regulatory systems build the capacity to harness electronic health information to improve health, care quality, and safety. Globally, the widely variable ability of nations to build reliable regulatory systems (from precise review to robust pharmacovigilance) is a dangerous source of health care inequality. Developing validated tools and techniques for "predictive pharmacovigilance" will assist all health systems in better understanding the risks and benefits of the medicines they regulate by understanding what should be happening once a new medicine moves from risk-benefit regulatory efficacy to real-world risk-effectiveness. This will be of particular utility for smaller regulatory agencies with fewer resources. By comparing preapproval predictive pharmacovigilance data, developing regulatory authorities will be able to better understand the potential gap between what was predicted and what was actually measured (via more traditional pharmacovigilance methodologies). Predictive pharmacovigilance recognizes the value of understanding the imperfect reporting of real-world clinical use and that the absence of reporting is, in itself, an important postmarketing signal.

Analysis and modeling of atmospheric turbulence on the high-resolution space optical systems

NASA Astrophysics Data System (ADS)

Lili, Jiang; Chen, Xiaomei; Ni, Guoqiang

2016-09-01

Modeling and simulation of optical remote sensing system plays an unslightable role in remote sensing mission predictions, imaging system design, image quality assessment. It has already become a hot research topic at home and abroad. Atmospheric turbulence influence on optical systems is attached more and more importance to as technologies of remote sensing are developed. In order to study the influence of atmospheric turbulence on earth observation system, the atmospheric structure parameter was calculated by using the weak atmospheric turbulence model; and the relationship of the atmospheric coherence length and high resolution remote sensing optical system was established; then the influence of atmospheric turbulence on the coefficient r0h of optical remote sensing system of ground resolution was derived; finally different orbit height of high resolution optical system imaging quality affected by atmospheric turbulence was analyzed. Results show that the influence of atmospheric turbulence on the high resolution remote sensing optical system, the resolution of which has reached sub meter level meter or even the 0.5m, 0.35m and even 0.15m ultra in recent years, image quality will be quite serious. In the above situation, the influence of the atmospheric turbulence must be corrected. Simulation algorithms of PSF are presented based on the above results. Experiment and analytical results are posted.

Human action quality evaluation based on fuzzy logic with application in underground coal mining.

PubMed

Ionica, Andreea; Leba, Monica

2015-01-01

The work system is defined by its components, their roles and the relationships between them. Any work system gravitates around the human resource and the interdependencies between human factor and the other components of it. Researches in this field agreed that the human factor and its actions are difficult to quantify and predict. The objective of this paper is to apply a method of human actions evaluation in order to estimate possible risks and prevent possible system faults, both at human factor level and at equipment level. In order to point out the importance of the human factor influence on all the elements of the working systems we propose a fuzzy logic based methodology for quality evaluation of human actions. This methodology has a multidisciplinary character, as it gathers ideas and methods from: quality management, ergonomics, work safety and artificial intelligence. The results presented refer to a work system with a high degree of specificity, namely, underground coal mining and are valuable for human resources risk evaluation pattern. The fuzzy logic evaluation of the human actions leads to early detection of possible dangerous evolutions of the work system and alarm the persons in charge.

Mitigating Provider Uncertainty in Service Provision Contracts

NASA Astrophysics Data System (ADS)

Smith, Chris; van Moorsel, Aad

Uncertainty is an inherent property of open, distributed and multiparty systems. The viability of the mutually beneficial relationships which motivate these systems relies on rational decision-making by each constituent party under uncertainty. Service provision in distributed systems is one such relationship. Uncertainty is experienced by the service provider in his ability to deliver a service with selected quality level guarantees due to inherent non-determinism, such as load fluctuations and hardware failures. Statistical estimators utilized to model this non-determinism introduce additional uncertainty through sampling error. Inability of the provider to accurately model and analyze uncertainty in the quality level guarantees can result in the formation of sub-optimal service provision contracts. Emblematic consequences include loss of revenue, inefficient resource utilization and erosion of reputation and consumer trust. We propose a utility model for contract-based service provision to provide a systematic approach to optimal service provision contract formation under uncertainty. Performance prediction methods to enable the derivation of statistical estimators for quality level are introduced, with analysis of their resultant accuracy and cost.

Assessing Aircraft Susceptibility to Nonlinear Aircraft-Pilot Coupling/Pilot-Induced Oscillations

NASA Technical Reports Server (NTRS)

Hess, R.A.; Stout, P. W.

1997-01-01

A unified approach for assessing aircraft susceptibility to aircraft-pilot coupling (or pilot-induced oscillations) which was previously reported in the literature and applied to linear systems is extended to nonlinear systems, with emphasis upon vehicles with actuator rate saturation. The linear methodology provided a tool for predicting: (1) handling qualities levels, (2) pilot-induced oscillation rating levels and (3) a frequency range in which pilot-induced oscillations are likely to occur. The extension to nonlinear systems provides a methodology for predicting the latter two quantities. Eight examples are presented to illustrate the use of the technique. The dearth of experimental flight-test data involving systematic variation and assessment of the effects of actuator rate limits presently prevents a more thorough evaluation of the methodology.

The Properties of Convective Clouds Over the Western Pacific and Their Relationship to the Environment of Tropical Cyclones

DTIC Science & Technology

2009-09-30

from radar , aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite... radar , lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., Navy Operational Global Atmospheric Prediction System or...ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 satellite, radar , lidar and in-situ data

Mechanisms of deterioration of nutrients. [retention of flavor during freeze drying

NASA Technical Reports Server (NTRS)

Karel, M.; Flink, J. M.

1975-01-01

The retention of flavor during freeze drying was studied with model systems. Mechanisms by which flavor retention phenomena is explained were developed and process conditions specified so that flavor retention is optimized. The literature is reviewed and results of studies of the flavor retention behavior of a number of real food products, including both liquid and solid foods are evaluated. Process parameters predicted by the mechanisms to be of greatest significance are freezing rate, initial solids content, and conditions which result in maintenance of sample structure. Flavor quality for the real food showed the same behavior relative to process conditions as predicted by the mechanisms based on model system studies.

State of the art metrics for aspect oriented programming

NASA Astrophysics Data System (ADS)

Ghareb, Mazen Ismaeel; Allen, Gary

2018-04-01

The quality evaluation of software, e.g., defect measurement, gains significance with higher use of software applications. Metric measurements are considered as the primary indicator of imperfection prediction and software maintenance in various empirical studies of software products. However, there is no agreement on which metrics are compelling quality indicators for novel development approaches such as Aspect Oriented Programming (AOP). AOP intends to enhance programming quality, by providing new and novel constructs for the development of systems, for example, point cuts, advice and inter-type relationships. Hence, it is not evident if quality pointers for AOP can be derived from direct expansions of traditional OO measurements. Then again, investigations of AOP do regularly depend on established coupling measurements. Notwithstanding the late reception of AOP in empirical studies, coupling measurements have been adopted as useful markers of flaw inclination in this context. In this paper we will investigate the state of the art metrics for measurement of Aspect Oriented systems development.

Time course and predictors of health-related quality of life improvement and medication satisfaction in children diagnosed with attention-deficit/hyperactivity disorder treated with the methylphenidate transdermal system.

PubMed

Frazier, Thomas W; Weiss, Margaret; Hodgkins, Paul; Manos, Michael J; Landgraf, Jeanne M; Gibbins, Christopher

2010-10-01

The aim of this study was to evaluate the time course and predictors of improvement in health-related quality of life (HRQL) and medication satisfaction in children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and treated with the methylphenidate transdermal system (MTS). Temporal relationships between ADHD symptoms, medication satisfaction, and HRQL measures were examined via latent growth curve, structural path, and growth mixture models. Higher levels of medication satisfaction at the end of titration predicted greater increases in family HRQL (p=0.004) and, to a lesser extent, child HRQL (p=0.068) throughout the study. At 4 of 6 (p<0.05) and 5 of 6 (p<0.10) contemporaneous time points, ADHD symptoms predicted child HRQL. At 2 of 6 (p<0.05) and 3 of 6 (p<0.10) contemporaneous time points, ADHD symptoms predicted family HRQL. ADHD did not predict child or family HRQL improvements at subsequent time points. A uniform pattern of change for child HRQL was noted, with most HRQL change following the pattern of symptom change during titration. Three distinct patterns of change were noted for family HRQL. In most cases, medication satisfaction, ADHD symptoms, and HRQL improved simultaneously, suggesting that HRQL was not a delayed response to improvement in symptoms. Children showed a uniform pattern of improvement in HRQL that followed symptom change; three distinct patterns of change were found for improvement in family HRQL.

Improving the Quality of Alerts and Predicting Intruder's Next Goal with Hidden Colored Petri-Net

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

Yu, Dong; Frincke, Deb A.

2006-06-22

Intrusion detection systems (IDS) often provide poor quality alerts, which are insufficient to support rapid identification of ongoing attacks or predict an intruder’s next likely goal. In this paper, we propose a novel approach to alert post-processing and correlation, the Hidden Colored Petri-Net (HCPN). Different from most other alert correlation methods, our approach treats the alert correlation problem as an inference problem rather than a filter problem. Our approach assumes that the intruder’s actions are unknown to the IDS and can be inferred only from the alerts generated by the IDS sensors. HCPN can describe the relationship between different stepsmore » carried out by intruders, model observations (alerts) and transitions (actions) separately, and associate each token element (system state) with a probability (or confidence). The model is an extension to Colored Petri-Net (CPN) .It is so called “hidden” because the transitions (actions) are not directly observable but can be inferred by looking through the observations (alerts). These features make HCPN especially suitable for discovering intruders’ actions from their partial observations (alerts,) and predicting intruders’ next goal. Our experiments on DARPA evaluation datasets and the attack scenarios from the Grand Challenge Problem (GCP) show that HCPN has promise as a way to reducing false positives and negatives, predicting intruder’s next possible action, uncovering intruders’ intrusion strategies after the attack scenario has happened, and providing confidence scores.« less

Social Anxiety and Friendship Quality over Time.

PubMed

Rodebaugh, Thomas L; Lim, Michelle H; Shumaker, Erik A; Levinson, Cheri A; Thompson, Tess

2015-01-01

High social anxiety in adults is associated with self-report of impaired friendship quality, but not necessarily with impairment reported by friends. Further, prospective prediction of social anxiety and friendship quality over time has not been tested among adults. We therefore examined friendship quality and social anxiety prospectively in 126 young adults (67 primary participants and 59 friends, aged 17-22 years); the primary participants were screened to be extreme groups to increase power and relevance to clinical samples (i.e., they were recruited based on having very high or very low social interaction anxiety). The prospective relationships between friendship quality and social anxiety were then tested using an Actor-Partner Interdependence Model. Friendship quality prospectively predicted social anxiety over time within each individual in the friendship, such that higher friendship quality at Time 1 predicted lower social anxiety approximately 6 months later at Time 2. Social anxiety did not predict friendship quality. Although the results support the view that social anxiety and friendship quality have an important causal relationship, the results run counter to the assumption that high social anxiety causes poor friendship quality. Interventions to increase friendship quality merit further consideration.

Extreme weather events: Should drinking water quality management systems adapt to changing risk profiles?

PubMed

Khan, Stuart J; Deere, Daniel; Leusch, Frederic D L; Humpage, Andrew; Jenkins, Madeleine; Cunliffe, David

2015-11-15

Among the most widely predicted and accepted consequences of global climate change are increases in both the frequency and severity of a variety of extreme weather events. Such weather events include heavy rainfall and floods, cyclones, droughts, heatwaves, extreme cold, and wildfires, each of which can potentially impact drinking water quality by affecting water catchments, storage reservoirs, the performance of water treatment processes or the integrity of distribution systems. Drinking water guidelines, such as the Australian Drinking Water Guidelines and the World Health Organization Guidelines for Drinking-water Quality, provide guidance for the safe management of drinking water. These documents present principles and strategies for managing risks that may be posed to drinking water quality. While these principles and strategies are applicable to all types of water quality risks, very little specific attention has been paid to the management of extreme weather events. We present a review of recent literature on water quality impacts of extreme weather events and consider practical opportunities for improved guidance for water managers. We conclude that there is a case for an enhanced focus on the management of water quality impacts from extreme weather events in future revisions of water quality guidance documents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recent experience with the CQE{trademark}

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

Harrison, C.D.; Kehoe, D.B.; O`Connor, D.C.

1997-12-31

CQE (the Coal Quality Expert) is a software tool that brings a new level of sophistication to fuel decisions by seamlessly integrating the system-wide effects of fuel purchase decisions on power plant performance, emissions, and power generation costs. The CQE technology, which addresses fuel quality from the coal mine to the busbar and the stack, is an integration and improvement of predecessor software tools including: EPRI`s Coal Quality Information System, EPRI`s Coal Cleaning Cost Model, EPRI`s Coal Quality Impact Model, and EPRI and DOE models to predict slagging and fouling. CQE can be used as a stand-alone workstation or asmore » a network application for utilities, coal producers, and equipment manufacturers to perform detailed analyses of the impacts of coal quality, capital improvements, operational changes, and/or environmental compliance alternatives on power plant emissions, performance and production costs. It can be used as a comprehensive, precise and organized methodology for systematically evaluating all such impacts or it may be used in pieces with some default data to perform more strategic or comparative studies.« less

Modification of sperm quality after sexual abstinence in Seba's short-tailed bat, Carollia perspicillata.

PubMed

Wesseling, Charlotte; Fasel, Nicolas; Richner, Heinz; Helfenstein, Fabrice

2016-05-01

In polygynous mating systems, few males have stable access to sexual mates. With an expected higher copulation rate, harem males may deplete seminal fluids or increase epididymal sperm maturation, generating poor sperm quality. In a first study, we reported a higher sperm quality in sneaker males of Carollia perspicillata To test whether the lower sperm quality observed in harem males was generated by an elevated copulation rate, we temporarily removed males of both social statuses from the colony. We thus assessed status-related changes of sperm quality resulting from sexual abstinence. Moreover, released from territory and female guarding, harem males were expected to show a reduction in somatic costs. On the basis of sperm competition models, we predicted a higher resource investment in the ejaculate with the reduction of pre-copulatory efforts. In line with our predictions, sperm quality of harem males improved significantly in contrast to sneaker males, whose sperm quality did not change. Without an increase in ejaculate lipid peroxidation, our results also provide evidence that the duration of sexual abstinence was not sufficient to generate sperm oxidative damage through senescence. Harem males did not show a reduction in blood lipid peroxidation or in the ratio of oxidized to reduced glutathione. In line with the maintenance of these somatic costs, harem males did not invest more superoxide dismutase to the ejaculate to maintain sperm quality. Our results suggest that a difference in copulation rate rather than an adaptation to sperm competition provides sneaker males with higher sperm quality in C. perspicillata. © 2016. Published by The Company of Biologists Ltd.

Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection.

PubMed

Yao, Jingting; Tridandapani, Srini; Wick, Carson A; Bhatti, Pamela T

2017-01-01

To more accurately trigger cardiac computed tomography angiography (CTA) than electrocardiography (ECG) alone, a sub-system is proposed as an intermediate step toward fusing ECG with seismocardiography (SCG). Accurate prediction of quiescent phases is crucial to prospectively gating CTA, which is susceptible to cardiac motion and, thus, can affect the diagnostic quality of images. The key innovation of this sub-system is that it identifies the SCG waveform corresponding to heart sounds and determines their phases within the cardiac cycles. Furthermore, this relationship is modeled as a linear function with respect to heart rate. For this paper, B-mode echocardiography is used as the gold standard for identifying the quiescent phases. We analyzed synchronous ECG, SCG, and echocardiography data acquired from seven healthy subjects (mean age: 31; age range: 22-48; males: 4) and 11 cardiac patients (mean age: 56; age range: 31-78; males: 6). On average, the proposed algorithm was able to successfully identify 79% of the SCG waveforms in systole and 68% in diastole. The simulated results show that SCG-based prediction produced less average phase error than that of ECG. It was found that the accuracy of ECG-based gating is more susceptible to increases in heart rate variability, while SCG-based gating is susceptible to high cycle to cycle variability in morphology. This pilot work of prediction using SCG waveforms enriches the framework of a comprehensive system with multiple modalities that could potentially, in real time, improve the image quality of CTA.

Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Tsvetkova, Olga

2011-06-01

SummaryLand use change is influenced by spatial and temporal factors that interact with watershed resources. Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in water quantity and quality. The objective of this study is to model the nature and emergence of spatial patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simulation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results show high potential for rapid urbanization that could result in lowering of groundwater recharge and increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that affect open space and pervious cover of the watershed system. Water quality deteriorated due to increased runoff which can also impact stream morphology. While overland erosion decreased, instream erosion increased from increased runoff from urban areas. Use of urban best management practices (BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful in sustaining the watershed system.

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Correlations between quality ratings of skilled nursing facilities and multidrug-resistant urinary tract infections.

PubMed

Gucwa, Azad L; Dolar, Veronika; Ye, Chao; Epstein, Stephanie

2016-11-01

The purpose of this study was to determine risk factors for the acquisition of urinary tract infections (UTIs) and multidrug-resistant organisms (MDROs) in residents of skilled nursing facilities (SNFs). Using the informational database provided by the Centers for Medicare and Medicaid Services (CMS), a retrospective logistic regression was performed on 1,523 urine cultures from 12 SNFs located in Long Island, New York. Of the 1,142 positive urine cultures, Escherichia coli was most prevalent. Additionally, 164 (14.4%) of the UTIs were attributed to an MDRO. In multivariate logistic regression, sex and overall quality rating predicted the occurrence of UTIs, whereas identification of MDROs was dependent on the level of nursing care received. The mean predicted probability of UTIs and receipt of contaminated samples was inversely dependent on the facility's rating, where the likelihood increased as overall quality ratings decreased. The CMS's quality rating system may provide some insight into the status of infection control practices in SNFs. The results of this study suggest that potential consumers should focus on the overall star ratings and the competency of the nursing staff in these facilities rather than on individual quality measures. Copyright © 2016 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

PubMed

Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

2017-07-28

New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

Physical activity predicts quality of life and happiness in children and adolescents with cerebral palsy.

PubMed

Maher, Carol Ann; Toohey, Monica; Ferguson, Monika

2016-01-01

To examine the associations between physical activity, health-related quality of life and happiness in young people with cerebral palsy. A total of 70 young people with cerebral palsy (45 males, 25 females; mean age 13 years 11 months, SD 2 years 0 month) took part in a cross-sectional, descriptive postal survey assessing physical activity (Physical Activity Questionnaire for Adolescents), functional ability (Gross Motor Function Classification System), quality of life (Pediatric Quality of Life Inventory 4.0) and happiness (single Likert-scale item). Relationships between physical activity, quality of life and happiness were examined using backward stepwise linear regression. Physical activity significantly predicted physical quality of life (R(2 )= 0.64, β = 6.12, p = 0.02), social quality of life (R(2 )= 0.28, β = 9.27, p < 0.01) and happiness (R(2 )= 0.08, β = 0.9, p = 0.04). Physical activity was not associated with emotional or school quality of life. This study found a positive association between physical activity, social and physical quality of life, and happiness in young people with cerebral palsy. Findings underscore the potential benefits of physical activity for the wellbeing of young people with cerebral palsy, in addition to its well-recognised physical and health benefits. Physical activity is a key predictor of quality of life and happiness in young people with cerebral palsy. Physical activity is widely recognised as having physical health benefits for young people with cerebral palsy; however, this study also highlights that it may have important benefits for wellbeing, quality of life and happiness. This emphasises the need for clinical services and intervention studies aimed specifically at increasing physical activity amongst children and adolescents with cerebral palsy.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.

PubMed

El-Sayed, Hesham; Sankar, Sharmi; Daraghmi, Yousef-Awwad; Tiwari, Prayag; Rattagan, Ekarat; Mohanty, Manoranjan; Puthal, Deepak; Prasad, Mukesh

2018-05-24

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Statistical quality control through overall vibration analysis

NASA Astrophysics Data System (ADS)

Carnero, M. ^a. Carmen; González-Palma, Rafael; Almorza, David; Mayorga, Pedro; López-Escobar, Carlos

2010-05-01

The present study introduces the concept of statistical quality control in automotive wheel bearings manufacturing processes. Defects on products under analysis can have a direct influence on passengers' safety and comfort. At present, the use of vibration analysis on machine tools for quality control purposes is not very extensive in manufacturing facilities. Noise and vibration are common quality problems in bearings. These failure modes likely occur under certain operating conditions and do not require high vibration amplitudes but relate to certain vibration frequencies. The vibration frequencies are affected by the type of surface problems (chattering) of ball races that are generated through grinding processes. The purpose of this paper is to identify grinding process variables that affect the quality of bearings by using statistical principles in the field of machine tools. In addition, an evaluation of the quality results of the finished parts under different combinations of process variables is assessed. This paper intends to establish the foundations to predict the quality of the products through the analysis of self-induced vibrations during the contact between the grinding wheel and the parts. To achieve this goal, the overall self-induced vibration readings under different combinations of process variables are analysed using statistical tools. The analysis of data and design of experiments follows a classical approach, considering all potential interactions between variables. The analysis of data is conducted through analysis of variance (ANOVA) for data sets that meet normality and homoscedasticity criteria. This paper utilizes different statistical tools to support the conclusions such as chi squared, Shapiro-Wilks, symmetry, Kurtosis, Cochran, Hartlett, and Hartley and Krushal-Wallis. The analysis presented is the starting point to extend the use of predictive techniques (vibration analysis) for quality control. This paper demonstrates the existence of predictive variables (high-frequency vibration displacements) that are sensible to the processes setup and the quality of the products obtained. Based on the result of this overall vibration analysis, a second paper will analyse self-induced vibration spectrums in order to define limit vibration bands, controllable every cycle or connected to permanent vibration-monitoring systems able to adjust sensible process variables identified by ANOVA, once the vibration readings exceed established quality limits.

No-reference quality assessment based on visual perception

NASA Astrophysics Data System (ADS)

Li, Junshan; Yang, Yawei; Hu, Shuangyan; Zhang, Jiao

2014-11-01

The visual quality assessment of images/videos is an ongoing hot research topic, which has become more and more important for numerous image and video processing applications with the rapid development of digital imaging and communication technologies. The goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images/videos in agreement with human quality judgments. Up to now, two kinds of models have been used for IQA, namely full-reference (FR) and no-reference (NR) models. For FR models, IQA algorithms interpret image quality as fidelity or similarity with a perfect image in some perceptual space. However, the reference image is not available in many practical applications, and a NR IQA approach is desired. Considering natural vision as optimized by the millions of years of evolutionary pressure, many methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychological features of the human visual system (HVS). To reach this goal, researchers try to simulate HVS with image sparsity coding and supervised machine learning, which are two main features of HVS. A typical HVS captures the scenes by sparsity coding, and uses experienced knowledge to apperceive objects. In this paper, we propose a novel IQA approach based on visual perception. Firstly, a standard model of HVS is studied and analyzed, and the sparse representation of image is accomplished with the model; and then, the mapping correlation between sparse codes and subjective quality scores is trained with the regression technique of least squaresupport vector machine (LS-SVM), which gains the regressor that can predict the image quality; the visual metric of image is predicted with the trained regressor at last. We validate the performance of proposed approach on Laboratory for Image and Video Engineering (LIVE) database, the specific contents of the type of distortions present in the database are: 227 images of JPEG2000, 233 images of JPEG, 174 images of White Noise, 174 images of Gaussian Blur, 174 images of Fast Fading. The database includes subjective differential mean opinion score (DMOS) for each image. The experimental results show that the proposed approach not only can assess many kinds of distorted images quality, but also exhibits a superior accuracy and monotonicity.

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care

PubMed Central

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables—training, self-efficacy, system quality and information quality—on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time. PMID:29315323

Energy density and variability in abundance of pigeon guillemot prey: Support for the quality-variability trade-off hypothesis

USGS Publications Warehouse

Litzow, Michael A.; Piatt, John F.; Abookire, Alisa A.; Robards, Martin D.

2004-01-01

1. The quality-variability trade-off hypothesis predicts that (i) energy density (kJ g-1) and spatial-temporal variability in abundance are positively correlated in nearshore marine fishes; and (ii) prey selection by a nearshore piscivore, the pigeon guillemot (Cepphus columba Pallas), is negatively affected by variability in abundance. 2. We tested these predictions with data from a 4-year study that measured fish abundance with beach seines and pigeon guillemot prey utilization with visual identification of chick meals. 3. The first prediction was supported. Pearson's correlation showed that fishes with higher energy density were more variable on seasonal (r = 0.71) and annual (r = 0.66) time scales. Higher energy density fishes were also more abundant overall (r = 0.85) and more patchy at a scale of 10s of km (r = 0.77). 4. Prey utilization by pigeon guillemots was strongly non-random. Relative preference, defined as the difference between log-ratio transformed proportions of individual prey taxa in chick diets and beach seine catches, was significantly different from zero for seven of the eight main prey categories. 5. The second prediction was also supported. We used principal component analysis (PCA) to summarize variability in correlated prey characteristics (energy density, availability and variability in abundance). Two PCA scores explained 32% of observed variability in pigeon guillemot prey utilization. Seasonal variability in abundance was negatively weighted by these PCA scores, providing evidence of risk-averse selection. Prey availability, energy density and km-scale variability in abundance were positively weighted. 6. Trophic interactions are known to create variability in resource distribution in other systems. We propose that links between resource quality and the strength of trophic interactions may produce resource quality-variability trade-offs.

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories

USGS Publications Warehouse

Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z; Read, Jordan S.; Ibelings, Bas W; Valensini, Fiona J; Brookes, Justin D

2015-01-01

Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of flexible model libraries and data from observationnetworks, within a learning framework, as a means to improve the accuracy and scope of model predictions.The framework is comprised of a data assimilation component that utilizes diverse data streams from sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identified as a means to develop and inte-grate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry,and, in doing so, also advance the role of prediction in aquatic ecosystem management.

Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

NASA Astrophysics Data System (ADS)

Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z.; Read, Jordan S.; Ibelings, Bas W.; Valesini, Fiona J.; Brookes, Justin D.

2015-09-01

Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management.

A portable device for rapid nondestructive detection of fresh meat quality

NASA Astrophysics Data System (ADS)

Lin, Wan; Peng, Yankun

2014-05-01

Quality attributes of fresh meat influence nutritional value and consumers' purchasing power. In order to meet the demand of inspection department for portable device, a rapid and nondestructive detection device for fresh meat quality based on ARM (Advanced RISC Machines) processor and VIS/NIR technology was designed. Working principal, hardware composition, software system and functional test were introduced. Hardware system consisted of ARM processing unit, light source unit, detection probe unit, spectral data acquisition unit, LCD (Liquid Crystal Display) touch screen display unit, power unit and the cooling unit. Linux operating system and quality parameters acquisition processing application were designed. This system has realized collecting spectral signal, storing, displaying and processing as integration with the weight of 3.5 kg. 40 pieces of beef were used in experiment to validate the stability and reliability. The results indicated that prediction model developed using PLSR method using SNV as pre-processing method had good performance, with the correlation coefficient of 0.90 and root mean square error of 1.56 for validation set for L*, 0.95 and 1.74 for a*，0.94 and 0.59 for b*, 0.88 and 0.13 for pH, 0.79 and 12.46 for tenderness, 0.89 and 0.91 for water content, respectively. The experimental result shows that this device can be a useful tool for detecting quality of meat.

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

SU-D-BRB-02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality

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

Robertson, SP; Moore, JA; Hui, X

Purpose: Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality. Methods: Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan-A: traditional IMRT optimization using clinically relevant default objectives; Plan-B: traditional IMRT optimization using database dose predictions; Plan-C: autoplanning using default objectives; and Plan-D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volumemore » (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%). Results: All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman’s χ{sup 2} ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A-D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A-D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B-D were significantly different from Plan-A for 3, 17, and 19 objectives, respectively. Plans C-D were also significantly different from Plan-B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan-D provided significantly lower dose than plan C (p = 0.003). Conclusion: Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted in a larger cohort across HN, prostate, and other treatment sites. This work is supported by Philips Radiation Oncology Systems.« less

Assessing microscope image focus quality with deep learning.

PubMed

Yang, Samuel J; Berndl, Marc; Michael Ando, D; Barch, Mariya; Narayanaswamy, Arunachalam; Christiansen, Eric; Hoyer, Stephan; Roat, Chris; Hung, Jane; Rueden, Curtis T; Shankar, Asim; Finkbeiner, Steven; Nelson, Philip

2018-03-15

Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Implementation and Test of the Automatic Flight Dynamics Operations for Geostationary Satellite Mission

NASA Astrophysics Data System (ADS)

Park, Sangwook; Lee, Young-Ran; Hwang, Yoola; Javier Santiago Noguero Galilea

2009-12-01

This paper describes the Flight Dynamics Automation (FDA) system for COMS Flight Dynamics System (FDS) and its test result in terms of the performance of the automation jobs. FDA controls the flight dynamics functions such as orbit determination, orbit prediction, event prediction, and fuel accounting. The designed FDA is independent from the specific characteristics which are defined by spacecraft manufacturer or specific satellite missions. Therefore, FDA could easily links its autonomous job control functions to any satellite mission control system with some interface modification. By adding autonomous system along with flight dynamics system, it decreases the operator’s tedious and repeated jobs but increase the usability and reliability of the system. Therefore, FDA is used to improve the completeness of whole mission control system’s quality. The FDA is applied to the real flight dynamics system of a geostationary satellite, COMS and the experimental test is performed. The experimental result shows the stability and reliability of the mission control operations through the automatic job control.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

NASA Astrophysics Data System (ADS)

Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

2017-06-01

Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

Sequence similarity is more relevant than species specificity in probabilistic backtranslation.

PubMed

Ferro, Alfredo; Giugno, Rosalba; Pigola, Giuseppe; Pulvirenti, Alfredo; Di Pietro, Cinzia; Purrello, Michele; Ragusa, Marco

2007-02-21

Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically.

Artificial neural networks applied to flow prediction scenarios in Tomebamba River - Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

NASA Astrophysics Data System (ADS)

Cisneros, Felipe; Veintimilla, Jaime

2013-04-01

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error are minimal. These predictions are useful for flood and water quality control and management at City of Cuenca Ecuador

Parenting and Preschool Self-Regulation as Predictors of Social Emotional Competence in 1st Grade

PubMed Central

Russell, Beth S.; Lee, Jungeun Olivia; Spieker, Susan; Oxford, Monica L.

2016-01-01

The current longitudinal study used data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD) to examine a model of development that emphasizes early caregiving environments as predictors of social emotional competence (including classroom competence). This path analysis model included features of parenting, emotion regulation, preschool language skills, and attention to predict child outcomes in 1st grade. Early caregiving environments were directly predictive of peer relationship satisfaction, oppositional behavior, social skills, and classroom competence over and above significant mediated effects through preschool self regulation (language, inattention, and anger/frustration). These results suggest that the characteristics of supportive and stimulating caregiving shift in valence over time, such that qualities of the infant-child relationship that are significant in predicting early childhood outcomes are not the same as the caregiving qualities that move to the foreground in predicting primary school outcomes. Implications for school-readiness programming are discussed, including interventions in the early caregiving system to encourage sensitive and supportive parent child interactions to bolster school readiness via the development of social-emotional competence. PMID:27616805

Presence of indicator plant species as a predictor of wetland vegetation integrity

USGS Publications Warehouse

Stapanian, Martin A.; Adams, Jean V.; Gara, Brian

2013-01-01

We fit regression and classification tree models to vegetation data collected from Ohio (USA) wetlands to determine (1) which species best predict Ohio vegetation index of biotic integrity (OVIBI) score and (2) which species best predict high-quality wetlands (OVIBI score >75). The simplest regression tree model predicted OVIBI score based on the occurrence of three plant species: skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris). The lowest OVIBI scores were best predicted by the absence of the selected plant species rather than by the presence of other species. The simplest classification tree model predicted high-quality wetlands based on the occurrence of two plant species: skunk-cabbage and marsh-fern (Thelypteris palustris). The overall misclassification rate from this tree was 13 %. Again, low-quality wetlands were better predicted than high-quality wetlands by the absence of selected species rather than the presence of other species using the classification tree model. Our results suggest that a species’ wetland status classification and coefficient of conservatism are of little use in predicting wetland quality. A simple, statistically derived species checklist such as the one created in this study could be used by field biologists to quickly and efficiently identify wetland sites likely to be regulated as high-quality, and requiring more intensive field assessments. Alternatively, it can be used for advanced determinations of low-quality wetlands. Agencies can save considerable money by screening wetlands for the presence/absence of such “indicator” species before issuing permits.

Having Fun on Facebook?: Mothers' Enjoyment as a Moderator of Mental Health and Facebook Use.

PubMed

Kaufmann, Renee; Buckner, Marjorie M; Ledbetter, Andrew M

2017-08-01

This study reports results of a study that examined the extent to which contextual factors (i.e., income level and number of children) might predict a mother's mental health quality, which, in turn, may predict level of engagement with Facebook. Results supported this model, finding that mothers with more children and lower income possess lower mental health quality, and lower mental health quality predicted more frequent Facebook use. However, this pattern was qualified by a mother's level of enjoyment of Facebook, such that mental health quality did not significantly predict Facebook intensity when enjoyment of Facebook was low. This research extends practitioners' knowledge of mothers' mental health quality by identifying a behavior that may indicate lower mental health quality and enhance abilities to recognize mothers who may need support or treatment. Future directions for this research are included.

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

Hyperspectral imaging technique for determination of pork freshness attributes

NASA Astrophysics Data System (ADS)

Li, Yongyu; Zhang, Leilei; Peng, Yankun; Tang, Xiuying; Chao, Kuanglin; Dhakal, Sagar

2011-06-01

Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive detection of pork freshness.

RZWQM predicted effects of soil N testing with incorporated automatic parameter optimization software (PEST) and weather input quality control

USDA-ARS?s Scientific Manuscript database

Among the most promising tools available for determining precise N requirements are soil mineral N tests. Field tests that evaluated this practice, however, have been conducted under only limited weather and soil conditions. Previous research has shown that using agricultural systems models such as ...

Writing Quality Predicts Chinese Learning

ERIC Educational Resources Information Center

Guan, Connie Qun; Perfetti, Charles A.; Meng, Wanjin

2015-01-01

To examine the importance of manual character writing to reading in a new writing system, 48 adult Chinese-as-a-foreign-language students were taught characters in either a character writing-to-read or an alphabet typing-to-read condition, and engaged in corresponding handwriting or typing training for five consecutive days. Prior knowledge of…

Evaluation of the RWEQ and SWEEP in simulating soil and PM10 loss from a portable wind tunnel

USDA-ARS?s Scientific Manuscript database

Wind erosion threatens sustainable agriculture and environmental quality in the Columbia Plateau region of the US Pacific Northwest. Wind erosion models such as Wind Erosion Prediction System (WEPS) and the Revised Wind Erosion Equation (RWEQ) have been developed as tools for identifying practices t...

Chronic Pain: The Impact on Academic, Social, and Emotional Functioning

ERIC Educational Resources Information Center

Parkins, Jason M.; Gfroerer, Susan D.

2009-01-01

Chronic pain is persistent and recurrent pain that tends to fluctuate in severity, quality, regularity, and predictability. It can occur in a single or multiple body regions or organ systems. Some of the most frequently reported types of chronic pain include headaches, recurrent abdominal pain (RAP), and musculoskeletal pain. In contrast to acute…

Assessing Text-Based Writing of Low-Skilled College Students

ERIC Educational Resources Information Center

Perin, Dolores; Lauterbach, Mark

2018-01-01

The problem of poor writing skills at the postsecondary level is a large and troubling one. This study investigated the writing skills of low-skilled adults attending college developmental education courses by determining whether variables from an automated scoring system were predictive of human scores on writing quality rubrics. The human-scored…

Application of WEPP to a Southern Appalachian Forest road

Treesearch

Johnny M. Grace

2005-01-01

Forest roads can be major sources of sediment and soil erosion from southern Appalachian Mountain watersheds. Sediments from forest roads are a concern due to their potential delivery to stream systems resulting in degradation of water quality. Prediction of sediment yields from forest road components can provide valuable information in planning, locating, and...

A landscape based, systems dynamic model for assessing impacts of urban development on water quality for sustainable seagrass growth in Tampa Bay, Florida

EPA Science Inventory

We present an integrated assessment model to predict potential unintended consequences of urban development on the sustainability of seagrasses and preservation of ecosystem services, such as catchable fish, in Tampa Bay. Ecosystem services are those ecological functions and pro...

Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume

EPA Science Inventory

Global nutrient cycles have been altered by use of fossil fuels and fertilizers resulting in increases in nutrient loads to aquatic systems. In the United States, excess nutrients have been repeatedly reported as the primary cause of lake water quality impairments. Setting nutr...

Family Interaction Patterns, Career Planning Attitudes, and Vocational Identity of High School Adolescents

ERIC Educational Resources Information Center

Hargrove, Byron K.; Inman, Arpana G.; Crane, Randy L.

2005-01-01

The purpose of the current study was to examine how perceptions of family interaction patterns as defined along three dimensions of family environment (quality of family relationships, family goal-orientations, and degree of organization and control within the family system) predict vocational identity and career planning attitudes among male and…

Is ATAR Useful for Predicting the Success of Australian Students in Initial Teacher Education?

ERIC Educational Resources Information Center

Wright, Vince J.

2015-01-01

Quality teaching is the most significant systemic factor contributing to student achievement. Attracting, developing and retaining effective teachers are important goals for Australia as they are for all nations. Debate rages currently about criteria for selection of students into Initial Teacher Education (ITE). The Australian Tertiary Admission…

Proceedings and findings of the 1976 Workshop on Ride Quality. [passenger acceptance of transportation systems

NASA Technical Reports Server (NTRS)

Kuhlthau, A. R. (Editor)

1976-01-01

The workshop was organized around the study of the three basic transfer functions required to evaluate and/or predict passenger acceptance of transportation systems: These are the vehicle, passenger, and value transfer functions. For the purpose of establishing working groups corresponding to the basic transfer functions, it was decided to split the vehicle transfer function into two distinct groups studying surface vehicles and air/marine vehicles, respectively.

Means of processing information on motor activity of patient during sleep

NASA Astrophysics Data System (ADS)

Gorbunov, A. V.; Egorov, V. S.; Neprokin, A. V.

2018-05-01

Information about the physical activity of a person during sleep is an important component of information about the state of one’s nervous system, the interpretation of which can be used for disease monitoring, diagnostics and prediction of diseases of the nervous system. This will significantly reduce the risks of disability and improve the quality of life of the patient in accordance with the concept of mobile telemedicine (mHealth).

Metrics of Software Quality.

DTIC Science & Technology

1980-11-01

Systems: A Raytheon Project History", RADC-TR-77-188, Final Technical Report, June 1977. 4. IBM Federal Systems Division, "Statistical Prediction of...147, June 1979. 4. W. D. Brooks, R. W. Motley, "Analysis of Discrete Software Reliability Models", IBM Corp., RADC-TR-80-84, RADC, New York, April 1980...J. C. King of IBM (Reference 9) and Lori A. Clark (Reference 10) of the University of Massachusetts. Programs, so exercised must be augmented so they

Analysis of defects of overhead facade systems and other light thin-walled structures

NASA Astrophysics Data System (ADS)

Endzhievskiy, L.; Frolovskaia, A.; Petrova, Y.

2017-04-01

This paper analyzes the defects and the causes of contemporary design solutions with an example of overhead facade systems with ventilated air gaps and light steel thin-walled structures on the basis of field experiments. The analysis is performed at all stages of work: design, manufacture, including quality, construction, and operation. Practical examples are given. The main causes of accidents and the accident rate prediction are looked upon and discussed.

Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines: Preprint

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

Schlipf, David; Raach, Steffen; Haizmann, Florian

2015-12-14

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the predictionmore » time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.« less

Factors influencing nurses' decisions to raise concerns about care quality.

PubMed

Attree, Moira

2007-05-01

To explore factors that influence nurses' decisions to raise concerns about standards of practice. Health care practitioners have a key role in monitoring care quality. Nurses are required by their professional body to raise concerns about standards; however, under-reporting is the norm. Grounded theory was used to collect and analyse data from semi-structured interviews with 142 practising nurses, theoretically sampled from three Acute NHS Trusts in England. Fear of repercussions, retribution, labelling and blame for raising concerns, about which they predicted nothing would be done, were identified as disincentives to raising concerns. Reporting was perceived as a high-risk:low-benefit action. Nurses lacked confidence in reporting systems. Disincentives to reporting need to be addressed if an open culture, which promotes quality, safety and learning, is to be developed. Findings give cause for concern and indicate a need to review organizational and professional guidelines, and organizational reporting systems.

Integrated quality assessment of sediments from harbour areas in Santos-São Vicente Estuarine System, Southern Brazil

NASA Astrophysics Data System (ADS)

Buruaem, Lucas Moreira; de Castro, Ítalo Braga; Hortellani, Marcos Antonio; Taniguchi, Satie; Fillmann, Gilberto; Sasaki, Silvio Tarou; Varella Petti, Mônica Angélica; Sarkis, Jorge Eduardo de Souza; Bícego, Márcia Caruso; Maranho, Luciane Alves; Davanso, Marcela Bergo; Nonato, Edmundo Ferraz; Cesar, Augusto; Costa-Lotufo, Leticia Veras; Abessa, Denis Moledo de Souza

2013-09-01

Santos-São Vicente Estuarine System is a highly populated coastal zone in Brazil and where it is located the major port of Latin America. Historically, port activities, industrial and domestic effluents discharges have constituted the main sources of contaminants to estuarine system. This study aimed to assess the recent status of sediment quality from 5 zones of Port of Santos by applying a lines-of-evidence approach through integrating results of: (1) acute toxicity of whole sediment and chronic toxicity of liquid phases; (2) grain size, organic matter, organic carbon, nitrogen, phosphorus, trace metals, polycyclic aromatic hydrocarbons, linear alkylbenzenes and butyltins; (3) benthic community descriptors. Results revealed a gradient of increasing contamination for metals and organic compounds, alongside with their geochemical carriers. Sediment liquid phases were more toxic compared to whole sediment. Low number of species and individuals indicated the impoverishment of benthic community. The use of site-specific sediment quality guidelines was more appropriate to predict sediment toxicity. The integration of results through Sediment Quality Triad approach and principal component analysis allowed observing the effects of natural stressors and dredging on sediment quality and benthic distribution. Even with recent governmental efforts to control, pollution is still relevant in Port of Santos and a threat to local ecosystems.

Relations among early adolescents' parent-adolescent attachment, perceived social competence, and friendship quality.

PubMed

Boling, Melissa W; Barry, Carolyn McNamara; Kotchick, Beth A; Lowry, Jen

2011-12-01

To assess whether the relation between attachment and friendship quality may be explained by social competence, 113 students in Grades 7 and 8 from the Baltimore metropolitan area completed self-report questionnaires on the variables of interest. In hierarchical regression analyses, both maternal Affective Quality of Attachment and the interaction of School with paternal Affective Quality of Attachment predicted social competence. Also, the interaction of School with paternal Affective Quality of Attachment predicted negative friendship features, whereas social competence predicted positive friendship features. These findings provide support for a pathway between adolescents' attachment to both parents and adolescents' perceived social competence and, in turn, their friendship quality.

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

ProTSAV: A protein tertiary structure analysis and validation server.

PubMed

Singh, Ankita; Kaushik, Rahul; Mishra, Avinash; Shanker, Asheesh; Jayaram, B

2016-01-01

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Controlling misses and false alarms in a machine learning framework for predicting uniformity of printed pages

NASA Astrophysics Data System (ADS)

Nguyen, Minh Q.; Allebach, Jan P.

2015-01-01

In our previous work1 , we presented a block-based technique to analyze printed page uniformity both visually and metrically. The features learned from the models were then employed in a Support Vector Machine (SVM) framework to classify the pages into one of the two categories of acceptable and unacceptable quality. In this paper, we introduce a set of tools for machine learning in the assessment of printed page uniformity. This work is primarily targeted to the printing industry, specifically the ubiquitous laser, electrophotographic printer. We use features that are well-correlated with the rankings of expert observers to develop a novel machine learning framework that allows one to achieve the minimum "false alarm" rate, subject to a chosen "miss" rate. Surprisingly, most of the research that has been conducted on machine learning does not consider this framework. During the process of developing a new product, test engineers will print hundreds of test pages, which can be scanned and then analyzed by an autonomous algorithm. Among these pages, most may be of acceptable quality. The objective is to find the ones that are not. These will provide critically important information to systems designers, regarding issues that need to be addressed in improving the printer design. A "miss" is defined to be a page that is not of acceptable quality to an expert observer that the prediction algorithm declares to be a "pass". Misses are a serious problem, since they represent problems that will not be seen by the systems designers. On the other hand, "false alarms" correspond to pages that an expert observer would declare to be of acceptable quality, but which are flagged by the prediction algorithm as "fails". In a typical printer testing and development scenario, such pages would be examined by an expert, and found to be of acceptable quality after all. "False alarm" pages result in extra pages to be examined by expert observers, which increases labor cost. But "false alarms" are not nearly as catastrophic as "misses", which represent potentially serious problems that are never seen by the systems developers. This scenario motivates us to develop a machine learning framework that will achieve the minimum "false alarm" rate subject to a specified "miss" rate. In order to construct such a set of receiver operating characteristic2 (ROC) curves, we examine various tools for the prediction, ranging from an exhaustive search over the space of the nonlinear discriminants to a Cost-Sentitive SVM3 framework. We then compare the curves gained from those methods. Our work shows promise for applying a standard framework to obtain a full ROC curve when it comes to tackling other machine learning problems in industry.

Quantifying risk and assessing outcome in cardiac surgery.

PubMed

Higgins, T L

1998-06-01

Quality improvement, research, and reporting of outcome results can be stratified by preoperative risk by using a logistic regression equation or scores to correct for multiple risk factors. The more than 30-fold mortality differences between lowest and highest risk patients make it critical to stratify outcome results by patient severity. Probabilities are not predictions, however, and caution must be exercised when applying scores to individuals. Outcome assessment will grow in its importance to professionals, initially in the guise of quality reporting and improvement, but increasingly as a tool for risk assessment, patient counseling, and directing therapeutic decisions based on more complete information about patient subgroups. Physicians may be called on for recommendations in choosing systems for their hospitals and communities. Therefore, it is important to have an understanding of how such systems are developed, what factors indicate adequate performance of a system, and how such systems of risk stratification should be applied in practice.

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

PubMed Central

Safa Sadiq, Ali; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches. PMID:25574490

An adaptive handover prediction scheme for seamless mobility based wireless networks.

PubMed

Sadiq, Ali Safa; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Therapy Decision Support Based on Recommender System Methods

PubMed Central

Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen

2017-01-01

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system. PMID:29065657

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Stress and Negative Relationship Quality among Older Couples: Implications for Blood Pressure.

PubMed

Birditt, Kira S; Newton, Nicky J; Cranford, James A; Ryan, Lindsay H

2016-09-01

The cardiovascular system may represent a significant pathway by which marriage and stress influence health, but research has focused on married individuals cross-sectionally. This study examined associations among chronic stress, negative spousal relationship quality, and systolic blood pressure over time among middle-aged and older husbands and wives. Participants were from the nationally representative longitudinal Health and Retirement Study. A total of 1,356 (N = 2,712) married and cohabitating couples completed psychosocial and biomeasure assessments in waves 2006 and 2010. Analyses examined whether Wave 1 (2006) relationship quality and stress were associated with changes in blood pressure over time. The effects of stress and negative relationship quality were dyadic and varied by gender. Husbands had increased blood pressure when wives reported greater stress, and this link was exacerbated by negative spousal relationship quality. Negative relationship quality predicted increased blood pressure when both members of the couple reported negative quality relations. Findings support the dyadic biopsychosocial model of marriage and health indicating: (a) stress and relationship quality directly effect the cardiovascular system, (b) relationship quality moderates the effect of stress, and (c) the dyad rather than only the individual should be considered when examining marriage and health. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Gas House Autonomous System Monitoring

NASA Technical Reports Server (NTRS)

Miller, Luke; Edsall, Ashley

2015-01-01

Gas House Autonomous System Monitoring (GHASM) will employ Integrated System Health Monitoring (ISHM) of cryogenic fluids in the High Pressure Gas Facility at Stennis Space Center. The preliminary focus of development incorporates the passive monitoring and eventual commanding of the Nitrogen System. ISHM offers generic system awareness, adept at using concepts rather than specific error cases. As an enabler for autonomy, ISHM provides capabilities inclusive of anomaly detection, diagnosis, and abnormality prediction. Advancing ISHM and Autonomous Operation functional capabilities enhances quality of data, optimizes safety, improves cost effectiveness, and has direct benefits to a wide spectrum of aerospace applications.

Structural Predictors of Child Care Quality in Child Care Homes.

ERIC Educational Resources Information Center

Burchinal, Margaret; Howes, Carollee; Kontos, Susan

2002-01-01

Used data from a family child care study and a licensing study to identify dimensions best predicting global day care quality in over 300 child care homes. Found that caregiver training most consistently predicted global quality. Found no reliable association between care quality and child-caregiver ratio or age-weighted group size recommendations…

Workshop on Satellite and In situ Observations for Climate Prediction

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

Acker, J.G.; Busalacchi, A.

1995-02-01

Participants in this workshop, which convened in Venice, Italy, 6-8 May 1993, met to consider the current state of climate monitoring programs and instrumentation for the purpose of climatological prediction on short-term (seasonal to interannual) timescales. Data quality and coverage requirements for definition of oceanographic heat and momentum fluxes, scales of inter- and intra-annual variability, and land-ocean-atmosphere exchange processes were examined. Advantages and disadvantages of earth-based and spaceborne monitoring systems were considered, as were the structures for future monitoring networks, research programs, and modeling studies.

Workshop on Satellite and In situ Observations for Climate Prediction

NASA Technical Reports Server (NTRS)

Acker, James G.; Busalacchi, Antonio

1995-01-01

Participants in this workshop, which convened in Venice, Italy, 6-8 May 1993, met to consider the current state of climate monitoring programs and instrumentation for the purpose of climatological prediction on short-term (seasonal to interannual) timescales. Data quality and coverage requirements for definition of oceanographic heat and momentum fluxes, scales of inter- and intra-annual variability, and land-ocean-atmosphere exchange processes were examined. Advantages and disadvantages of earth-based and spaceborne monitoring systems were considered, as were the structures for future monitoring networks, research programs, and modeling studies.

Analysis of the longitudinal handling qualities and pilot-induced-oscillation tendencies of the High-Angle-of-Attack Research Vehicle (HARV)

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1994-01-01

The NASA High-Angle-of Attack Research Vehicle (HARV), a modified F-18 aircraft, experienced handling qualities problems in recent flight tests at NASA Dryden Research Center. Foremost in these problems was the tendency of the pilot-aircraft system to exhibit a potentially dangerous phenomenon known as a pilot-induced oscillation (PIO). When they occur, PIO's can severely restrict performance, sharply dimish mission capabilities, and can even result in aircraft loss. A pilot/vehicle analysis was undertaken with the goal of reducing these PIO tendencies and improving the overall vehicle handling qualities with as few changes as possible to the existing feedback/feedforward flight control laws. Utilizing a pair of analytical pilot models developed by the author, a pilot/vehicle analysis of the existing longitudinal flight control system was undertaken. The analysis included prediction of overall handling qualities levels and PIO susceptability. The analysis indicated that improvement in the flight control system was warranted and led to the formulation of a simple control stick command shaping filter. Analysis of the pilot/vehicle system with the shaping filter indicated significant improvements in handling qualities and PIO tendencies could be achieved. A non-real time simulation of the modified control system was undertaken with a realistic, nonlinear model of the current HARV. Special emphasis was placed upon those details of the command filter implementation which could effect safety of flight. The modified system is currently awaiting evaluation in the real-time, pilot-in-the-loop, Dual-Maneuvering-Simulator (DMS) facility at Langley.

Assessing Receiving Water Quality Impacts due to Flow Path Alteration in Residential Catchments, using the Stormwater and Wastewater Management Model

NASA Astrophysics Data System (ADS)

Wolosoff, S. E.; Duncan, J.; Endreny, T.

2001-05-01

The Croton water supply system, responsible for supplying approximately 10% of New York City's water, provides an opportunity for exploration into the impacts of significant terrestrial flow path alteration upon receiving water quality. Natural flow paths are altered during residential development in order to allow for construction at a given location, reductions in water table elevation in low lying areas and to provide drainage of increased overland flow volumes. Runoff conducted through an artificial drainage system, is prevented from being attenuated by the natural environment, thus the pollutant removal capacity inherent in most natural catchments is often limited to areas where flow paths are not altered by development. By contrasting the impacts of flow path alterations in two small catchments in the Croton system, with different densities of residential development, we can begin to identify appropriate limits to the re-routing of runoff in catchments draining into surface water supplies. The Stormwater and Wastewater Management Model (SWMM) will be used as a tool to predict the runoff quantity and quality generated from two small residential catchments and to simulate the potential benefits of changes to the existing drainage system design, which may improve water quality due to longer residence times.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Monitoring and analysis of air quality in Riga

NASA Astrophysics Data System (ADS)

Ubelis, Arnolds; Leitass, Andris; Vitols, Maris

1995-09-01

Riga, the capital of Latvia is a city with nearly 900,000 inhabitants and various highly concentrated industries. Air pollution in Riga is a serious problem affecting health and damaging valuable buildings of historical importance, as acid rain and smog take their toll. Therefore the Air Quality Management System with significant assistance from Swedish Government and persistent efforts from Riga City Council was arranged in Riga. It contains INDIC AIRVIRO system which simulates and evaluates air pollution levels at various locations. It then processes the data in order to predict air quality based on a number of criteria and parameters, measured by OPSIS differential absorption instruments, as well as data from the Meteorological Service and results of episodic measurements. The analysis of the results provided by Riga Air Quality Management System for the first time allows us to start comprehensive supervision of troposphere physical, chemical, and photochemical processes in the air of Riga as well as to appreciate the influence of lcoal pollution and transboundary transfer. The report contains the actual results of this work and first attempts of analysis as well as overview about activities towards research and teaching in the fields of spectroscopy and photochemistry of polluted atmospheres.

Thermodynamic metrics for measuring the ``sustainability'' of design for recycling

NASA Astrophysics Data System (ADS)

Reuter, Markus; van Schaik, Antoinette

2008-08-01

In this article, exergy is applied as a parameter to measure the “sustainability” of a recycling system in addition to the fundamental prediction of material recycling and energy recovery, summarizing a development of over 20 years by the principal author supported by various co-workers, Ph.D., and M.Sc. students. In order to achieve this, recyclate qualities and particle size distributions throughout the system must be predicted as a function of product design, liberation during shredding, process dynamics, physical separation physics, and metallurgical thermodynamics. This crucial development enables the estimation of the true exergy of a recycling system from its inputs and outputs including all its realistic industrial traits. These models have among others been linked to computer aided design tools of the automotive industry and have been used to evaluate the performance of waste electric and electronic equipment recycling systems in The Netherlands. This paper also suggests that the complete system must be optimized to find a “truer” optimum of the material production system linked to the consumer market.

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

PubMed

Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

2014-03-01

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

Analysis of aircraft longitudinal handling qualities

NASA Technical Reports Server (NTRS)

Hess, R. A.

1981-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot induced oscillations (PIO) is formulated. Finally, a model-based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.