Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

PubMed

Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

2017-08-07

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

A multivariate approach for a comparison of big data matrices. Case study: thermo-hygrometric monitoring inside the Carcer Tullianum (Rome) in the absence and in the presence of visitors.

PubMed

Visco, Giovanni; Plattner, Susanne H; Fortini, Patrizia; Sammartino, Mariapia

2017-06-01

In the last decades, the very fast improvement of the analytical instrumentation has led to the possibility of quickly and easily getting a lot of data; in turn, the need of advanced statistical methods suitable to extract the full information furnished by instruments has increased. Such kind of data treatments is particularly important in any case of continuous monitoring of one or more parameters, so the microclimate monitoring is a typical example for this application. Microclimate control is essential in the conservation of Cultural Heritage (CH), but decisions on optimal conservation parameters cannot base only on existing norms that do not take into account the environment's history. Often CH has survived for many centuries in conditions that must be considered risky but also a stable state (equilibrium) resulting from a long adaptation process during which a more or less heavy damage occurred to the materials. Any successive change of microclimate parameters has interrupted this equilibrium conditions and has induced further damage to material until a new equilibrium is reached; dimension and frequency of changes are proportional to the expected damage. This thermodynamic consideration provides the background for a CH conservation project based on microclimate control and highlights the importance of environmental monitoring for the identification of equilibrium parameters to be maintained. In 2010, we monitored the microclimate of an important historical building in Rome, the Mamertino Carcer, before its opening to visitors. One year later, we repeated the monitoring in the presence of visitors, and here, we present a careful choice of multivariate data treatments adopted for an enough, simple and immediate evaluation of the microclimatic changes; this allows an easier understanding also for persons with not too deep scientific background, such as Superintendents and, in turn, really useful information to provide suggestions for a conservation project. Results evidenced the expected loss of isolation of the site that occurred by opening to visitors; this led to wider excursions of both temperature and relative humidity and, in turn, to a worsening of the conservative conditions. Surely, a monitoring of particulate matter, correlated to air fluxes and, in turn, to microclimate, is of fundamental importance for the conservation of frescoes and will be object of one of our future diagnostic interventions in the site.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

[Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

PubMed

Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

2017-10-01

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

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

PubMed

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

2014-11-15

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

Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State

USGS Publications Warehouse

Wilmoth, Siri K.; Irvine, Kathryn M.; Larson, Chad

2015-01-01

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

New strategy to identify radicals in a time evolving EPR data set by multivariate curve resolution-alternating least squares.

PubMed

Fadel, Maya Abou; de Juan, Anna; Vezin, Hervé; Duponchel, Ludovic

2016-12-01

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra. Copyright © 2016 Elsevier B.V. All rights reserved.

Ecological relevance of current water quality assessment unit designations in impaired rivers

USGS Publications Warehouse

Layhee, Megan J.; Sepulveda, Adam; Ray, Andrew; Mladenka, Greg; Van Every, Lynn

2016-01-01

Managers often nest sections of water bodies together into assessment units (AUs) to monitor and assess water quality criteria. Ideally, AUs represent an extent of waters with similar ecological, watershed, habitat and land-use conditions and no overlapping characteristics with other waters. In the United States, AUs are typically based on political or hydrologic boundaries rather than on ecologically relevant features, so it can be difficult to detect changes in impairment status. Our goals were to evaluate if current AU designation criteria of an impaired water body in southeastern Idaho, USA that, like many U.S. waters, has three-quarters of its mainstem length divided into two AUs. We focused our evaluation in southeastern Idaho's Portneuf River, an impaired river and three-quarters of the river is divided into two AUs. We described biological and environmental conditions at multiple reaches within each AU. We used these data to (1) test if variability at the reach-scale is greater within or among AUs and, (2) to evaluate alternate AU boundaries based on multivariate analyses of reach-scale data. We found that some biological conditions had greater variability within an AU than between AUs. Multivariate analyses identified alternative, 2- and 3-group, AUs that reduced this variability. Our results suggest that the current AU designations in the mainstem Portneuf River contain ecologically distinct sections of river and that the existing AU boundaries should be reconsidered in light of the ecological conditions measured at the reach scale. Variation in biological integrity within designated AUs may complicate water quality and biological assessments, influence management decisions or affect where monitoring or mitigation resources are directed.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

Coble, Jamie; Orton, Christopher; Schwantes, Jon

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

Monitoring gas-phase CO2 in the headspace of champagne glasses through combined diode laser spectrometry and micro-gas chromatography analysis.

PubMed

Moriaux, Anne-Laure; Vallon, Raphaël; Parvitte, Bertrand; Zeninari, Virginie; Liger-Belair, Gérard; Cilindre, Clara

2018-10-30

During Champagne or sparkling wine tasting, gas-phase CO 2 and volatile organic compounds invade the headspace above glasses, thus progressively modifying the chemical space perceived by the consumer. Gas-phase CO 2 in excess can even cause a very unpleasant tingling sensation perturbing both ortho- and retronasal olfactory perception. Monitoring as accurately as possible the level of gas-phase CO 2 above glasses is therefore a challenge of importance aimed at better understanding the close relationship between the release of CO 2 and a collection of various tasting parameters. Here, the concentration of CO 2 found in the headspace of champagne glasses served under multivariate conditions was accurately monitored, all along the 10 min following pouring, through a new combined approach by a CO 2 -Diode Laser Sensor and micro-gas chromatography. Our results show the strong impact of various tasting conditions (volume dispensed, intensity of effervescence, and glass shape) on the release of gas-phase CO 2 above the champagne surface. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quality of life in relation to upper and lower respiratory conditions among retired 9/11-exposed firefighters with pulmonary disability.

PubMed

Berninger, Amy; Webber, Mayris P; Weakley, Jessica; Gustave, Jackson; Zeig-Owens, Rachel; Lee, Roy; Al-Othman, Fairouz; Cohen, Hillel W; Kelly, Kerry; Prezant, David J

2010-12-01

To examine health-related quality of life (HRQoL) and World Trade Center (WTC) cough syndrome conditions in male firefighters who retired due to a 9/11-related pulmonary disability. From 3/1/2008 to 1/31/2009, we contacted 275 disability-retired firefighters and compared their HRQoL and current aerodigestive conditions to those from WTC-exposed non-disabled retired and active firefighters. Relationships between HRQoL and explanatory variable(s) were examined using multivariable linear regression models. Mean physical component summary (PCS) scores were lowest in disabled retirees compared with non-disabled retirees and actives: 36.4 (9.6), 49.4 (8.7), and 53.1 (5.1), respectively (P < 0.0001). Mean mental component summary (MCS) scores were closer: 44.5 (11.9), 48.1 (8.5), and 48.7 (7.4), respectively (P < 0.0001). In multivariable models, after adjustment for many factors, PCS scores were not associated with early WTC arrival, but were inversely associated with disability retirement and all WTC cough syndrome conditions. MCS scores were inversely associated with early WTC arrival and most WTC cough syndrome conditions, but were not associated with disability retirement. WTC cough syndrome conditions predict lower HRQoL scores even 8 years after exposure, independent of retirement status. These data suggest that monitoring physical conditions of individuals with occupational exposures might help identify those at risk for impaired HRQoL.

Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

PubMed

Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

2015-06-01

Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil.

PubMed

Rauscher, Markus S; Tremmel, Anton J; Schardt, Michael; Koch, Alexander W

2017-02-18

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil

PubMed Central

Rauscher, Markus S.; Tremmel, Anton J.; Schardt, Michael; Koch, Alexander W.

2017-01-01

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit. PMID:28218701

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

1H NMR-based metabolic profiling for evaluating poppy seed rancidity and brewing.

PubMed

Jawień, Ewa; Ząbek, Adam; Deja, Stanisław; Łukaszewicz, Marcin; Młynarz, Piotr

2015-12-01

Poppy seeds are widely used in household and commercial confectionery. The aim of this study was to demonstrate the application of metabolic profiling for industrial monitoring of the molecular changes which occur during minced poppy seed rancidity and brewing processes performed on raw seeds. Both forms of poppy seeds were obtained from a confectionery company. Proton nuclear magnetic resonance (1H NMR) was applied as the analytical method of choice together with multivariate statistical data analysis. Metabolic fingerprinting was applied as a bioprocess control tool to monitor rancidity with the trajectory of change and brewing progressions. Low molecular weight compounds were found to be statistically significant biomarkers of these bioprocesses. Changes in concentrations of chemical compounds were explained relative to the biochemical processes and external conditions. The obtained results provide valuable and comprehensive information to gain a better understanding of the biology of rancidity and brewing processes, while demonstrating the potential for applying NMR spectroscopy combined with multivariate data analysis tools for quality control in food industries involved in the processing of oilseeds. This precious and versatile information gives a better understanding of the biology of these processes.

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

PubMed

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

2015-01-01

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

Coupling GIS and multivariate approaches to reference site selection for wadeable stream monitoring.

PubMed

Collier, Kevin J; Haigh, Andy; Kelly, Johlene

2007-04-01

Geographic Information System (GIS) was used to identify potential reference sites for wadeable stream monitoring, and multivariate analyses were applied to test whether invertebrate communities reflected a priori spatial and stream type classifications. We identified potential reference sites in segments with unmodified vegetation cover adjacent to the stream and in >85% of the upstream catchment. We then used various landcover, amenity and environmental impact databases to eliminate sites that had potential anthropogenic influences upstream and that fell into a range of access classes. Each site identified by this process was coded by four dominant stream classes and seven zones, and 119 candidate sites were randomly selected for follow-up assessment. This process yielded 16 sites conforming to reference site criteria using a conditional-probabilistic design, and these were augmented by an additional 14 existing or special interest reference sites. Non-metric multidimensional scaling (NMS) analysis of percent abundance invertebrate data indicated significant differences in community composition among some of the zones and stream classes identified a priori providing qualified support for this framework in reference site selection. NMS analysis of a range standardised condition and diversity metrics derived from the invertebrate data indicated a core set of 26 closely related sites, and four outliers that were considered atypical of reference site conditions and subsequently dropped from the network. Use of GIS linked to stream typology, available spatial databases and aerial photography greatly enhanced the objectivity and efficiency of reference site selection. The multi-metric ordination approach reduced variability among stream types and bias associated with non-random site selection, and provided an effective way to identify representative reference sites.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

PubMed

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-05-15

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

Experts' perceptions on the entrepreneurial framework conditions

NASA Astrophysics Data System (ADS)

Correia, Aldina; e Silva, Eliana Costa; Lopes, I. Cristina; Braga, Alexandra; Braga, Vitor

2017-11-01

The Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship. This database includes information of more than 100 countries concerning several aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, in two main sources of primary data: the Adult Population Survey and the National Expert Survey. In the present work the National Expert Survey datasets for 2011, 2012 and 2013 are analyzed with the purpose of studying the effects of different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions (EFCs). The results of the multivariate analysis of variance for the 2013 data show significant differences of the entrepreneurship experts when compared the 2011 and 2012 surveys. For the 2013 data entrepreneur experts are less favorable then most of the other experts to the EFCs.

Multivariate thermo-hygrometric characterisation of the archaeological site of Plaza de l'Almoina (Valencia, Spain) for preventive conservation.

PubMed

Fernández-Navajas, Angel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-07-29

Preventive conservation requires monitoring and control of the parameters involved in the deterioration process, mainly temperature and relative humidity. It is important to characterise an archaeological site prior to carrying out comparative studies in the future for preventive conservation, either by regular studies to verify whether the conditions are constant, or occasional ones when the boundary conditions are altered. There are numerous covered archaeological sites, but few preventive conservation works that give special attention to the type of cover installed. In particular, there is no background of microclimatic studies in sites that are in the ground and, as in the Plaza de l'Almoina (Valencia, Spain), are buried and partially covered by a transparent roof. A large effect of the transparent cover was found by the sensors located below this area, with substantial increases in temperature and a decrease in the relative humidity during the day. Surrounding zones also have values above the recommended temperature values. On the other hand, the influence of a buried water drainage line near the site is notable, causing an increase in relative humidity levels in the surrounding areas. Multivariate statistical analyses enabled us to characterise the microclimate of the archaeological site, allowing future testing to determine whether the conservation conditions have been altered.

Multivariate Thermo-Hygrometric Characterisation of the Archaeological Site of Plaza de l’Almoina (Valencia, Spain) for Preventive Conservation

PubMed Central

Fernández-Navajas, Ángel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-01-01

Preventive conservation requires monitoring and control of the parameters involved in the deterioration process, mainly temperature and relative humidity. It is important to characterise an archaeological site prior to carrying out comparative studies in the future for preventive conservation, either by regular studies to verify whether the conditions are constant, or occasional ones when the boundary conditions are altered. There are numerous covered archaeological sites, but few preventive conservation works that give special attention to the type of cover installed. In particular, there is no background of microclimatic studies in sites that are in the ground and, as in the Plaza de l’Almoina (Valencia, Spain), are buried and partially covered by a transparent roof. A large effect of the transparent cover was found by the sensors located below this area, with substantial increases in temperature and a decrease in the relative humidity during the day. Surrounding zones also have values above the recommended temperature values. On the other hand, the influence of a buried water drainage line near the site is notable, causing an increase in relative humidity levels in the surrounding areas. Multivariate statistical analyses enabled us to characterise the microclimate of the archaeological site, allowing future testing to determine whether the conservation conditions have been altered. PMID:23899937

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

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

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

MANOVA for distinguishing experts' perceptions about entrepreneurship using NES data from GEM

NASA Astrophysics Data System (ADS)

Correia, Aldina; Costa e Silva, Eliana; Lopes, Isabel C.; Braga, Alexandra

2016-12-01

Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship that includes information about many aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, of a large number of countries. This project has two main sources of primary data: the Adult Population Survey and the National Expert Survey. In this work the 2011 and 2012 National Expert Survey datasets are studied. Our goal is to analyze the effects of the different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions. For this purpose the multivariate analysis of variance is used. Some similarities between the results obtained for the 2011 and 2012 datasets were found, however the differences between experts still exist.

Chronic Conditions Among Children Investigated by Child Welfare: A National Sample

PubMed Central

Hurlburt, Michael S.; Heneghan, Amy M.; Zhang, Jinjin; Rolls-Reutz, Jennifer; Silver, Ellen J.; Fisher, Emily; Landsverk, John; Horwitz, Sarah McCue

2013-01-01

OBJECTIVE: To assess the presence of chronic health conditions (CHCs) among a nationally representative sample of children investigated by child welfare agencies. METHODS: The study included 5872 children, aged 0 to 17.5 years, whose families were investigated for maltreatment between February 2008 and April 2009. Using data from the second National Survey of Child and Adolescent Well-Being, we examined the proportion of children who had CHC. We developed 2 categorical and 2 noncategorical measures of CHC from the available data and analyzed them by using bivariate and multivariable analyses. RESULTS: Depending on the measure used, 30.6% to 49.0% of all children investigated were reported by their caregivers to have a CHC. Furthermore, the children identified by using diverse methods were not entirely overlapping. In the multivariable analyses, children with poorer health were more likely to be male, older, and receiving special educational services but not more likely to be in out-of-home placements. CONCLUSIONS: The finding that a much higher proportion of these children have CHC than in the general population underscores the substantial health problems of children investigated by child welfare agencies and the need to monitor their health carefully, regardless of their placement postinvestigation. PMID:23420907

Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.

PubMed

Rathore, Anurag S; Pathak, Mili; Jain, Renu; Jadaun, Gaurav Pratap Singh

2016-07-01

Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts.

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet.

PubMed

Potyrailo, Radislav A

2016-10-12

Modern gas monitoring scenarios for medical diagnostics, environmental surveillance, industrial safety, and other applications demand new sensing capabilities. This Review provides analysis of development of new generation of gas sensors based on the multivariable response principles. Design criteria of these individual sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with independent outputs to recognize these different gas responses. These new sensors quantify individual components in mixtures, reject interferences, and offer more stable response over sensor arrays. Such performance is attractive when selectivity advantages of classic gas chromatography, ion mobility, and mass spectrometry instruments are canceled by requirements for no consumables, low power, low cost, and unobtrusive form factors for Internet of Things, Industrial Internet, and other applications. This Review is concluded with a perspective for future needs in fundamental and applied aspects of gas sensing and with the 2025 roadmap for ubiquitous gas monitoring.

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

An improvement of drought monitoring through the use of a multivariate magnitude index

NASA Astrophysics Data System (ADS)

Real-Rangel, R. A.; Alcocer-Yamanaka, V. H.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.; Ocón-Gutiérrez, A. R.

2017-12-01

In drought monitoring activities it is widely acknowledged that the severity of an event is determined in relation to monthly values of univariate indices of one or more hydrological variables. Normally, these indices are estimated using temporal windows from 1 to 12 months or more to aggregate the effects of deficits in the variable of interest. However, the use of these temporal windows may lead to a misperception of both, the drought event intensity and the timing of its occurrence. In this context, this work presents the implementation of a trivariate drought magnitude index, considering key hydrological variables (e.g., precipitation, soil moisture and runoff) using for this the framework of the Multivariate Standardized Drought Index (MSDI). Despite the popularity and simplicity of the concept of drought magnitude for standardized drought indices, its implementation in drought monitoring and early warning systems has not been reported. This approach has been tested for operational purposes in the recently launched Multivariate Drought Monitor of Mexico (MOSEMM) and the results shows that the inclusion of a Magnitude index facilitates the drought detection and, thus, improves the decision making process for emergency managers.

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

PubMed

Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

2016-10-01

Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

PubMed

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Environmental, political, and economic determinants of water quality monitoring in Europe

NASA Astrophysics Data System (ADS)

Beck, Lucas; Bernauer, Thomas; Kalbhenn, Anna

2010-11-01

Effective monitoring is essential for effective pollution control in national and international water systems. To what extent are countries' monitoring choices driven by environmental criteria, as they should be? And to what extent are they also influenced by other factors, such as political and economic conditions? To address these questions, we describe and explain the evolution of one of the most important international environmental monitoring networks in Europe, the one for water quality, in the time period 1965-2004. We develop a geographic information system that contains information on the location of several thousand active monitoring stations in Europe. Using multivariate statistics, we then examine whether and to what extent the spatial and temporal clustering of monitoring intensity is driven by environmental, political, and economic factors. The results show that monitoring intensity is higher in river basins exposed to greater environmental pressure. However, political and economic factors also play a strong role in monitoring decisions: democracy, income, and peer pressure are conducive to monitoring intensity, and monitoring intensity generally increases over time. Moreover, even though monitoring is more intense in international upstream-downstream settings, we observe only a weak bias toward more monitoring downstream of international borders. In contrast, negative effects of European Union (EU) membership and runup to the EU's Water Framework Directive are potential reasons for concern. Our results strongly suggest that international coordination and standardization of water quality monitoring should be intensified. It will be interesting to apply our analytical approach also to other national and international monitoring networks, for instance, the U.S. National Water-Quality Assessment Program or the European Monitoring and Evaluation Program for air pollution.

The effect of process parameters on audible acoustic emissions from high-shear granulation.

PubMed

Hansuld, Erin M; Briens, Lauren; Sayani, Amyn; McCann, Joe A B

2013-02-01

Product quality in high-shear granulation is easily compromised by minor changes in raw material properties or process conditions. It is desired to develop a process analytical technology (PAT) that can monitor the process in real-time and provide feedback for quality control. In this work, the application of audible acoustic emissions (AAEs) as a PAT tool was investigated. A condenser microphone was placed at the top of the air exhaust on a PMA-10 high-shear granulator to collect AAEs for a design of experiment (DOE) varying impeller speed, total binder volume and spray rate. The results showed the 10 Hz total power spectral densities (TPSDs) between 20 and 250 Hz were significantly affected by the changes in process conditions. Impeller speed and spray rate were shown to have statistically significant effects on granulation wetting, and impeller speed and total binder volume were significant in terms of process end-point. The DOE results were confirmed by a multivariate PLS model of the TPSDs. The scores plot showed separation based on impeller speed in the first component and spray rate in the second component. The findings support the use of AAEs to monitor changes in process conditions in real-time and achieve consistent product quality.

Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

PubMed

Yotova, Galina I; Tsitouridou, Roxani; Tsakovski, Stefan L; Simeonov, Vasil D

2016-01-01

The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 μm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified. A specific element of the study is the effort to clarify the role of outliers in the data set. The reason for the appearance of outliers is strongly related to the atmospheric condition on the particular sampling days leading to enhanced concentration of pollutants (secondary emissions, sea sprays, road and soil dust, combustion processes) especially for ultra fine and coarse particles. It is also shown that three major sources affect the urban air quality of the location studied-sea sprays, mineral dust and anthropogenic influences (agricultural activity, combustion processes, and industrial sources). The level of impact is related to certain extent to the aerosol fraction size. The assessment of the meteorological conditions leads to defining of four downwind patterns affecting the air quality (Pelagic, Western and Central Europe, Eastern and Northeastern Europe and Africa and Southern Europe). Thus, the present study offers a complete urban air assessment taking into account the weather conditions, pollution sources and aerosol fractioning.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

In-line monitoring of cocrystallization process and quantification of carbamazepine-nicotinamide cocrystal using Raman spectroscopy and chemometric tools.

PubMed

Soares, Frederico L F; Carneiro, Renato L

2017-06-05

A cocrystallization process may involve several molecular species, which are generally solid under ambient conditions. Thus, accurate monitoring of different components that might appear during the reaction is necessary, as well as quantification of the final product. This work reports for the first time the synthesis of carbamazepine-nicotinamide cocrystal in aqueous media with a full conversion. The reactions were monitored by Raman spectroscopy coupled with Multivariate Curve Resolution - Alternating Least Squares, and the quantification of the final product among its coformers was performed using Raman spectroscopy and Partial Least Squares regression. The slurry reaction was made in four different conditions: room temperature, 40°C, 60°C and 80°C. The slurry reaction at 80°C enabled a full conversion of initial substrates into the cocrystal form, using water as solvent for a greener method. The employment of MCR-ALS coupled with Raman spectroscopy enabled to observe the main steps of the reactions, such as drug dissolution, nucleation and crystallization of the cocrystal. The PLS models gave mean errors of cross validation around 2.0 (% wt/wt), and errors of validation between 2.5 and 8.2 (% wt/wt) for all components. These were good results since the spectra of cocrystals and the physical mixture of the coformers present some similar peaks. Copyright © 2017 Elsevier B.V. All rights reserved.

In-line monitoring of cocrystallization process and quantification of carbamazepine-nicotinamide cocrystal using Raman spectroscopy and chemometric tools

NASA Astrophysics Data System (ADS)

Soares, Frederico L. F.; Carneiro, Renato L.

2017-06-01

A cocrystallization process may involve several molecular species, which are generally solid under ambient conditions. Thus, accurate monitoring of different components that might appear during the reaction is necessary, as well as quantification of the final product. This work reports for the first time the synthesis of carbamazepine-nicotinamide cocrystal in aqueous media with a full conversion. The reactions were monitored by Raman spectroscopy coupled with Multivariate Curve Resolution - Alternating Least Squares, and the quantification of the final product among its coformers was performed using Raman spectroscopy and Partial Least Squares regression. The slurry reaction was made in four different conditions: room temperature, 40 °C, 60 °C and 80 °C. The slurry reaction at 80 °C enabled a full conversion of initial substrates into the cocrystal form, using water as solvent for a greener method. The employment of MCR-ALS coupled with Raman spectroscopy enabled to observe the main steps of the reactions, such as drug dissolution, nucleation and crystallization of the cocrystal. The PLS models gave mean errors of cross validation around 2.0 (% wt/wt), and errors of validation between 2.5 and 8.2 (% wt/wt) for all components. These were good results since the spectra of cocrystals and the physical mixture of the coformers present some similar peaks.

Clinical outcome of continuous facial nerve monitoring during primary parotidectomy.

PubMed

Terrell, J E; Kileny, P R; Yian, C; Esclamado, R M; Bradford, C R; Pillsbury, M S; Wolf, G T

1997-10-01

To assess whether continuous facial nerve monitoring during parotidectomy is associated with a lower incidence of facial nerve paresis or paralysis compared with parotidectomy without monitoring and to assess the cost of such monitoring. A retrospective analysis of outcomes for patients who underwent parotidectomy with or without continuous facial nerve monitoring. University medical center. Fifty-six patients undergoing parotidectomy in whom continuous electromyographic monitoring was used and 61 patients in whom it was not used. (1) The incidence of early and persistent facial nerve paresis or paralysis and (2) the cost associated with facial nerve monitoring. Early, unintentional facial weakness was significantly lower in the group monitored by electromyograpy (43.6%) than in the unmonitored group (62.3%) (P=.04). In the subgroup of patients without comorbid conditions or surgeries, early weakness in the monitored group (33.3%) remained statistically lower than the rate of early weakness in the unmonitored group (57.5%) (P=.03). There was no statistical difference in the final facial nerve function or incidence of permanent nerve injury between the groups or subgroups. After multivariate analysis, nonmonitored status (odds ratio [OR], 3.22), advancing age (OR, 1.47 per 10 years), and longer operative times (OR, 1.3 per hour) were the only significant independent predictive variables significantly associated with early postoperative facial weakness. The incremental cost of facial nerve monitoring was $379. The results suggest that continuous electromyographic monitoring of facial muscle during primary parotidectomy reduces the incidence of short-term postoperative facial paresis. Advantages and disadvantages of this technique need to be considered together with the additional costs in deciding whether routine use of continuous monitoring is a useful, cost-effective adjunct to parotid surgery.

Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques.

PubMed

Nnane, Daniel Ekane

2011-11-15

Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish 'sentry' sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment. Copyright © 2011 Elsevier B.V. All rights reserved.

Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

PubMed

Lin, Deborah S; Taylor, Peter; Tibbett, Mark

2014-10-01

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Multivariate Analysis To Quantify Species in the Presence of Direct Interferents: Micro-Raman Analysis of HNO 3 in Microfluidic Devices

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

Lines, Amanda M.; Nelson, Gilbert L.; Casella, Amanda J.

Microfluidic devices are a growing field with significant potential for application to small scale processing of solutions. Much like large scale processing, fast, reliable, and cost effective means of monitoring the streams during processing are needed. Here we apply a novel Micro-Raman probe to the on-line monitoring of streams within a microfluidic device. For either macro or micro scale process monitoring via spectroscopic response, there is the danger of interfering or confounded bands obfuscating results. By utilizing chemometric analysis, a form of multivariate analysis, species can be accurately quantified in solution despite the presence of overlapping or confounded spectroscopic bands.more » This is demonstrated on solutions of HNO 3 and NaNO 3 within micro-flow and microfluidic devices.« less

An extended multivariate framework for drought monitoring in Mexico

NASA Astrophysics Data System (ADS)

Real-Rangel, Roberto; Pedrozo-Acuña, Adrián; Breña-Naranjo, Agustín; Alcocer-Yamanaka, Víctor

2017-04-01

Around the world, monitoring natural hazards, such as droughts, represents a critical task in risk assessment and management plans. A reliable drought monitoring system allows to identify regions affected by these phenomena so that early response measures can be implemented. In Mexico, this activity is performed using Mexico's Drought Monitor, which is based on a similar methodology as the United States Drought Monitor and the North American Drought Monitor. The main feature of these monitoring systems is the combination of ground-based and remote sensing observations that is ultimately validated by local experts. However, in Mexico in situ records of variables such as precipitation and streamflow are often scarce, or even null, in many regions of the country. Another issue that adds uncertainty in drought monitoring is the arbitrary weight given to each analyzed variable. This study aims at providing an operational framework for drought monitoring in Mexico, based on univariate and multivariate nonparametric standardized indexes proposed in recent studies. Furthermore, the framework has been extended by taking into account the Enhanced Vegetation Index (EVI) for the drought severity assessment. The analyzed variables used for computing the drought indexes are mainly derived from remote sensing (MODIS) and land surface models datasets (NASA MERRA-2). A qualitative evaluation of the results shows that the indexes used are capable of adequately describes the intensity and spatial distribution of past drought documented events.

FT-IR/ATR univariate and multivariate calibration models for in situ monitoring of sugars in complex microalgal culture media.

PubMed

Girard, Jean-Michel; Deschênes, Jean-Sébastien; Tremblay, Réjean; Gagnon, Jonathan

2013-09-01

The objective of this work is to develop a quick and simple method for the in situ monitoring of sugars in biological cultures. A new technology based on Attenuated Total Reflectance-Fourier Transform Infrared (FT-IR/ATR) spectroscopy in combination with an external light guiding fiber probe was tested, first to build predictive models from solutions of pure sugars, and secondly to use those models to monitor the sugars in the complex culture medium of mixotrophic microalgae. Quantification results from the univariate model were correlated with the total dissolved solids content (R(2)=0.74). A vector normalized multivariate model was used to proportionally quantify the different sugars present in the complex culture medium and showed a predictive accuracy of >90% for sugars representing >20% of the total. This method offers an alternative to conventional sugar monitoring assays and could be used at-line or on-line in commercial scale production systems. Copyright © 2013 Elsevier Ltd. All rights reserved.

Measured and perceived environmental comfort: field monitoring in an Italian school.

PubMed

De Giuli, Valeria; Zecchin, Roberto; Corain, Livio; Salmaso, Luigi

2014-07-01

Microclimatic conditions were recorded in an Italian school and Fanger's indexes PMV and PPD were calculated under different conditions. Students' sensations were investigated four times by means of two surveys, one related to actual microclimatic conditions and one on overall satisfaction, interaction occupant-building and reactions to discomfort. Pupils' classroom position was considered to look for possible influence on thermal comfort: a difference emerged from PMV and the survey, but the results obtained from the two approaches differ for both the entity of discomfort and its distribution within each classroom. Innovative multivariate nonparametric statistical techniques were applied to compare and rank the classrooms in accordance with students' subjective perceptions; a global ranking has been also calculated, considering thermal and visual comfort and air quality. Comparing pupil-sensation-based ranking with environmental parameters no clear correspondence was found, except for mid-season, where PMV, CO2 concentration and desk illuminance were similar in all the classrooms. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Impact of ACA Health Reforms for People With Mental Health Conditions.

PubMed

Thomas, Kathleen C; Shartzer, Adele; Kurth, Noelle K; Hall, Jean P

2018-02-01

This brief report explores the impact of health reform for people with mental illness. The Health Reform Monitoring Survey was used to examine health insurance, access to care, and employment for 1,550 people with mental health conditions pre- and postimplementation of the Affordable Care Act (ACA) and by state Medicaid expansion status. Multivariate logistic regressions with predictive margins were used. Post-ACA reforms, people with mental health conditions were less likely to be uninsured (5% versus 13%; t=-6.89, df=50, p<.001) and to report unmet need due to cost of mental health care (17% versus 21%; t=-3.16, df=50, p=.002) and any health services (46% versus 51%; t=-3.71, df=50, p<.001), and they were more likely to report a usual source of care (82% versus 76%; t=3.11, df=50, p=.002). These effects were experienced in both Medicaid expansion and nonexpansion states. Findings underscore the importance of ACA improvements in the quality of health insurance coverage.

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

PubMed Central

Levman, Jacob; Takahashi, Emi

2016-01-01

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

Trial by fire: a multivariate examination of the relation between job tenure and work injuries.

PubMed

Breslin, F C; Smith, P

2006-01-01

This study examined the relation between months on the job and lost-time claim rates, with a particular focus on age related differences. Workers' compensation records and labour force survey data were used to compute claim rates per 1000 full time equivalents. To adjust for potential confounding, multivariate analyses included age, sex, occupation, and industry, as well job tenure as predictors of claim rates. At any age, the claim rates decline as time on the job increases. For example, workers in the first month on the job were over four times more likely to have a lost-time claim than workers with over one year in their current job. The job tenure injury associations were stronger among males, the goods industry, manual occupations, and older adult workers. The present results suggest that all worker subgroups examined show increased risk when new on the job. Recommendations for improving this situation include earlier training, starting workers in low hazard conditions, reducing job turnover rates in firms, and improved monitoring of hazard exposures that new workers encounter.

A Computer Interview for Multivariate Monitoring of Psychiatric Outcome.

ERIC Educational Resources Information Center

Stevenson, John F.; And Others

Application of computer technology to psychiatric outcome measurement offers the promise of coping with increasing demands for extensive patient interviews repeated longitudinally. Described is the development of a cost-effective multi-dimensional tracking device to monitor psychiatric functioning, building on a previous local computer interview…

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

PubMed

Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

2018-02-09

The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Study of jojoba oil aging by FTIR.

PubMed

Le Dréau, Y; Dupuy, N; Gaydou, V; Joachim, J; Kister, J

2009-05-29

As the jojoba oil was used in cosmetic, pharmaceutical, dietetic food, animal feeding, lubrication, polishing and bio-diesel fields, it was important to study its aging at high temperature by oxidative process. In this work a FT-MIR methodology was developed for monitoring accelerate oxidative degradation of jojoba oils. Principal component analysis (PCA) was used to differentiate various samples according to their origin and obtaining process, and to differentiate oxidative conditions applied on oils. Two spectroscopic indices were calculated to report simply the oxidation phenomenon. Results were confirmed and deepened by multivariate curve resolution-alternative least square method (MCR-ALS). It allowed identifying chemical species produced or degraded during the thermal treatment according to a SIMPLISMA pretreatment.

Kidney function monitoring and nonvitamin K oral anticoagulant dosage in atrial fibrillation.

PubMed

Andreu Cayuelas, Jose Manuel; Caro Martínez, Cesar; Flores Blanco, Pedro Jose; Elvira Ruiz, Gines; Albendin Iglesias, Helena; Cerezo Manchado, Juan Jose; Bailen Lorenzo, Jose Luis; Januzzi, James L; García Alberola, Arcadio; Manzano-Fernández, Sergio

2018-06-01

Clinical practice guidelines recommend regular kidney function monitoring in atrial fibrillation patients on nonvitamin K oral anticoagulants (NOAC); however, information regarding compliance with these recommendations in daily life conditions is scarce. We sought to determine the compliance with kidney function monitoring recommendations in nonvalvular atrial fibrillation (NVAF) patients starting NOAC and its implication on the appropriateness of NOAC dosage. This study involves the retrospective analysis of a multicentre registry including consecutive NVAF patients who started NOAC (n = 692). Drug dosage changes and serum creatinine determinations were recorded during 1-year follow-up. European Heart Rhythm Association criteria were used to define the appropriateness of kidney function monitoring as well as adequate NOAC dosage. During the follow-up (334 ± 89 days), the compliance with kidney function monitoring recommendations was 61% (n = 425). After multivariate adjustment, age (OR × year: 0.92 (CI 95%: 0.89-0.95) P < .001), creatinine clearance (OR × mL/min: 1.02 (CI 95%: 1.01-1.03) P < .001) and adequate NOAC dosage at baseline (OR: 1.54 (CI 95%: 1.06-2.23), P = .024) were independent predictors of appropriate kidney function monitoring. Compliance with kidney function monitoring recommendations was independently associated with change to appropriate NOAC dose after 1 year (OR: 2.80 (CI 95%: 1.01-7.80), P = .049). Noncompliance with kidney function monitoring recommendations is common in NVAF patients starting NOAC, especially in elderly patients with kidney dysfunction. Compliance with kidney function monitoring recommendations was associated with adequate NOAC dosage at 1-year follow-up. Further studies are warranted to evaluate the implication of kidney function monitoring on prognosis. © 2018 Stichting European Society for Clinical Investigation Journal Foundation.

Heart Rate Monitoring in Team Sports-A Conceptual Framework for Contextualizing Heart Rate Measures for Training and Recovery Prescription.

PubMed

Schneider, Christoph; Hanakam, Florian; Wiewelhove, Thimo; Döweling, Alexander; Kellmann, Michael; Meyer, Tim; Pfeiffer, Mark; Ferrauti, Alexander

2018-01-01

A comprehensive monitoring of fitness, fatigue, and performance is crucial for understanding an athlete's individual responses to training to optimize the scheduling of training and recovery strategies. Resting and exercise-related heart rate measures have received growing interest in recent decades and are considered potentially useful within multivariate response monitoring, as they provide non-invasive and time-efficient insights into the status of the autonomic nervous system (ANS) and aerobic fitness. In team sports, the practical implementation of athlete monitoring systems poses a particular challenge due to the complex and multidimensional structure of game demands and player and team performance, as well as logistic reasons, such as the typically large number of players and busy training and competition schedules. In this regard, exercise-related heart rate measures are likely the most applicable markers, as they can be routinely assessed during warm-ups using short (3-5 min) submaximal exercise protocols for an entire squad with common chest strap-based team monitoring devices. However, a comprehensive and meaningful monitoring of the training process requires the accurate separation of various types of responses, such as strain, recovery, and adaptation, which may all affect heart rate measures. Therefore, additional information on the training context (such as the training phase, training load, and intensity distribution) combined with multivariate analysis, which includes markers of (perceived) wellness and fatigue, should be considered when interpreting changes in heart rate indices. The aim of this article is to outline current limitations of heart rate monitoring, discuss methodological considerations of univariate and multivariate approaches, illustrate the influence of different analytical concepts on assessing meaningful changes in heart rate responses, and provide case examples for contextualizing heart rate measures using simple heuristics. To overcome current knowledge deficits and methodological inconsistencies, future investigations should systematically evaluate the validity and usefulness of the various approaches available to guide and improve the implementation of decision-support systems in (team) sports practice.

Heart Rate Monitoring in Team Sports—A Conceptual Framework for Contextualizing Heart Rate Measures for Training and Recovery Prescription

PubMed Central

Schneider, Christoph; Hanakam, Florian; Wiewelhove, Thimo; Döweling, Alexander; Kellmann, Michael; Meyer, Tim; Pfeiffer, Mark; Ferrauti, Alexander

2018-01-01

A comprehensive monitoring of fitness, fatigue, and performance is crucial for understanding an athlete's individual responses to training to optimize the scheduling of training and recovery strategies. Resting and exercise-related heart rate measures have received growing interest in recent decades and are considered potentially useful within multivariate response monitoring, as they provide non-invasive and time-efficient insights into the status of the autonomic nervous system (ANS) and aerobic fitness. In team sports, the practical implementation of athlete monitoring systems poses a particular challenge due to the complex and multidimensional structure of game demands and player and team performance, as well as logistic reasons, such as the typically large number of players and busy training and competition schedules. In this regard, exercise-related heart rate measures are likely the most applicable markers, as they can be routinely assessed during warm-ups using short (3–5 min) submaximal exercise protocols for an entire squad with common chest strap-based team monitoring devices. However, a comprehensive and meaningful monitoring of the training process requires the accurate separation of various types of responses, such as strain, recovery, and adaptation, which may all affect heart rate measures. Therefore, additional information on the training context (such as the training phase, training load, and intensity distribution) combined with multivariate analysis, which includes markers of (perceived) wellness and fatigue, should be considered when interpreting changes in heart rate indices. The aim of this article is to outline current limitations of heart rate monitoring, discuss methodological considerations of univariate and multivariate approaches, illustrate the influence of different analytical concepts on assessing meaningful changes in heart rate responses, and provide case examples for contextualizing heart rate measures using simple heuristics. To overcome current knowledge deficits and methodological inconsistencies, future investigations should systematically evaluate the validity and usefulness of the various approaches available to guide and improve the implementation of decision-support systems in (team) sports practice. PMID:29904351

Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy.

PubMed

Igne, Benoît; de Juan, Anna; Jaumot, Joaquim; Lallemand, Jordane; Preys, Sébastien; Drennen, James K; Anderson, Carl A

2014-10-01

The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation. Copyright © 2014. Published by Elsevier B.V.

[Near infrared spectroscopy based process trajectory technology and its application in monitoring and controlling of traditional Chinese medicine manufacturing process].

PubMed

Li, Wen-Long; Qu, Hai-Bin

2016-10-01

In this paper, the principle of NIRS (near infrared spectroscopy)-based process trajectory technology was introduced.The main steps of the technique include:① in-line collection of the processes spectra of different technics; ② unfolding of the 3-D process spectra;③ determination of the process trajectories and their normal limits;④ monitoring of the new batches with the established MSPC (multivariate statistical process control) models.Applications of the technology in the chemical and biological medicines were reviewed briefly. By a comprehensive introduction of our feasibility research on the monitoring of traditional Chinese medicine technical process using NIRS-based multivariate process trajectories, several important problems of the practical applications which need urgent solutions are proposed, and also the application prospect of the NIRS-based process trajectory technology is fully discussed and put forward in the end. Copyright© by the Chinese Pharmaceutical Association.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Calibration of an electronic nose for poultry farm

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Shukor, S. A.; Kamis, M. S.; Shakaff, A. Y. M.; Zakaria, A.; Rahim, N. A.; Mamduh, S. M.; Kamarudin, K.; Saad, F. S. A.; Masnan, M. J.; Mustafa, H.

2017-03-01

Malodour from the poultry farms could cause air pollution and therefore potentially dangerous to humans' and animals' health. This issue also poses sustainability risk to the poultry industries due to objections from local community. The aim of this paper is to develop and calibrate a cost effective and efficient electronic nose for poultry farm air monitoring. The instrument main components include sensor chamber, array of specific sensors, microcontroller, signal conditioning circuits and wireless sensor networks. The instrument was calibrated to allow classification of different concentrations of main volatile compounds in the poultry farm malodour. The outcome of the process will also confirm the device's reliability prior to being used for poultry farm malodour assessment. The Multivariate Analysis (HCA and KNN) and Artificial Neural Network (ANN) pattern recognition technique was used to process the acquired data. The results show that the instrument is able to calibrate the samples using ANN classification model with high accuracy. The finding verifies the instrument's performance to be used as an effective poultry farm malodour monitoring.

Transient multivariable sensor evaluation

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

Vilim, Richard B.; Heifetz, Alexander

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

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.

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…

Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors

PubMed Central

van den Berg, Frans; Racher, Andrew J.; Martin, Elaine B.; Jaques, Colin

2017-01-01

Cell culture process development requires the screening of large numbers of cell lines and process conditions. The development of miniature bioreactor systems has increased the throughput of such studies; however, there are limitations with their use. One important constraint is the limited number of offline samples that can be taken compared to those taken for monitoring cultures in large‐scale bioreactors. The small volume of miniature bioreactor cultures (15 mL) is incompatible with the large sample volume (600 µL) required for bioanalysers routinely used. Spectroscopy technologies may be used to resolve this limitation. The purpose of this study was to compare the use of NIR, Raman, and 2D‐fluorescence to measure multiple analytes simultaneously in volumes suitable for daily monitoring of a miniature bioreactor system. A novel design‐of‐experiment approach is described that utilizes previously analyzed cell culture supernatant to assess metabolite concentrations under various conditions while providing optimal coverage of the desired design space. Multivariate data analysis techniques were used to develop predictive models. Model performance was compared to determine which technology is more suitable for this application. 2D‐fluorescence could more accurately measure ammonium concentration (RMSECV 0.031 g L−1) than Raman and NIR. Raman spectroscopy, however, was more robust at measuring lactate and glucose concentrations (RMSECV 1.11 and 0.92 g L−1, respectively) than the other two techniques. The findings suggest that Raman spectroscopy is more suited for this application than NIR and 2D‐fluorescence. The implementation of Raman spectroscopy increases at‐line measuring capabilities, enabling daily monitoring of key cell culture components within miniature bioreactor cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:337–346, 2017 PMID:28271638

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

A case study of real-time monitoring of solid-state phase transformations in acoustically levitated particles using near infrared and Raman spectroscopy.

PubMed

Rehder, Sönke; Wu, Jian X; Laackmann, Julian; Moritz, Hans-Ulrich; Rantanen, Jukka; Rades, Thomas; Leopold, Claudia S

2013-01-23

The objective of this study was to monitor the amorphous-to-crystalline solid-state phase transformation kinetics of the model drug ibuprofen with spectroscopic methods during acoustic levitation. Chemical and physical information was obtained by real-time near infrared (NIRS) and Raman spectroscopy measurements. The recrystallisation kinetic parameters (overall recrystallisation rate constant β and the time needed to reach 50% of the equilibrated level t(50)), were determined using a multivariate curve resolution approach. The acoustic levitation device coupled with non-invasive spectroscopy enabled monitoring of the recrystallisation process of the difficult-to-handle (adhesive) amorphous sample. The application of multivariate curve resolution enabled isolation of the underlying pure spectra, which corresponded well with the reference spectra of amorphous and crystalline ibuprofen. The recrystallisation kinetic parameters were estimated from the recrystallisation profiles. While the empirical recrystallisation rate constant determined by NIR and Raman spectroscopy were comparable, the lag time for recrystallisation was significantly lower with Raman spectroscopy as compared to NIRS. This observation was explained by the high energy density of the Raman laser beam, which might have led to local heating effects of the sample and thus reduced the recrystallisation onset time. It was concluded that acoustic levitation with NIR and Raman spectroscopy combined with multivariate curve resolution allowed direct determination of the recrystallisation kinetics of amorphous drugs and thus is a promising technique for monitoring solid-state phase transformations of adhesive small-sized samples during the early phase of drug development. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate Drought Characterization in India for Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sreekumaran Unnithan, P.; Mondal, A.

2016-12-01

Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

On the use of multi-agent systems for the monitoring of industrial systems

NASA Astrophysics Data System (ADS)

Rezki, Nafissa; Kazar, Okba; Mouss, Leila Hayet; Kahloul, Laid; Rezki, Djamil

2016-03-01

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Development of a real time monitor and multivariate method for long term diagnostics of atmospheric pressure dielectric barrier discharges: application to He, He/N2, and He/O2 discharges.

PubMed

O'Connor, N; Milosavljević, V; Daniels, S

2011-08-01

In this paper we present the development and application of a real time atmospheric pressure discharge monitoring diagnostic. The software based diagnostic is designed to extract latent electrical and optical information associated with the operation of an atmospheric pressure dielectric barrier discharge (APDBD) over long time scales. Given that little is known about long term temporal effects in such discharges, the diagnostic methodology is applied to the monitoring of an APDBD in helium and helium with both 0.1% nitrogen and 0.1% oxygen gas admixtures over periods of tens of minutes. Given the large datasets associated with the experiments, it is shown that this process is much expedited through the novel application of multivariate correlations between the electrical and optical parameters of the corresponding chemistries which, in turn, facilitates comparisons between each individual chemistry also. The results of these studies show that the electrical and optical parameters of the discharge in helium and upon the addition of gas admixtures evolve over time scales far longer than the gas residence time and have been compared to current modelling works. It is envisaged that the diagnostic together with the application of multivariate correlations will be applied to rapid system identification and prototyping in both experimental and industrial APDBD systems in the future.

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

NASA Astrophysics Data System (ADS)

Aghakouchak, Amir; Tourian, Mohammad J.

2015-04-01

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

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Application of a three-tier framework to assess ecological condition of Gulf of Mexico coastal wetlands.

PubMed

Nestlerode, Janet A; Hansen, Virginia D; Teague, Aarin; Harwell, Matthew C

2014-06-01

A multi-level coastal wetland assessment strategy was applied to wetlands in the northern Gulf of Mexico (GOM) to evaluate the feasibility of this approach for a broad national scale wetland condition assessment (US Environmental Protection Agency's National Wetlands Condition Assessment). Landscape-scale assessment indicators (tier 1) were developed and applied at the sub-watershed (12-digit hydrologic unit code (HUC)) level within the GOM coastal wetland sample frame with scores calculated using land-use maps and geographic information system. Rapid assessment protocols (tier 2), using a combination of data analysis and field work, evaluated metrics associated with landscape context, hydrology, physical structure, and biological structure. Intensive site monitoring (tier 3) included measures of soil chemistry and composition, water column and pore-water chemistry, and dominant macrophyte community composition and tissue chemistry. Relationships within and among assessment levels were evaluated using multivariate analyses with few significant correlations found. More detailed measures of hydrology, soils, and macrophyte species composition from sites across a known condition gradient, in conjunction with validation of standardized rapid assessment method, may be necessary to fully characterize coastal wetlands across the region.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

NASA Technical Reports Server (NTRS)

Smith, M. E.; Gevins, A.; Brown, H.; Karnik, A.; Du, R.

2001-01-01

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Does Parental Monitoring Moderate the Relation between Parent-Child Communication and Pre-Coital Sexual Behaviours among Urban, Minority Early Adolescents?

ERIC Educational Resources Information Center

Santa Maria, Diane; Markham, Christine; Swank, Paul; Baumler, Elizabeth; McCurdy, Sheryl; Tortolero, Susan

2014-01-01

This study examined parental monitoring (PM) as a potential moderator of the relation between parent-child communication (PCC) and pre-coital sexual behaviours (PCSB) in an urban, minority, early adolescent population. Seventh-grade students (n = 1609) reported PCC, PM and PCSB. Multivariable logistic regression was conducted to assess for…

Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

PubMed

Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

2014-01-01

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers.

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

PubMed

André, Silvère; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-03-01

In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017. © 2017 American Institute of Chemical Engineers.

Creation and validation of a novel body condition scoring method for the magellanic penguin (Spheniscus magellanicus) in the zoo setting.

PubMed

Clements, Julie; Sanchez, Jessica N

2015-11-01

This research aims to validate a novel, visual body scoring system created for the Magellanic penguin (Spheniscus magellanicus) suitable for the zoo practitioner. Magellanics go through marked seasonal fluctuations in body mass gains and losses. A standardized multi-variable visual body condition guide may provide a more sensitive and objective assessment tool compared to the previously used single variable method. Accurate body condition scores paired with seasonal weight variation measurements give veterinary and keeper staff a clearer understanding of an individual's nutritional status. San Francisco Zoo staff previously used a nine-point body condition scale based on the classic bird standard of a single point of keel palpation with the bird restrained in hand, with no standard measure of reference assigned to each scoring category. We created a novel, visual body condition scoring system that does not require restraint to assesses subcutaneous fat and muscle at seven body landmarks using illustrations and descriptive terms. The scores range from one, the least robust or under-conditioned, to five, the most robust, or over-conditioned. The ratio of body weight to wing length was used as a "gold standard" index of body condition and compared to both the novel multi-variable and previously used single-variable body condition scores. The novel multi-variable scale showed improved agreement with weight:wing ratio compared to the single-variable scale, demonstrating greater accuracy, and reliability when a trained assessor uses the multi-variable body condition scoring system. Zoo staff may use this tool to manage both the colony and the individual to assist in seasonally appropriate Magellanic penguin nutrition assessment. © 2015 Wiley Periodicals, Inc.

Career satisfaction level, mental distress, and gender differences in working conditions among Japanese obstetricians and gynecologists.

PubMed

Sugiura-Ogasawara, Mayumi; Suzuki, Sadao; Kitazawa, Masafumi; Kuwae, Chizuko; Sawa, Rintaro; Shimizu, Yukiko; Takeshita, Toshiyuki; Yoshimura, Yasunori

2012-03-01

Career satisfaction level, degree of mental distress associated with certain work-related factors, and demographics were examined for the first time in obstetricians and gynecologists in Japan. Associations between the score on Kessler 6 screening scale, or the job satisfaction level, and the scores on the job content questionnaire, Social Support Questionnaire (SSQ), working conditions and demographics were examined in 1301 members of the Japan Society of Obstetrics and Gynecology. 8.4% of respondents were speculated to suffer from depression or anxiety disorder. Multivariate linear regression analysis identified a heavier workload, less personal control, lower satisfaction on the SSQ, and longer working hours as being independent risk factors for mental distress. Careful monitoring of the mental state is necessary for obstetricians and gynecologists with lower incomes, heavier workloads, lower degrees of personal control, and lower satisfaction scores on the SSQ. © 2012 The Authors. Journal of Obstetrics and Gynaecology Research © 2012 Japan Society of Obstetrics and Gynecology.

[Multidimensional measurement of precarious employment: social distribution and its association with health in Catalonia (Spain)].

PubMed

Benach, Joan; Julià, Mireia; Tarafa, Gemma; Mir, Jordi; Molinero, Emilia; Vives, Alejandra

2015-01-01

To show the prevalence of precarious employment in Catalonia (Spain) for the first time and its association with mental and self-rated health, measured with a multidimensional scale. A cross-sectional study was conducted using data from the II Catalan Working Conditions Survey (2010) with a subsample of employed workers with a contract. The prevalence of precarious employment using a multidimensional scale and its association with health was calculated using multivariate log-binomial regression stratified by gender. The prevalence of precarious employment in Catalonia was high (42.6%). We found higher precariousness in women, youth, immigrants, and manual and less educated workers. There was a positive gradient in the association between precarious employment and poor health. Precarious employment is associated with poor health in the working population. Working conditions surveys should include questions on precarious employment and health indicators, which would allow monitoring and subsequent analyses of health inequalities. Copyright © 2015 SESPAS. Published by Elsevier Espana. All rights reserved.

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Identification of unusual events in multi-channel bridge monitoring data

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; Brownjohn, James Mark William; Moyo, Pilate

2004-03-01

Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure such as bridges. However, converting large amounts of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localising sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Application of Dynamic naïve Bayesian classifier to comprehensive drought assessment

NASA Astrophysics Data System (ADS)

Park, D. H.; Lee, J. Y.; Lee, J. H.; KIm, T. W.

2017-12-01

Drought monitoring has already been extensively studied due to the widespread impacts and complex causes of drought. The most important component of drought monitoring is to estimate the characteristics and extent of drought by quantitatively measuring the characteristics of drought. Drought assessment considering different aspects of the complicated drought condition and uncertainty of drought index is great significance in accurate drought monitoring. This study used the dynamic Naïve Bayesian Classifier (DNBC) which is an extension of the Hidden Markov Model (HMM), to model and classify drought by using various drought indices for integrated drought assessment. To provide a stable model for combined use of multiple drought indices, this study employed the DNBC to perform multi-index drought assessment by aggregating the effect of different type of drought and considering the inherent uncertainty. Drought classification was performed by the DNBC using several drought indices: Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Vegetation Supply Water Index (NVSWI)) that reflect meteorological, hydrological, and agricultural drought characteristics. Overall results showed that in comparison unidirectional (SPI, SDI, and NVSWI) or multivariate (Composite Drought Index, CDI) drought assessment, the proposed DNBC was able to synthetically classify of drought considering uncertainty. Model provided method for comprehensive drought assessment with combined use of different drought indices.

Surface water monitoring in the mercury mining district of Asturias (Spain).

PubMed

Loredo, Jorge; Petit-Domínguez, María Dolores; Ordóñez, Almudena; Galán, María Pilar; Fernández-Martínez, Rodolfo; Alvarez, Rodrigo; Rucandio, María Isabel

2010-04-15

Systematic monitoring of surface waters in the area of abandoned mine sites constitutes an essential step in the characterisation of pollution from historic mine sites. The analytical data collected throughout a hydrologic period can be used for hydrological modelling and also to select appropriate preventive and/or corrective measures in order to avoid pollution of watercourses. Caudal River drains the main abandoned Hg mine sites (located in Mieres and Pola de Lena districts) in Central Asturias (NW Spain). This paper describes a systematic monitoring of physical and chemical parameters in eighteen selected sampling points within the Caudal River catchment. At each sampling station, water flow, pH, specific conductance, dissolved oxygen, salinity, temperature, redox potential and turbidity were controlled "in situ" and major and trace elements were analysed in the laboratory. In the Hg-mineralised areas, As is present in the form of As-rich pyrite, realgar and occasionally arsenopyrite. Mine drainage and leachates from spoil heaps exhibit in some cases acidic conditions and high As contents, and they are incorporated to Caudal River tributaries. Multivariate statistical analysis aids to the interpretation of the spatial and temporary variations found in the sampled areas, as part of a methodology applicable to different environmental and geological studies. 2009 Elsevier B.V. All rights reserved.

System and Method for Outlier Detection via Estimating Clusters

NASA Technical Reports Server (NTRS)

Iverson, David J. (Inventor)

2016-01-01

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Fast computation of the multivariable stability margin for real interrelated uncertain parameters

NASA Technical Reports Server (NTRS)

Sideris, Athanasios; Sanchez Pena, Ricardo S.

1988-01-01

A novel algorithm for computing the multivariable stability margin for checking the robust stability of feedback systems with real parametric uncertainty is proposed. This method eliminates the need for the frequency search involved in another given algorithm by reducing it to checking a finite number of conditions. These conditions have a special structure, which allows a significant improvement on the speed of computations.

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.

PubMed

Rathore, Anurag S; Kumar Singh, Sumit; Pathak, Mili; Read, Erik K; Brorson, Kurt A; Agarabi, Cyrus D; Khan, Mansoor

2015-01-01

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers.

Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

Space Weather Research in Armenia

NASA Astrophysics Data System (ADS)

Chilingarian, A. A.

DVIN for ASEC (Data Visualization interactive Network for Aragats Space Environmental Center) is product for accessing and analysis the on-line data from Solar Monitors located at high altitude research station on Mt. Aragats in Armenia. Data from ASEC monitors is used worldwide for scientific purposes and for monitoring of severe solar storms in progress. Alert service, based on the automatic analysis of variations of the different species of cosmic ray particles is available for subscribers. DVIN advantages: DVIN is strategically important as a scientific application to help develop space science and to foster global collaboration in forecasting potential hazards of solar storms. It precisely fits with the goals of the new evolving information society to provide long-term monitoring and collection of high quality scientific data, and enables adequate dialogue between scientists, decision makers, and civil society. The system is highly interactive and exceptional information is easily accessible online. Data can be monitored and analyzed for desired time spans in a fast and reliable manner. The ASEC activity is an example of a balance between the scientific independence of fundamental research and the needs of civil society. DVIN is also an example of how scientific institutions can apply the newest powerful methods of information technologies, such as multivariate data analysis, to their data and also how information technologies can provide convenient and reliable access to this data and to new knowledge for the world-wide scientific community. DVIN provides very wide possibilities for sharing data and sending warnings and alerts to scientists and other entities world-wide, which have fundamental and practical interest in knowing the space weather conditions.

A Simple Score That Predicts Paroxysmal Atrial Fibrillation on Outpatient Cardiac Monitoring after Embolic Stroke of Unknown Source.

PubMed

Ricci, Brittany; Chang, Andrew D; Hemendinger, Morgan; Dakay, Katarina; Cutting, Shawna; Burton, Tina; Mac Grory, Brian; Narwal, Priya; Song, Christopher; Chu, Antony; Mehanna, Emile; McTaggart, Ryan; Jayaraman, Mahesh; Furie, Karen; Yaghi, Shadi

2018-06-01

Occult paroxysmal atrial fibrillation (AF) is detected in 16%-30% of patients with embolic stroke of unknown source (ESUS). The identification of AF predictors on outpatient cardiac monitoring can help guide clinicians decide on a duration or method of cardiac monitoring after ESUS. We included all patients with ESUS who underwent an inpatient diagnostic evaluation and outpatient cardiac monitoring between January 1, 2013, and December 31, 2016. Patients were divided into 2 groups based on detection of AF or atrial flutter during monitoring. We compared demographic data, clinical risk factors, and cardiac biomarkers between the 2 groups. Multivariable logistic regression was used to determine predictors of AF. We identified 296 consecutive patients during the study period; 38 (12.8%) patients had AF detected on outpatient cardiac monitoring. In a multivariable regression analysis, advanced age (ages 65-74: odds ratio [OR] 2.36, 95% confidence interval [CI] .85-6.52; ages 75 or older: OR 4.08, 95% CI 1.58-10.52) and moderate-to-severe left atrial enlargement (OR 4.66, 95% CI 1.79-12.12) were predictors of AF on outpatient monitoring. We developed the Brown ESUS-AF score: age (65-74 years: 1 point, 75 years or older: 2 points) and left atrial enlargement (moderate or severe: 2 points) with good prediction of AF (area under the curve .725) and was internally validated using bootstrapping. The percentage of patients with AF detected in each score category were as follows: 0: 4.2%; 1: 14.8%; 2: 20.8%; 3: 22.2%; 4: 55.6%. The Brown ESUS-AF score predicts AF on prolonged outpatient monitoring after ESUS. More studies are needed to externally validate our findings. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Kinetic approach for the enzymatic determination of levodopa and carbidopa assisted by multivariate curve resolution-alternating least squares.

PubMed

Grünhut, Marcos; Garrido, Mariano; Centurión, Maria E; Fernández Band, Beatriz S

2010-07-12

A combination of kinetic spectroscopic monitoring and multivariate curve resolution-alternating least squares (MCR-ALS) was proposed for the enzymatic determination of levodopa (LVD) and carbidopa (CBD) in pharmaceuticals. The enzymatic reaction process was carried out in a reverse stopped-flow injection system and monitored by UV-vis spectroscopy. The spectra (292-600 nm) were recorded throughout the reaction and were analyzed by multivariate curve resolution-alternating least squares. A small calibration matrix containing nine mixtures was used in the model construction. Additionally, to evaluate the prediction ability of the model, a set with six validation mixtures was used. The lack of fit obtained was 4.3%, the explained variance 99.8% and the overall prediction error 5.5%. Tablets of commercial samples were analyzed and the results were validated by pharmacopeia method (high performance liquid chromatography). No significant differences were found (alpha=0.05) between the reference values and the ones obtained with the proposed method. It is important to note that a unique chemometric model made it possible to determine both analytes simultaneously. Copyright 2010 Elsevier B.V. All rights reserved.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Impact of storage conditions on the urinary metabolomics fingerprint.

PubMed

Laparre, Jérôme; Kaabia, Zied; Mooney, Mark; Buckley, Tom; Sherry, Mark; Le Bizec, Bruno; Dervilly-Pinel, Gaud

2017-01-25

Urine stability during storage is essential in metabolomics to avoid misleading conclusions or erroneous interpretations. Facing the lack of comprehensive studies on urine metabolome stability, the present work performed a follow-up of potential modifications in urinary chemical profile using LC-HRMS on the basis of two parameters: the storage temperature (+4 °C, -20 °C, -80 °C and freeze-dried stored at -80 °C) and the storage duration (5-144 days). Both HILIC and RP chromatographies have been implemented in order to globally monitor the urinary metabolome. Using an original data processing associated to univariate and multivariate data analysis, our study confirms that chemical profiles of urine samples stored at +4 °C are very rapidly modified, as observed for instance for compounds such as:N-acetyl Glycine, Adenosine, 4-Amino benzoic acid, N-Amino diglycine, creatine, glucuronic acid, 3-hydroxy-benzoic acid, pyridoxal, l-pyroglutamic acid, shikimic acid, succinic acid, thymidine, trigonelline and valeryl-carnitine, while it also demonstrates that urine samples stored at -20 °C exhibit a global stability over a long period with no major modifications compared to -80 °C condition. This study is the first to investigate long term stability of urine samples and report potential modifications in the urinary metabolome, using both targeted approach monitoring individually a large number (n > 200) of urinary metabolites and an untargeted strategy enabling assessing for global impact of storage conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatial and temporal trends in water quality in a Mediterranean temporary river impacted by sewage effluents.

PubMed

David, Arthur; Tournoud, Marie-George; Perrin, Jean-Louis; Rosain, David; Rodier, Claire; Salles, Christian; Bancon-Montigny, Chrystelle; Picot, Bernadette

2013-03-01

This paper analyzes how changes in hydrological conditions can affect the water quality of a temporary river that receives direct inputs of sewage effluents. Data from 12 spatial surveys of the Vène river were examined. Physico-chemical parameters, major ion, and nutrient concentrations were measured. Analyses of variance (ANOVA) and multivariate analyses were performed. ANOVA revealed significant spatial differences for conductivity and major ion but no significant spatial differences for nutrient concentrations even if higher average concentrations were observed at stations located downstream from sewage effluent discharge points. Significant temporal differences were observed among all the parameters. Karstic springs had a marked dilution effect on the direct disposal of sewage effluents. During high-flow periods, nutrient concentrations were high to moderate whereas nutrient concentrations ranged from moderate to bad at stations located downstream from the direct inputs of sewage effluents during low-flow periods. Principal component analysis showed that water quality parameters that explained the water quality of the Vène river were highly dependent on hydrological conditions. Cluster analysis showed that when the karstic springs were flowing, water quality was homogeneous all along the river, whereas when karstic springs were dry, water quality at the monitoring stations was more fragmented. These results underline the importance of considering hydrological conditions when monitoring the water quality of temporary rivers. In view of the pollution observed in the Vène river, "good water chemical status" can probably only be achieved by improving the management of sewage effluents during low-flow periods.

What`s normal?: Body condition in Great Lakes herring gulls

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

Hebert, C.E.; Shutt, J.L.

1994-12-31

The Canadian Wildlife Service`s herring gull (Larus argentatus) surveillance program has demonstrated the usefulness of this species as a monitor of spatial and temporal trends in contaminant levels. However, the effects of environmental contaminants on gulls are difficult to distinguish from the effects of other anthropogenic stressors such as the introduction of exotic species, overfishing and habitat loss. To understand the relative importance of these factors in regulating the success of individual gulls and, hence, gull populations, the authors must first have a better understanding of what constitutes a ``normal`` bird. Improving the ability to differentiate between normal and abnormalmore » birds is crucial in any health assessment of Great Lakes gulls. Body condition has been shown to be an important measure of a bird`s ability to provide energy for egg production, migration etc. Numerous approaches have been used to assess condition, most of which required that the bird be sacrificed. In this study, the authors describe a nonlethal technique to quantify body condition in herring gulls. Multivariate statistics are used to quantify body size, relate body size to total mass and from that, determine relative body condition. Initially, body condition is assessed in gulls from a reference colony where reproductive success is normal and anthropogenic influences are limited. This reference population is then used as a baseline against which other gull populations are compared.« less

Continuous glucose monitoring to assess the ecologic validity of dietary glycemic index and glycemic load123

PubMed Central

Ebbeling, Cara B; Wadden, Thomas A; Ludwig, David S

2011-01-01

Background: The circumstances under which the glycemic index (GI) and glycemic load (GL) are derived do not reflect real-world eating behavior. Thus, the ecologic validity of these constructs is incompletely known. Objective: This study examined the relation of dietary intake to glycemic response when foods are consumed under free-living conditions. Design: Participants were 26 overweight or obese adults with type 2 diabetes who participated in a randomized trial of lifestyle modification. The current study includes baseline data, before initiation of the intervention. Participants wore a continuous glucose monitor and simultaneously kept a food diary for 3 d. The dietary variables included GI, GL, and intakes of energy, fat, protein, carbohydrate, sugars, and fiber. The glycemic response variables included AUC, mean and SD of continuous glucose monitoring (CGM) values, percentage of CGM values in euglycemic and hyperglycemic ranges, and mean amplitude of glycemic excursions. Relations between daily dietary intake and glycemic outcomes were examined. Results: Data were available from 41 d of monitoring. Partial correlations, controlled for energy intake, indicated that GI or GL was significantly associated with each glycemic response outcome. In multivariate analyses, dietary GI accounted for 10% to 18% of the variance in each glycemic variable, independent of energy and carbohydrate intakes (P < 0.01). Conclusions: The data support the ecologic validity of the GI and GL constructs in free-living obese adults with type 2 diabetes. GI was the strongest and most consistent independent predictor of glycemic stability and variability. PMID:22071699

Risk Factors for Pregnancy-Associated Stroke in Women With Preeclampsia.

PubMed

Miller, Eliza C; Gatollari, Hajere J; Too, Gloria; Boehme, Amelia K; Leffert, Lisa; Marshall, Randolph S; Elkind, Mitchell S V; Willey, Joshua Z

2017-07-01

Preeclampsia affects 3% to 8% of pregnancies and increases risk of pregnancy-associated stroke (PAS). Data are limited on which women with preeclampsia are at highest risk for PAS. Using billing data from the 2003 to 2012 New York State Department of Health inpatient database, we matched women with preeclampsia and PAS 1:3 to preeclamptic controls based on age and race/ethnicity. Pre-defined PAS risk factors included pregnancy complications, infection present on admission, vascular risk factors, prothrombotic states, and coagulopathies. We constructed multivariable conditional logistic regression models to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) for independent risk factors for PAS. Among women aged 12 to 55 years admitted to New York State hospitals for any reason during the study period (n=3 373 114), 88 857 had preeclampsia, and 197 of whom (0.2%) had PAS. In multivariable analysis, women with preeclampsia and stroke were more likely than controls to have severe preeclampsia or eclampsia (OR, 7.2; 95% confidence interval [CI], 4.6-11.3), infections present on admission (OR, 3.0; 95% CI, 1.6-5.8), prothrombotic states (OR, 3.5; 95% CI, 1.3-9.2), coagulopathies (OR, 3.1; 95% CI, 1.3-7.1), or chronic hypertension (OR, 3.2; 95% CI, 1.8-5.5). Additional analyses matched and stratified by severity of preeclampsia confirmed these results. Infections, chronic hypertension, coagulopathies, and underlying prothrombotic conditions increase PAS risk in women with preeclampsia. These women may warrant closer monitoring. © 2017 American Heart Association, Inc.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors.

PubMed

Rowland-Jones, Ruth C; van den Berg, Frans; Racher, Andrew J; Martin, Elaine B; Jaques, Colin

2017-03-01

Cell culture process development requires the screening of large numbers of cell lines and process conditions. The development of miniature bioreactor systems has increased the throughput of such studies; however, there are limitations with their use. One important constraint is the limited number of offline samples that can be taken compared to those taken for monitoring cultures in large-scale bioreactors. The small volume of miniature bioreactor cultures (15 mL) is incompatible with the large sample volume (600 µL) required for bioanalysers routinely used. Spectroscopy technologies may be used to resolve this limitation. The purpose of this study was to compare the use of NIR, Raman, and 2D-fluorescence to measure multiple analytes simultaneously in volumes suitable for daily monitoring of a miniature bioreactor system. A novel design-of-experiment approach is described that utilizes previously analyzed cell culture supernatant to assess metabolite concentrations under various conditions while providing optimal coverage of the desired design space. Multivariate data analysis techniques were used to develop predictive models. Model performance was compared to determine which technology is more suitable for this application. 2D-fluorescence could more accurately measure ammonium concentration (RMSE CV 0.031 g L -1 ) than Raman and NIR. Raman spectroscopy, however, was more robust at measuring lactate and glucose concentrations (RMSE CV 1.11 and 0.92 g L -1 , respectively) than the other two techniques. The findings suggest that Raman spectroscopy is more suited for this application than NIR and 2D-fluorescence. The implementation of Raman spectroscopy increases at-line measuring capabilities, enabling daily monitoring of key cell culture components within miniature bioreactor cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:337-346, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Erectile dysfunction and lower urinary tract symptoms: a consensus on the importance of co-diagnosis

PubMed Central

Kirby, M; Chapple, C; Jackson, G; Eardley, I; Edwards, D; Hackett, G; Ralph, D; Rees, J; Speakman, M; Spinks, J; Wylie, K

2013-01-01

Despite differences in design, many large epidemiological studies using well-powered multivariate analyses consistently provide overwhelming evidence of a link between erectile dysfunction (ED) and lower urinary tract symptoms (LUTS). Preclinical evidence suggests that several common pathophysiological mechanisms are involved in the development of both ED and LUTS. We recommend that patients seeking consultation for one condition should always be screened for the other condition. We propose that co-diagnosis would ensure that patient management accounts for all possible co-morbid and associated conditions. Medical, socio-demographic and lifestyle risk factors can help to inform diagnoses and should be taken into consideration during the initial consultation. Awareness of risk factors may alert physicians to patients at risk of ED or LUTS and so allow them to manage patients accordingly; early diagnosis of ED in patients with LUTS, for example, could help reduce the risk of subsequent cardiovascular disease. Prescribing physicians should be aware of the sexual adverse effects of many treatments currently recommended for LUTS; sexual function should be evaluated prior to commencement of treatment, and monitored throughout treatment to ensure that the choice of drug is appropriate. PMID:23617950

A comparison of macroinvertebrate and habitat methods of data collection in the Little Colorado River Watershed, Arizona 2007

USGS Publications Warehouse

Spindler, Patrice; Paretti, Nick V.

2007-01-01

The Arizona Department of Environmental Quality (ADEQ) and the U.S. Environmental Protection Agency (USEPA) Ecological Monitoring and Assessment Program (EMAP), use different field methods for collecting macroinvertebrate samples and habitat data for bioassessment purposes. Arizona’s Biocriteria index was developed using a riffle habitat sampling methodology, whereas the EMAP method employs a multi-habitat sampling protocol. There was a need to demonstrate comparability of these different bioassessment methodologies to allow use of the EMAP multi-habitat protocol for both statewide probabilistic assessments for integration of the EMAP data into the national (305b) assessment and for targeted in-state bioassessments for 303d determinations of standards violations and impaired aquatic life conditions. The purpose of this study was to evaluate whether the two methods yield similar bioassessment results, such that the data could be used interchangeably in water quality assessments. In this Regional EMAP grant funded project, a probabilistic survey of 30 sites in the Little Colorado River basin was conducted in the spring of 2007. Macroinvertebrate and habitat data were collected using both ADEQ and EMAP sampling methods, from adjacent reaches within these stream channels.

All analyses indicated that the two macroinvertebrate sampling methods were significantly correlated. ADEQ and EMAP samples were classified into the same scoring categories (meeting, inconclusive, violating the biocriteria standard) 82% of the time. When the ADEQ-IBI was applied to both the ADEQ and EMAP taxa lists, the resulting IBI scores were significantly correlated (r=0.91), even though only 4 of the 7 metrics in the IBI were significantly correlated. The IBI scores from both methods were significantly correlated to the percent of riffle habitat, even though the average percent riffle habitat was only 30% of the stream reach. Multivariate analyses found that the percent riffle was an important attribute for both datasets in classifying IBI scores into assessment categories.

Habitat measurements generated from EMAP and ADEQ methods were also significantly correlated; 13 of 16 habitat measures were significantly correlated (p<0.01). The visual-based percentage estimates of percent riffle and pool habitats, vegetative cover and percent canopy cover, and substrate measurements of percent fine substrate and embeddedness were all remarkably similar, given the different field methods used. A multivariate analysis identified substrate and flow conditions, as well as canopy cover as important combinations of habitat attributes affecting both IBI scores. These results indicate that similar habitat measures can be obtained using two different field sampling protocols. In addition, similar combinations of these habitat parameters were important to macroinvertebrate community condition in multivariate analyses of both ADEQ and EMAP datasets.

These results indicate the two sampling methods for macroinvertebrates and habitat data were very similar in terms of bioassessment results and stressors. While the bioassessment category was not identical for all sites, overall the assessments were significantly correlated, providing similar bioassessment results for the cold water streams used in this study. The findings of this study indicate that ADEQ can utilize either a riffle-based sampling methodology or a multi-habitat sampling approach in cold water streams as both yield similar results relative to the macroinvertebrate assemblage. These results will allow for use of either macroinvertebrate dataset to determine water quality standards compliance with the ADEQ Indexes of Biological Integrity, for which threshold values were just recently placed into the Arizona Surface Water Quality Standards. While this survey did not include warm water desert streams of Arizona, we would predict that EMAP and ADEQ sampling methodologies would provide similar bioassessment results and would not be significantly different, as we have found that the percent riffle habitat in cold and warm water perennial, wadeable streams is not significantly different. However, a comparison study of sampling methodologies in warm water streams should be conducted to confirm the predicted similarity of bioassessment results. ADEQ will continue to implement a monitoring strategy that includes probabilistic monitoring for a statewide ecological assessment of stream conditions. Conclusions from this study will guide decisions regarding the most appropriate sampling methods for future probabilistic monitoring sample plans.

Hybrid hard- and soft-modeling of spectrophotometric data for monitoring of ciprofloxacin and its main photodegradation products at different pH values

NASA Astrophysics Data System (ADS)

Razuc, Mariela; Garrido, Mariano; Caro, Yamile S.; Teglia, Carla M.; Goicoechea, Héctor C.; Fernández Band, Beatriz S.

2013-04-01

A simple and fast on line spectrophotometric method combined with a hybrid hard-soft modeling multivariate curve resolution (HS-MCR) was proposed for the monitoring of photodegradation reaction of ciprofloxacin under UV radiation. The studied conditions attempt to emulate the effect of sunlight on these antibiotics that could be eventually present in the environment. The continuous flow system made it possible to study the ciprofloxacin degradation at different pH values almost at real time, avoiding errors that could arise from typical batch monitoring of the reaction. On the base of a concentration profiles obtained by previous pure soft-modeling approach, reaction pathways have been proposed for the parent compound and its photoproducts at different pH values. These kinetic models were used as a constraint in the HS-MCR analysis. The kinetic profiles and the corresponding pure response profile (UV-Vis spectra) of ciprofloxacin and its main degradation products were recovered after the application of HS-MCR analysis to the spectra recorded throughout the reaction. The observed behavior showed a good agreement with the photodegradation studies reported in the bibliography. Accordingly, the photodegradation reaction was studied by high performance liquid chromatography coupled with UV-Vis diode array detector (HPLC-DAD). The spectra recorded during the chromatographic analysis present a good correlation with the ones recovered by UV-Vis/HS-MCR method.

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

PubMed

Moss, Travis J; Lake, Douglas E; Calland, J Forrest; Enfield, Kyle B; Delos, John B; Fairchild, Karen D; Moorman, J Randall

2016-09-01

Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. None. We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

Operational and environmental determinants of in-vehicle CO and PM2.5 exposure.

PubMed

Alameddine, I; Abi Esber, L; Bou Zeid, E; Hatzopoulou, M; El-Fadel, M

2016-05-01

This study presents a modeling framework to quantify the complex roles that traffic, seasonality, vehicle characteristics, ventilation, meteorology, and ambient air quality play in dictating in-vehicle commuter exposure to CO and PM2.5. For this purpose, a comprehensive one-year monitoring program of 25 different variables was coupled with a multivariate regression analysis to develop models to predict in-vehicle CO and PM2.5 exposure using a database of 119 mobile tests and 120 fume leakage tests. The study aims to improve the understanding of in-cabin exposure, as well as interior-exterior pollutant exchange. Model results highlighted the strong correlation between out-vehicle and in-vehicle concentrations, with the effect of ventilation type only discerned for PM2.5 levels. Car type, road conditions, as well as meteorological conditions all played a significant role in modulating in-vehicle exposure. The CO and PM2.5 exposure models were able to explain 72 and 92% of the variability in measured concentrations, respectively. Both models exhibited robustness and no-evidence of over-fitting. Copyright © 2016 Elsevier B.V. All rights reserved.

V/STOL propulsion control analysis: Phase 2, task 5-9

NASA Technical Reports Server (NTRS)

1981-01-01

Typical V/STOL propulsion control requirements were derived for transition between vertical and horizontal flight using the General Electric RALS (Remote Augmented Lift System) concept. Steady-state operating requirements were defined for a typical Vertical-to-Horizontal transition and for a typical Horizontal-to-Vertical transition. Control mode requirements were established and multi-variable regulators developed for individual operating conditions. Proportional/Integral gain schedules were developed and were incorporated into a transition controller with capabilities for mode switching and manipulated variable reassignment. A non-linear component-level transient model of the engine was developed and utilized to provide a preliminary check-out of the controller logic. An inlet and nozzle effects model was developed for subsequent incorporation into the engine model and an aircraft model was developed for preliminary flight transition simulations. A condition monitoring development plan was developed and preliminary design requirements established. The Phase 1 long-range technology plan was refined and restructured toward the development of a real-time high fidelity transient model of a supersonic V/STOL propulsion system and controller for use in a piloted simulation program at NASA-Ames.

MRMPROBS: a data assessment and metabolite identification tool for large-scale multiple reaction monitoring based widely targeted metabolomics.

PubMed

Tsugawa, Hiroshi; Arita, Masanori; Kanazawa, Mitsuhiro; Ogiwara, Atsushi; Bamba, Takeshi; Fukusaki, Eiichiro

2013-05-21

We developed a new software program, MRMPROBS, for widely targeted metabolomics by using the large-scale multiple reaction monitoring (MRM) mode. The strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability. However, the traditional method of assessing measured metabolomics data without probabilistic criteria is not only time-consuming but is often subjective and makeshift work. Our program overcomes these problems by detecting and identifying metabolites automatically, by separating isomeric metabolites, and by removing background noise using a probabilistic score defined as the odds ratio from an optimized multivariate logistic regression model. Our software program also provides a user-friendly graphical interface to curate and organize data matrices and to apply principal component analyses and statistical tests. For a demonstration, we conducted a widely targeted metabolome analysis (152 metabolites) of propagating Saccharomyces cerevisiae measured at 15 time points by gas and liquid chromatography coupled to triple quadrupole mass spectrometry. MRMPROBS is a useful and practical tool for the assessment of large-scale MRM data available to any instrument or any experimental condition.

Identification of unusual events in multichannel bridge monitoring data using wavelet transform and outlier analysis

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; Brownjohn, James M. W.; Moyo, Pilate

2003-08-01

Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure. However, converting large amount of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure in Singapore and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localizing sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

Relationships among functional markers, management, and husbandry in sheep: a Mediterranean case study.

PubMed

Petazzi, F; Rubino, G; Alloggio, I; Caroli, A; Pieragostini, E

2009-12-01

Most sheep farmers are aware of the importance of monitoring animal health and well-being for profitable sheep production. Unfortunately, there are only a few benchmarked functional measures of sheep well-being but much can be gained from our understanding of other species. Moreover, comprehensive monitoring programs may be complex and relatively expensive to implement. Hence, this work reports the results of a research study on the usefulness of functional markers in measuring dairy sheep well-being, taking into account farm management and environmental conditions. The study was conducted on 11 farms breeding Italian islander sheep breeds. The husbandry and management parameters of each farm were assessed and, based on the findings, the farms were scored in ascending quality order. Flock information concerned housing, milking system, pen size, grazing hours, health management, and stockmanship. Medical history, clinical data, the most relevant haematological, chemical and biochemical parameters, as well as the haemoglobin genotype were recorded for 415 individuals. The whole data-set was analyzed by Spearman correlation and multivariate statistical procedures, showing that albumin, serum alkaline phosphatase, haematocrit, and haemoglobin were the most significant functional markers of a flock's general conditions. Haematocrit and haemoglobin reflect animal health status, while albumin and serum alkaline phosphatase are a measure of nutritional status and physical activity, respectively. These are objective parameters, which can be easily measured from blood samples and have proved to be effective for grouping to interpret animal well-being.

Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor.

PubMed

Farooq, Muhammad; Sazonov, Edward

2017-11-01

Several methods have been proposed for automatic and objective monitoring of food intake, but their performance suffers in the presence of speech and motion artifacts. This paper presents a novel sensor system and algorithms for detection and characterization of chewing bouts from a piezoelectric strain sensor placed on the temporalis muscle. The proposed data acquisition device was incorporated into the temple of eyeglasses. The system was tested by ten participants in two part experiments, one under controlled laboratory conditions and the other in unrestricted free-living. The proposed food intake recognition method first performed an energy-based segmentation to isolate candidate chewing segments (instead of using epochs of fixed duration commonly reported in research literature), with the subsequent classification of the segments by linear support vector machine models. On participant level (combining data from both laboratory and free-living experiments), with ten-fold leave-one-out cross-validation, chewing were recognized with average F-score of 96.28% and the resultant area under the curve was 0.97, which are higher than any of the previously reported results. A multivariate regression model was used to estimate chew counts from segments classified as chewing with an average mean absolute error of 3.83% on participant level. These results suggest that the proposed system is able to identify chewing segments in the presence of speech and motion artifacts, as well as automatically and accurately quantify chewing behavior, both under controlled laboratory conditions and unrestricted free-living.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

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

PubMed

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

2013-06-01

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

The association of fatigue, comorbidity burden, disease activity, disability and gross domestic product in patients with rheumatoid arthritis. Results from 34 countries participating in the Quest-RA program.

PubMed

Grøn, Kathrine Lederballe; Ornbjerg, Lykke Midtbøll; Hetland, Merete Lund; Aslam, Fawad; Khan, Nasim A; Jacobs, Johannes W G; Henrohn, Dan; Rasker, J J; Kauppi, Markku J; Lang, Hui-Chu; Mota, Licia M H; Aggarwal, Amita; Yamanaka, Hisahi; Badsha, Humeira; Gossec, Laure; Cutolo, Maurizio; Ferraccioli, Gianfranco; Gremese, Elisa; Bong Lee, Eun; Inanc, Nevsun; Direskeneli, Haner; Taylor, Peter; Huisman, Margriet; Alten, Rieke; Pohl, Christoph; Oyoo, Omondi; Stropuviene, Sigita; Drosos, Alexandrosos A; Kerzberg, Eduardo; Ancuta, Codorina; Mofti, Ayman; Bergman, Martin; Detert, Jaqueline; Selim, Zaraa I; Abda, Essam A; Rexhepi, Blerta; Sokka, Tuulikki

2014-01-01

The aim is to assess the prevalence of comorbidities and to further analyse to which degree fatigue can be explained by comorbidity burden, disease activity, disability and gross domestic product (GDP) in patients with rheumatoid arthritis (RA). Nine thousands eight hundred seventy-four patients from 34 countries, 16 with high GDP (>24.000 US dollars [USD] per capita) and 18 low-GDP countries (<24.000 USD) participated in the Quantitative Standard monitoring of Patients with RA (QUEST-RA) study. The prevalence of 31 comorbid conditions, fatigue (0-10 cm visual analogue scale [VAS] [10=worst]), disease activity in 28 joints (DAS28), and physical disability (Health Assessment Questionnaire score [HAQ]) were assessed. Univariate and multivariate linear regression analyses were performed to assess the association between fatigue and comorbidities, disease activity, disability and GDP. Overall, patients reported a median of 2 comorbid conditions of which hypertension (31.5%), osteoporosis (17.6%), osteoarthritis (15.5%) and hyperlipidaemia (14.2%) were the most prevalent. The majority of comorbidities were more common in high-GDP countries. The median fatigue score was 4.4 (4.8 in low-GDP countries and 3.8 in high-GDP countries, p<0.001). In low-GDP countries 25.4% of the patients had a high level of fatigue (>6.6) compared with 23.0% in high-GDP countries (p<0.001). In univariate analysis, fatigue increased with increasing number of comorbidities, disease activity and disability in both high- and low-GDP countries. In multivariate analysis of all countries, these 3 variables explained 29.4% of the variability, whereas GDP was not significant. Fatigue is a widespread problem associated with high comorbidity burden, disease activity and disability regardless of GDP.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Time to antibiotics and outcomes in cancer patients with febrile neutropenia

PubMed Central

2014-01-01

Background Febrile neutropenia is an oncologic emergency. The timing of antibiotics administration in patients with febrile neutropenia may result in adverse outcomes. Our study aims to determine time-to- antibiotic administration in patients with febrile neutropenia, and its relationship with length of hospital stay, intensive care unit monitoring, and hospital mortality. Methods The study population was comprised of adult cancer patients with febrile neutropenia who were hospitalized, at a tertiary care hospital, between January 2010 and December 2011. Using Multination Association of Supportive Care in Cancer (MASCC) risk score, the study cohort was divided into high and low risk groups. A multivariate regression analysis was performed to assess relationship between time-to- antibiotic administration and various outcome variables. Results One hundred and five eligible patients with median age of 60 years (range: 18–89) and M:F of 43:62 were identified. Thirty-seven (35%) patients were in MASCC high risk group. Median time-to- antibiotic administration was 2.5 hrs (range: 0.03-50) and median length of hospital stay was 6 days (range: 1–57). In the multivariate analysis time-to- antibiotic administration (regression coefficient [RC]: 0.31 days [95% CI: 0.13-0.48]), known source of fever (RC: 4.1 days [95% CI: 0.76-7.5]), and MASCC high risk group (RC: 4 days [95% CI: 1.1-7.0]) were significantly correlated with longer hospital stay. Of 105 patients, 5 (4.7%) died & or required ICU monitoring. In multivariate analysis no variables significantly correlated with mortality or ICU monitoring. Conclusions Our study revealed that delay in antibiotics administration has been associated with a longer hospital stay. PMID:24716604

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

PubMed

Kitakaji, Yoko; Ohnuma, Susumu

2014-04-01

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

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-attribute subjective evaluations of manual tracking tasks vs. objective performance of the human operator

NASA Technical Reports Server (NTRS)

Siapkaras, A.

1977-01-01

A computational method to deal with the multidimensional nature of tracking and/or monitoring tasks is developed. Operator centered variables, including the operator's perception of the task, are considered. Matrix ratings are defined based on multidimensional scaling techniques and multivariate analysis. The method consists of two distinct steps: (1) to determine the mathematical space of subjective judgements of a certain individual (or group of evaluators) for a given set of tasks and experimental conditionings; and (2) to relate this space with respect to both the task variables and the objective performance criteria used. Results for a variety of second-order trackings with smoothed noise-driven inputs indicate that: (1) many of the internally perceived task variables form a nonorthogonal set; and (2) the structure of the subjective space varies among groups of individuals according to the degree of familiarity they have with such tasks.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Novel health monitoring method using an RGB camera.

PubMed

Hassan, M A; Malik, A S; Fofi, D; Saad, N; Meriaudeau, F

2017-11-01

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.

Evaluation of drinking quality of groundwater through multivariate techniques in urban area.

PubMed

Das, Madhumita; Kumar, A; Mohapatra, M; Muduli, S D

2010-07-01

Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.

Online UV-visible spectroscopy and multivariate curve resolution as powerful tool for model-free investigation of laccase-catalysed oxidation.

PubMed

Kandelbauer, A; Kessler, W; Kessler, R W

2008-03-01

The laccase-catalysed transformation of indigo carmine (IC) with and without a redox active mediator was studied using online UV-visible spectroscopy. Deconvolution of the mixture spectra obtained during the reaction was performed on a model-free basis using multivariate curve resolution (MCR). Thereby, the time courses of educts, products, and reaction intermediates involved in the transformation were reconstructed without prior mechanistic assumptions. Furthermore, the spectral signature of a reactive intermediate which could not have been detected by a classical hard-modelling approach was extracted from the chemometric analysis. The findings suggest that the combined use of UV-visible spectroscopy and MCR may lead to unexpectedly deep mechanistic evidence otherwise buried in the experimental data. Thus, although rather an unspecific method, UV-visible spectroscopy can prove useful in the monitoring of chemical reactions when combined with MCR. This offers a wide range of chemists a cheap and readily available, highly sensitive tool for chemical reaction online monitoring.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Raman exfoliative cytology for oral precancer diagnosis

NASA Astrophysics Data System (ADS)

Sahu, Aditi; Gera, Poonam; Pai, Venkatesh; Dubey, Abhishek; Tyagi, Gunjan; Waghmare, Mandavi; Pagare, Sandeep; Mahimkar, Manoj; Murali Krishna, C.

2017-11-01

Oral premalignant lesions (OPLs) such as leukoplakia, erythroplakia, and oral submucous fibrosis, often precede oral cancer. Screening and management of these premalignant conditions can improve prognosis. Raman spectroscopy has previously demonstrated potential in the diagnosis of oral premalignant conditions (in vivo), detected viral infection, and identified cancer in both oral and cervical exfoliated cells (ex vivo). The potential of Raman exfoliative cytology (REC) in identifying premalignant conditions was investigated. Oral exfoliated samples were collected from healthy volunteers (n=20), healthy volunteers with tobacco habits (n=20), and oral premalignant conditions (n=27, OPL) using Cytobrush. Spectra were acquired using Raman microprobe. Spectral acquisition parameters were: λex: 785 nm, laser power: 40 mW, acquisition time: 15 s, and average: 3. Postspectral acquisition, cell pellet was subjected to Pap staining. Multivariate analysis was carried out using principal component analysis and principal component-linear discriminant analysis using both spectra- and patient-wise approaches in three- and two-group models. OPLs could be identified with ˜77% (spectra-wise) and ˜70% (patient-wise) sensitivity in the three-group model while with 86% (spectra-wise) and 83% (patient-wise) in the two-group model. Use of histopathologically confirmed premalignant cases and better sampling devices may help in development of improved standard models and also enhance the sensitivity of the method. Future longitudinal studies can help validate potential of REC in screening and monitoring high-risk populations and prognosis prediction of premalignant lesions.

Application of a three-tier framework to assess ecological ...

EPA Pesticide Factsheets

A multi‐level coastal wetland assessment strategy was applied to wetlands in the northern Gulf of Mexico (GOM) to evaluate the feasibility of this approach for a broad national scale wetland condition assessment (U.S. Environmental Protection Agency’s National Wetlands Condition Assessment). Landscape‐scale assessment indicators (Tier 1) were developed and applied at the sub‐watershed (12‐digit Hydrologic Unit) level within the GOM coastal wetland sample frame with scores calculated using land‐use maps and GIS. Rapid assessment protocols (Tier‐2), using a combination of office and field work, evaluated metrics associated with landscape context, hydrology, physical structure, and biological structure. Intensive site monitoring (Tier‐3) included measures of soil chemistry and composition, water column and pore‐water chemistry, and dominant macrophyte community composition and tissue chemistry. Relationships within and among assessment levels were evaluated using multivariate and principal component analyses with few significant correlations were found. More detailed measures of hydrology, soils, and macrophyte species composition from sites across a known condition gradient, in conjunction with validation of standardized rapid assessment method, may be necessary to fully characterize coastal wetlands across the region This manuscript describes the application of a multi-level coastal wetland assessment strategy to wetlands in the northern Gulf of

Development of a multimetric index for integrated assessment of salt marsh ecosystem condition

USGS Publications Warehouse

Nagel, Jessica L.; Neckles, Hilary A.; Guntenspergen, Glenn R.; Rocks, Erika N.; Schoolmaster, Donald; Grace, James B.; Skidds, Dennis; Stevens, Sara

2018-01-01

Tools for assessing and communicating salt marsh condition are essential to guide decisions aimed at maintaining or restoring ecosystem integrity and services. Multimetric indices (MMIs) are increasingly used to provide integrated assessments of ecosystem condition. We employed a theory-based approach that considers the multivariate relationship of metrics with human disturbance to construct a salt marsh MMI for five National Parks in the northeastern USA. We quantified the degree of human disturbance for each marsh using the first principal component score from a principal components analysis of physical, chemical, and land use stressors. We then applied a metric selection algorithm to different combinations of about 45 vegetation and nekton metrics (e.g., species abundance, species richness, and ecological and functional classifications) derived from multi-year monitoring data. While MMIs derived from nekton or vegetation metrics alone were strongly correlated with human disturbance (r values from −0.80 to −0.93), an MMI derived from both vegetation and nekton metrics yielded an exceptionally strong correlation with disturbance (r = −0.96). Individual MMIs included from one to five metrics. The metric-assembly algorithm yielded parsimonious MMIs that exhibit the greatest possible correlations with disturbance in a way that is objective, efficient, and reproducible.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Algal Assemblages for Large River Monitoring: Comparison Among Biovolume, Absolute and Relative Abundance Metrics

EPA Science Inventory

Periphyton and phytoplankton samples were collected and analyzed from 393 locations in three mid-continent (US) great rivers: the Upper Mississippi, Missouri and Ohio rivers. From the 410 taxa identified, 303 taxa were common enough for multivariate analyses. Algae assemblages we...

The assessment of processes controlling the spatial distribution of hydrogeochemical groundwater types in Mali using multivariate statistics

NASA Astrophysics Data System (ADS)

Keita, Souleymane; Zhonghua, Tang

2017-10-01

Sustainable management of groundwater resources is a major issue for developing countries, especially in Mali. The multiple uses of groundwater led countries to promote sound management policies for sustainable use of the groundwater resources. For this reason, each country needs data enabling it to monitor and predict the changes of the resources. Also given the importance of groundwater quality changes often marked by the recurrence of droughts; the potential impacts of regional and geological setting of groundwater resources requires careful study. Unfortunately, recent decades have seen a considerable reduction of national capacities to ensure the hydrogeological monitoring and production of qualit data for decision making. The purpose of this work is to use the groundwater data and translate into useful information that can improve water resources management capacity in Mali. In this paper, we used groundwater analytical data from accredited, laboratories in Mali to carry out a national scale assessment of the groundwater types and their distribution. We, adapted multivariate statistical methods to classify 2035 groundwater samples into seven main groundwater types and built a national scale map from the results. We used a two-level K-mean clustering technique to examine the hydro-geochemical records as percentages of the total concentrations of major ions, namely sodium (Na), magnesium (Mg), calcium (Ca), chloride (Cl), bicarbonate (HCO3), and sulphate (SO4). The first step of clustering formed 20 groups, and these groups were then re-clustered to produce the final seven groundwater types. The results were verified and confirmed using Principal Component Analysis (PCA) and RockWare (Aq.QA) software. We found that HCO3 was the most dominant anion throughout the country and that Cl and SO4 were only important in some local zones. The dominant cations were Na and Mg. Also, major ion ratios changed with geographical location and geological, and climatic conditions.

Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle.

PubMed

Reith, S; Hoy, S

2018-02-01

Efficient detection of estrus is a permanent challenge for successful reproductive performance in dairy cattle. In this context, comprehensive knowledge of estrus-related behaviors is fundamental to achieve optimal estrus detection rates. This review was designed to identify the characteristics of behavioral estrus as a necessary basis for developing strategies and technologies to improve the reproductive management on dairy farms. The focus is on secondary symptoms of estrus (mounting, activity, aggressive and agonistic behaviors) which seem more indicative than standing behavior. The consequences of management, housing conditions and cow- and environmental-related factors impacting expression and detection of estrus as well as their relative importance are described in order to increase efficiency and accuracy of estrus detection. As traditional estrus detection via visual observation is time-consuming and ineffective, there has been a considerable advancement of detection aids during the last 10 years. By now, a number of fully automated technologies including pressure sensing systems, activity meters, video cameras, recordings of vocalization as well as measurements of body temperature and milk progesterone concentration are available. These systems differ in many aspects regarding sustainability and efficiency as keys to their adoption for farm use. As being most practical for estrus detection a high priority - according to the current research - is given to the detection based on sensor-supported activity monitoring, especially accelerometer systems. Due to differences in individual intensity and duration of estrus multivariate analysis can support herd managers in determining the onset of estrus. Actually, there is increasing interest in investigating the potential of combining data of activity monitoring and information of several other methods, which may lead to the best results concerning sensitivity and specificity of detection. Future improvements will likely require more multivariate detection by data and systems already existing on farms.

Diagnosis of abnormal patterns in multivariate microclimate monitoring: a case study of an open-air archaeological site in Pompeii (Italy).

PubMed

Merello, Paloma; García-Diego, Fernando-Juan; Zarzo, Manuel

2014-08-01

Chemometrics has been applied successfully since the 1990s for the multivariate statistical control of industrial processes. A new area of interest for these tools is the microclimatic monitoring of cultural heritage. Sensors record climatic parameters over time and statistical data analysis is performed to obtain valuable information for preventive conservation. A case study of an open-air archaeological site is presented here. A set of 26 temperature and relative humidity data-loggers was installed in four rooms of Ariadne's house (Pompeii). If climatic values are recorded versus time at different positions, the resulting data structure is equivalent to records of physical parameters registered at several points of a continuous chemical process. However, there is an important difference in this case: continuous processes are controlled to reach a steady state, whilst open-air sites undergo tremendous fluctuations. Although data from continuous processes are usually column-centred prior to applying principal components analysis, it turned out that another pre-treatment (row-centred data) was more convenient for the interpretation of components and to identify abnormal patterns. The detection of typical trajectories was more straightforward by dividing the whole monitored period into several sub-periods, because the marked climatic fluctuations throughout the year affect the correlation structures. The proposed statistical methodology is of interest for the microclimatic monitoring of cultural heritage, particularly in the case of open-air or semi-confined archaeological sites. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

PubMed Central

Austin, Elena; Coull, Brent A.; Zanobetti, Antonella; Koutrakis, Petros

2013-01-01

Background Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. Objectives Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. Methods 109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. Results Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. Conclusions The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research. PMID:23850585

Combination Social Protection for Reducing HIV-Risk Behavior Among Adolescents in South Africa.

PubMed

Cluver, Lucie D; Orkin, F Mark; Yakubovich, Alexa R; Sherr, Lorraine

2016-05-01

Social protection (ie, cash transfers, free schools, parental support) has potential for adolescent HIV prevention. We aimed to identify which social protection interventions are most effective and whether combined social protection has greater effects in South Africa. In this prospective longitudinal study, we interviewed 3516 adolescents aged 10-18 between 2009 and 2012. We sampled all homes with a resident adolescent in randomly selected census areas in 4 urban and rural sites in 2 South African provinces. We measured household receipt of 14 social protection interventions and incidence of HIV-risk behaviors. Using gender-disaggregated multivariate logistic regression and marginal effects analyses, we assessed respective contributions of interventions and potential combination effects. Child-focused grants, free schooling, school feeding, teacher support, and parental monitoring were independently associated with reduced HIV-risk behavior incidence (odds ratio: 0.10-0.69). Strong effects of combination social protection were shown, with cumulative reductions in HIV-risk behaviors. For example, girls' predicted past-year incidence of economically driven sex dropped from 11% with no interventions to 2% among those with a child grant, free school, and good parental monitoring. Similarly, girls' incidence of unprotected/casual sex or multiple partners dropped from 15% with no interventions to 10% with either parental monitoring or school feeding, and to 7% with both interventions. In real world, high-epidemic conditions, "combination social protection," shows strong HIV prevention effects for adolescents and may maximize prevention efforts.

FTIR microspectroscopy for rapid screening and monitoring of polyunsaturated fatty acid production in commercially valuable marine yeasts and protists.

PubMed

Vongsvivut, Jitraporn; Heraud, Philip; Gupta, Adarsha; Puri, Munish; McNaughton, Don; Barrow, Colin J

2013-10-21

The increase in polyunsaturated fatty acid (PUFA) consumption has prompted research into alternative resources other than fish oil. In this study, a new approach based on focal-plane-array Fourier transform infrared (FPA-FTIR) microspectroscopy and multivariate data analysis was developed for the characterisation of some marine microorganisms. Cell and lipid compositions in lipid-rich marine yeasts collected from the Australian coast were characterised in comparison to a commercially available PUFA-producing marine fungoid protist, thraustochytrid. Multivariate classification methods provided good discriminative accuracy evidenced from (i) separation of the yeasts from thraustochytrids and distinct spectral clusters among the yeasts that conformed well to their biological identities, and (ii) correct classification of yeasts from a totally independent set using cross-validation testing. The findings further indicated additional capability of the developed FPA-FTIR methodology, when combined with partial least squares regression (PLSR) analysis, for rapid monitoring of lipid production in one of the yeasts during the growth period, which was achieved at a high accuracy compared to the results obtained from the traditional lipid analysis based on gas chromatography. The developed FTIR-based approach when coupled to programmable withdrawal devices and a cytocentrifugation module would have strong potential as a novel online monitoring technology suited for bioprocessing applications and large-scale production.

Spatial and temporal variation of water quality of a segment of Marikina River using multivariate statistical methods.

PubMed

Chounlamany, Vanseng; Tanchuling, Maria Antonia; Inoue, Takanobu

2017-09-01

Payatas landfill in Quezon City, Philippines, releases leachate to the Marikina River through a creek. Multivariate statistical techniques were applied to study temporal and spatial variations in water quality of a segment of the Marikina River. The data set included 12 physico-chemical parameters for five monitoring stations over a year. Cluster analysis grouped the monitoring stations into four clusters and identified January-May as dry season and June-September as wet season. Principal components analysis showed that three latent factors are responsible for the data set explaining 83% of its total variance. The chemical oxygen demand, biochemical oxygen demand, total dissolved solids, Cl - and PO 4 3- are influenced by anthropogenic impact/eutrophication pollution from point sources. Total suspended solids, turbidity and SO 4 2- are influenced by rain and soil erosion. The highest state of pollution is at the Payatas creek outfall from March to May, whereas at downstream stations it is in May. The current study indicates that the river monitoring requires only four stations, nine water quality parameters and testing over three specific months of the year. The findings of this study imply that Payatas landfill requires a proper leachate collection and treatment system to reduce its impact on the Marikina River.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

FINGERPRINT ANALYSIS OF CONTAMINANT DATA: A FORENSIC TOOL FOR EVALUATING ENVIRONMENTAL CONTAMINATION

EPA Science Inventory

Several studies have been conducted on behalf of the U .S. Environmental Protection Agency (EPA) to identify detection monitoring parameters for specific industries.1,2,3,4,5 One outcome of these studies was the evolution of an empirical multi-variant contaminant fingerprinting p...

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

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

NASA Astrophysics Data System (ADS)

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

2005-07-01

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

Allogeneic stem cell transplantation provides durable disease control in poor-risk chronic lymphocytic leukemia: long-term clinical and MRD results of the German CLL Study Group CLL3X trial.

PubMed

Dreger, Peter; Döhner, Hartmut; Ritgen, Matthias; Böttcher, Sebastian; Busch, Raymonde; Dietrich, Sascha; Bunjes, Donald; Cohen, Sandra; Schubert, Jörg; Hegenbart, Ute; Beelen, Dietrich; Zeis, Matthias; Stadler, Michael; Hasenkamp, Justin; Uharek, Lutz; Scheid, Christof; Humpe, Andreas; Zenz, Thorsten; Winkler, Dirk; Hallek, Michael; Kneba, Michael; Schmitz, Norbert; Stilgenbauer, Stephan

2010-10-07

The purpose of this prospective multicenter phase 2 trial was to investigate the long-term outcome of reduced-intensity conditioning allogeneic stem cell transplantation (alloSCT) in patients with poor-risk chronic lymphocytic leukemia. Conditioning was fludarabine/ cyclophosphamide-based. Longitudinal quantitative monitoring of minimal residual disease (MRD) was performed centrally by MRD-flow or real-time quantitative polymerase chain reaction. One hundred eligible patients were enrolled, and 90 patients proceeded to alloSCT. With a median follow-up of 46 months (7-102 months), 4-year nonrelapse mortality, event-free survival (EFS) and overall survival (OS) were 23%, 42%, and 65%, respectively. Of 52 patients with MRD monitoring available, 27 (52%) were alive and MRD negative at 12 months after transplant. Four-year EFS of this subset was 89% with all event-free patients except for 2 being MRD negative at the most recent assessment. EFS was similar for all genetic subsets, including 17p deletion (17p-). In multivariate analyses, uncontrolled disease at alloSCT and in vivo T-cell depletion with alemtuzumab, but not 17p-, previous purine analogue refractoriness, or donor source (human leukocyte antigen-identical siblings or unrelated donors) had an adverse impact on EFS and OS. In conclusion, alloSCT for poor-risk chronic lymphocytic leukemia can result in long-term MRD-negative survival in up to one-half of the patients independent of the underlying genomic risk profile. This trial is registered at http://clinicaltrials.gov as NCT00281983.

A direct-gradient multivariate index of biotic condition

USGS Publications Warehouse

Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

2012-01-01

Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

Comparison of AC electronic monitoring and field data for estimating tolerance to Empoasca kraemeri (Homoptera: Cicadellidae) in common bean genotypes.

PubMed

Serrano, M S; Backus, E A; Cardona, C

2000-12-01

Two methods for estimating the tolerance of common bean genotypes to Empoasca kraemeri Ross & Moore were compared, using a yield trial carried out at Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia, versus stylet penetration tactics measured by AC electronic feeding monitors. A stylet penetration index was devised based on principal component scores of three penetration tactics identified (pulsing laceration, cell rupturing, and lancing sap ingestion), combined with knowledge of the hopperburn symptoms caused by each tactic. Tolerant genotypes, as classified by the CIAT yield index, showed significantly more unprotected yield and lower hopperburn scores than the susceptible control. They also induced performance of less pulsing laceration (the tactic considered most damaging to the plant), and more of the other two, mitigating tactics, especially cell rupturing. When index values were calculated for each genotype, stylet penetration index values matched those of the yield index for three out of five genotypes: two EMP-coded tolerant lines ('EMP 385' and 'EMP 392') and the susceptible control 'BAT 41'. Thus, for these three genotypes, all subsequent hoppereburn symptoms are predictable by the type of feeding behavior performed on them. 'Porrillo Sintético' and 'EMP 84', considered borderline genotypes by the yield index, were overestimated and underestimated respectively, by the stylet penetration index. We postulate that, for these two genotypes, plant physiological responses to feeding (either compensatory or heightened sensitivity, respectively) synergize with type of feeding performed to generate the overall hopperburn condition. This multivariate analysis of electronic monitoring data was successfully used to devise an index of resistance. The implications of using the stylet penetration index and the advantages of using electronic monitoring in a bean-breeding program are discussed.

Fluid Status in Peritoneal Dialysis Patients: The European Body Composition Monitoring (EuroBCM) Study Cohort

PubMed Central

Van Biesen, Wim; Williams, John D.; Covic, Adrian C.; Fan, Stanley; Claes, Kathleen; Lichodziejewska-Niemierko, Monika; Verger, Christian; Steiger, Jurg; Schoder, Volker; Wabel, Peter; Gauly, Adelheid; Himmele, Rainer

2011-01-01

Background Euvolemia is an important adequacy parameter in peritoneal dialysis (PD) patients. However, accurate tools to evaluate volume status in clinical practice and data on volume status in PD patients as compared to healthy population, and the associated factors, have not been available so far. Methods We used a bio-impedance spectroscopy device, the Body Composition Monitor (BCM) to assess volume status in a cross-sectional cohort of prevalent PD patients in different European countries. The results were compared to an age and gender matched healthy population. Results Only 40% out of 639 patients from 28 centres in 6 countries were normovolemic. Severe fluid overload was present in 25.2%. There was a wide scatter in the relation between blood pressure and volume status. In a multivariate analysis in the subgroup of patients from countries with unrestricted availability of all PD modalities and fluid types, older age, male gender, lower serum albumin, lower BMI, diabetes, higher systolic blood pressure, and use of at least one exchange per day with the highest hypertonic glucose were associated with higher relative tissue hydration. Neither urinary output nor ultrafiltration, PD fluid type or PD modality were retained in the model (total R2 of the model = 0.57). Conclusions The EuroBCM study demonstrates some interesting issues regarding volume status in PD. As in HD patients, hypervolemia is a frequent condition in PD patients and blood pressure can be a misleading clinical tool to evaluate volume status. To monitor fluid balance, not only fluid output but also dietary input should be considered. Close monitoring of volume status, a correct dialysis prescription adapted to the needs of the patient and dietary measures seem to be warranted to avoid hypervolemia. PMID:21390320

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Monitoring of pistachio (Pistacia Vera) ripening by high field nuclear magnetic resonance spectroscopy.

PubMed

Sciubba, Fabio; Avanzato, Damiano; Vaccaro, Angela; Capuani, Giorgio; Spagnoli, Mariangela; Di Cocco, Maria Enrica; Tzareva, Irina Nikolova; Delfini, Maurizio

2017-04-01

The metabolic profiling of pistachio (Pistacia vera) aqueous extracts from two different cultivars, namely 'Bianca' and 'Gloria', was monitored over the months from May to September employing high field NMR spectroscopy. A large number of water-soluble metabolites were assigned by means of 1D and 2D NMR experiments. The change in the metabolic profiles monitored over time allowed the pistachio development to be investigated. Specific temporal trends of amino acids, sugars, organic acids and other metabolites were observed and analysed by multivariate Partial Least Squares (PLS) analysis. Statistical analysis showed that while in the period from May to September there were few differences between the two cultivars, the ripening rate was different.

Linking multimetric and multivariate approaches to assess the ecological condition of streams.

PubMed

Collier, Kevin J

2009-10-01

Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

Testing a new application for TOPSIS: monitoring drought and wet periods in Iran

NASA Astrophysics Data System (ADS)

Roshan, Gholamreza; Ghanghermeh, AbdolAzim; Grab, Stefan W.

2018-01-01

Globally, droughts are a recurring major natural disaster owing to below normal precipitation, and are occasionally associated with high temperatures, which together negatively impact upon human health and social, economic, and cultural activities. Drought early warning and monitoring is thus essential for reducing such potential impacts on society. To this end, several experimental methods have previously been proposed for calculating drought, yet these are based almost entirely on precipitation alone. Here, for the first time, and in contrast to previous studies, we use seven climate parameters to establish drought/wet periods; these include: T min, T max, sunshine hours, relative humidity, average rainfall, number of rain days greater than 1 mm, and the ratio of total precipitation to number of days with precipitation, using the technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. To test the TOPSIS method for different climate zones, six sample stations representing a variety of different climate conditions were used by assigning weight changes to climate parameters, which are then applied to the model, together with multivariate regression analysis. For the six stations tested, model results indicate the lowest errors for Zabol station and maximum errors for Kermanshah. The validation techniques strongly support our proposed new method for calculating and rating drought/wet events using TOPSIS.

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

The Effects of Continuous Vs. Intermittent Self-Monitoring on the Duration and Magnitude of Behavior Change.

ERIC Educational Resources Information Center

Schayer, Laurel L.; Schroeder, Harold E.

Continuous self-monitoring (CSM) was compared with a demand characteristics control condition (non self-monitoring), with intermittent self-monitoring (ISM) and with another control condition. It was predicted that both self-monitoring conditions would produce effects over and above the demand characteristics inherent in the self-monitoring…

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

2017-04-01

The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining vegetation and hydro-meteorological drought indices for the assessment of cereal yield. Moreover, the present study will provide some guidance on user's decision making process in agricultural practices in the IP, assisting farmers in deciding whether to purchase crop insurance. Acknowledgements: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014). Ana Russo thanks FCT for granted support (SFRH/BPD/99757/2014). Andreia Ribeiro also thanks FCT for grant PD/BD/114481/2016.

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

NIR monitoring of in-service wood structures

Treesearch

Michela Zanetti; Timothy G. Rials; Douglas Rammer

2005-01-01

Near infrared spectroscopy (NIRS) was used to study a set of Southern Yellow Pine boards exposed to natural weathering for different periods of exposure time. This non-destructive spectroscopic technique is a very powerful tool to predict the weathering of wood when used in combination with multivariate analysis (Principal Component Analysis, PCA, and Projection to...

Determinants of Anabolic-Androgenic Steroid Risk Perceptions in Youth Populations: A Multivariate Analysis

ERIC Educational Resources Information Center

Denham, Bryan E.

2009-01-01

Grounded conceptually in social cognitive theory, this research examines how personal, behavioral, and environmental factors are associated with risk perceptions of anabolic-androgenic steroids. Ordinal logistic regression and logit log-linear models applied to data gathered from high-school seniors (N = 2,160) in the 2005 Monitoring the Future…

Monitoring Human Development Goals: A Straightforward (Bayesian) Methodology for Cross-National Indices

ERIC Educational Resources Information Center

Abayomi, Kobi; Pizarro, Gonzalo

2013-01-01

We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country…

Fringing coral reef condition decline: assembling the puzzle of human impact associated to coastal development.

NASA Astrophysics Data System (ADS)

Garza-Perez, J. R.; Lopez-Patoni, A.; Naranjo-Garcia, M. J.

2014-12-01

Coral cover at Akumal fringing coral reef decreased 50% in a 13 yr. period, while the adjacent coastal zone increased its human-modified surface (associated to urban-tourist development) in 192%. In the same period, the number of local residents only increased 20% (1088 to1362) but the visitors did in 50% from ca. 200,000 to ca. 300,000. In this coastal zone, the phreatic acts as a storage of nutrients and pollutants from sources related to human activity, thus having a chronic run-off towards the reef, with acute episodes during the rainy season, specially during the anomalous rainy season of 2013. Using videotransects for monitoring the benthic reef components, changes were detected: from 2000 to 2013 the algae cover increased 166%, the reef condition and the reef structure indexes decreased in 50%, and coral diseases incidence increased 25% after a spike increment of 150% in 2010. The role of anthropogenic-stress indicators (population, modified land area, nutrients) was explored along reef condition indicators (reef structure and diversity indexes, topographic complexity, benthic cover and coral diseases incidence) via spatial analysis and multivariate statistics. Spatial patterns of the change in reef condition derived from high-resolution satellite imagery also provided insight for the stressors analysis and their relationships along the study period. Stress indicators (land-modified area and population) are correlated to decreases in coral cover and in reef structure. Direct stressors as sedimentation, nutrients and pollutants seem to be related to the decrease in overall reef condition, although time-series data is lacking; the contextual interpretation of their effects, paired with benthic condition characteristics suggest a strong relationship between these stressors and the decrease in the condition of the reef.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Quality-by-design III: application of near-infrared spectroscopy to monitor roller compaction in-process and product quality attributes of immediate release tablets.

PubMed

Kona, Ravikanth; Fahmy, Raafat M; Claycamp, Gregg; Polli, James E; Martinez, Marilyn; Hoag, Stephen W

2015-02-01

The objective of this study is to use near-infrared spectroscopy (NIRS) coupled with multivariate chemometric models to monitor granule and tablet quality attributes in the formulation development and manufacturing of ciprofloxacin hydrochloride (CIP) immediate release tablets. Critical roller compaction process parameters, compression force (CFt), and formulation variables identified from our earlier studies were evaluated in more detail. Multivariate principal component analysis (PCA) and partial least square (PLS) models were developed during the development stage and used as a control tool to predict the quality of granules and tablets. Validated models were used to monitor and control batches manufactured at different sites to assess their robustness to change. The results showed that roll pressure (RP) and CFt played a critical role in the quality of the granules and the finished product within the range tested. Replacing binder source did not statistically influence the quality attributes of the granules and tablets. However, lubricant type has significantly impacted the granule size. Blend uniformity, crushing force, disintegration time during the manufacturing was predicted using validated PLS regression models with acceptable standard error of prediction (SEP) values, whereas the models resulted in higher SEP for batches obtained from different manufacturing site. From this study, we were able to identify critical factors which could impact the quality attributes of the CIP IR tablets. In summary, we demonstrated the ability of near-infrared spectroscopy coupled with chemometrics as a powerful tool to monitor critical quality attributes (CQA) identified during formulation development.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

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

Mayer, B. P.; Valdez, C. A.; DeHope, A. J.

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduledmore » precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.« less

Optical Spectroscopy and Multivariate Analysis for Biodosimetry and Monitoring of Radiation Injury to the Skin

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

Levitskaia, Tatiana G.; Bryan, Samuel A.; Creim, Jeffrey A.

2012-08-01

In the event of an intentional or accidental release of ionizing radiation in a densely populated area, timely assessment and triage of the general population for the radiation exposure is critical. In particular, a significant number of the victims may sustain cutaneous radiation injury, which increases the mortality and worsens the overall prognosis of the victims suffered from combined thermal/mechanical and radiation trauma. Diagnosis of the cutaneous radiation injury is challenging, and established methods largely rely on visual manifestations, presence of the skin contamination, and a high degree of recall by the victim. Availability of a high throughput non-invasive inmore » vivo biodosimetry tool for assessment of the radiation exposure of the skin is of particular importance for the timely diagnosis of the cutaneous injury. In the reported investigation, we have tested the potential of an optical reflectance spectroscopy for the evaluation of the radiation injury to the skin. This is technically attractive because optical spectroscopy relies on well-established and routinely used for various applications instrumentation, one example being pulse oximetry which uses selected wavelengths for the quantification of the blood oxygenation. Our method relies on a broad spectral region ranging from the locally absorbed, shallow-penetrating ultraviolet and visible (250 to 800 nm) to more deeply penetrating near-Infrared (800 – 1600 nm) light for the monitoring of multiple physiological changes in the skin upon irradiation. Chemometrics is a multivariate methodology that allows the information from entire spectral region to be used to generate predictive regression models. In this report we demonstrate that simple spectroscopic method, such as the optical reflectance spectroscopy, in combination with multivariate data analysis, offers the promise of rapid and non-invasive in vivo diagnosis and monitoring of the cutaneous radiation exposure, and is able accurately predict the dose and time of exposure in the rodent model.« less

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (Plectropomus leopardus).

PubMed

Mekuchi, Miyuki; Asakura, Taiga; Sakata, Kenji; Yamaguchi, Tomofumi; Teruya, Kazuhisa; Kikuchi, Jun

2018-01-01

Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system. Although our understanding of microbiota in fish gut has increased, comprehensive studies examining fish microbiota and host metabolism remain limited. Here, we investigated the microbiota and host metabolism in the coral leopard grouper, which is traded in Asian markets as a superior fish and has begun to be produced via aquaculture. We initially examined the structural changes of the gut microbiota using next-generation sequencing and found that the composition of microbiota changed between fasting and feeding conditions. The dominant phyla were Proteobacteria in fasting and Firmicutes in feeding; interchanging the dominant bacteria required 12 hours. Moreover, microbiota diversity was higher under feeding conditions than under fasting conditions. Multivariate analysis revealed that Proteobacteria are the key bacteria in fasting and Firmicutes and Fusobacteria are the key bacteria in feeding. Subsequently, we estimated microbiota functional capacity. Microbiota functional structure was relatively stable throughout the experiment; however, individual function activity changed according to feeding conditions. Taken together, these findings indicate that the gut microbiota could be a key factor to understanding fish feeding conditions and play a role in interactions with host metabolism. In addition, the composition of microbiota in ambient seawater directly affects the fish; therefore, it is important to monitor the microbiota in rearing tanks and seawater circulating systems.

Influence of pasteurization, brining conditions and production environment on the microbiota of artisan Gouda-type cheeses.

PubMed

Van Hoorde, Koenraad; Heyndrickx, Marc; Vandamme, Peter; Huys, Geert

2010-05-01

To monitor the effect of the indigenous milk microbiota and of technological and environmental parameters on the microbiota established in ripened cheese, the diversity and dynamics of the predominant microbial communities in artisan Gouda-type cheeses produced under different conditions was studied. A total of 22 cheese types differing in milk source, milk treatment, production environment and brining conditions were analyzed by PCR-denaturing gradient gel electrophoresis (PCR-DGGE) using total DNA extracts as well as DNA extracted from culturable fractions. Through band position analysis and band sequencing, the majority of DGGE bands could be attributed to lactic acid bacteria (LAB), although a few bands also belonged to staphylococci and gamma-Proteobacteria. Aided by principal component analysis (PCA) and multivariate analysis of variance (MANOVA), cheeses produced at different locations could clearly be differentiated. The same approach also allowed to distinguish raw and pasteurized milk cheeses, the former showing a more diverse microbiota in terms of a higher species richness and number of DGGE bands. No substantial differences were found between cheeses brined at two different locations. In conclusion, the combined PCR-DGGE approach relying on both total DNA extracts and culturable fractions proved its value for analyzing the effect of technological and environmental parameters on the diversity and dynamics of the microbiota in Gouda-type cheeses. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Electronic fetal heart rate monitoring and its relationship to neonatal and infant mortality in the United States.

PubMed

Chen, Han-Yang; Chauhan, Suneet P; Ananth, Cande V; Vintzileos, Anthony M; Abuhamad, Alfred Z

2011-06-01

To examine the association between electronic fetal heart rate monitoring and neonatal and infant mortality, as well as neonatal morbidity. We used the United States 2004 linked birth and infant death data. Multivariable log-binomial regression models were fitted to estimate risk ratio for association between electronic fetal heart rate monitoring and mortality, while adjusting for potential confounders. In 2004, 89% of singleton pregnancies had electronic fetal heart rate monitoring. Electronic fetal heart rate monitoring was associated with significantly lower infant mortality (adjusted relative risk, 0.75); this was mainly driven by the lower risk of early neonatal mortality (adjusted relative risk, 0.50). In low-risk pregnancies, electronic fetal heart rate monitoring was associated with decreased risk for Apgar scores <4 at 5 minutes (relative risk, 0.54); in high-risk pregnancies, with decreased risk of neonatal seizures (relative risk, 0.65). In the United States, the use of electronic fetal heart rate monitoring was associated with a substantial decrease in early neonatal mortality and morbidity that lowered infant mortality. Copyright © 2011 Mosby, Inc. All rights reserved.

Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using Labview

NASA Astrophysics Data System (ADS)

Kong, Changduk; Kim, Keonwoo

2011-12-01

Recently, an aero gas turbine health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which occurs in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the online condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measured engine performance data and base performance results calculated by base engine performance model. This work aims to develop a GUI type on-line condition monitoring program for the PT6A-67 turboprop engine of a high altitude and long endurance operation UAV using LabVIEW. The base engine performance of the on-line condition monitoring program is simulated using component maps inversely generated from the limited performance deck data provided by engine manufacturer. The base engine performance simulation program is evaluated because analysis results by this program agree well with the performance deck data. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between clean engine performance results calculated by the base performance simulation program and measured engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed online monitoring system. For user friendly purpose, all monitoring program are coded by LabVIEW, and monitoring examples are demonstrated using the proposed GUI type on-condition monitoring program.

A methodological approach to study the stability of selected watercolours for painting reintegration, through reflectance spectrophotometry, Fourier transform infrared spectroscopy and hyperspectral imaging

NASA Astrophysics Data System (ADS)

Pelosi, Claudia; Capobianco, Giuseppe; Agresti, Giorgia; Bonifazi, Giuseppe; Morresi, Fabio; Rossi, Sara; Santamaria, Ulderico; Serranti, Silvia

2018-06-01

The aim of this work is to investigate the stability to simulated solar radiation of some paintings samples through a new methodological approach adopting non-invasive spectroscopic techniques. In particular, commercial watercolours and iron oxide based pigments were used, these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Reflectance spectrophotometry in the visible range and Hyperspectral Imaging in the short wave infrared were chosen as non-invasive techniques for evaluation the stability to irradiation of the chosen pigments. These were studied before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the chosen techniques in identifying the variations on paint layers, induced by photo-degradation, before they could be observed by eye. Furthermore a supervised classification method for monitoring the painted surface changes adopting a multivariate approach was successfully applied.

Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies

PubMed Central

Potyrailo, Radislav A.; Bonam, Ravi K.; Hartley, John G.; Starkey, Timothy A.; Vukusic, Peter; Vasudev, Milana; Bunning, Timothy; Naik, Rajesh R.; Tang, Zhexiong; Palacios, Manuel A.; Larsen, Michael; Le Tarte, Laurie A.; Grande, James C.; Zhong, Sheng; Deng, Tao

2015-01-01

Combining vapour sensors into arrays is an accepted compromise to mitigate poor selectivity of conventional sensors. Here we show individual nanofabricated sensors that not only selectively detect separate vapours in pristine conditions but also quantify these vapours in mixtures, and when blended with a variable moisture background. Our sensor design is inspired by the iridescent nanostructure and gradient surface chemistry of Morpho butterflies and involves physical and chemical design criteria. The physical design involves optical interference and diffraction on the fabricated periodic nanostructures and uses optical loss in the nanostructure to enhance the spectral diversity of reflectance. The chemical design uses spatially controlled nanostructure functionalization. Thus, while quantitation of analytes in the presence of variable backgrounds is challenging for most sensor arrays, we achieve this goal using individual multivariable sensors. These colorimetric sensors can be tuned for numerous vapour sensing scenarios in confined areas or as individual nodes for distributed monitoring. PMID:26324320

Self-Monitoring Using Continuous Glucose Monitors with Real-Time Feedback Improves Exercise Adherence in Individuals with Impaired Blood Glucose: A Pilot Study.

PubMed

Bailey, Kaitlyn J; Little, Jonathan P; Jung, Mary E

2016-03-01

Exercise helps individuals with prediabetes or type 2 diabetes (T2D) manage their blood glucose (BG); however, exercise adherence in this population is dismal. In this pilot study we tested the efficacy of a self-monitoring group-based intervention using continuous glucose monitors (CGMs) at increasing exercise adherence in individuals with impaired BG. Thirteen participants with prediabetes or T2D were randomized to an 8-week standard care exercise program (CON condition) (n = 7) or self-monitoring exercise intervention (SM condition) (n = 6). Participants in the SM condition were taught how to self-monitor their exercise and BG, to goal set, and to use CGM to observe how exercise influences BG. We hypothesized that compared with the CON condition, using a real-time CGM would facilitate self-monitoring behavior, resulting in increased exercise adherence. Repeated-measures analysis of variance revealed significant Condition × Time interactions for self-monitoring (P < 0.01), goal setting (P = 0.01), and self-efficacy to self-monitor (P = 0.01), such that the SM condition showed greater increases in these outcomes immediately after the program and at the 1-month follow-up compared with the CON condition. The SM condition had higher program attendance rates (P = 0.03), and a greater proportion of participants reregistered for additional exercise programs (P = 0.048) compared with the CON condition. Participants in both conditions experienced improvements in health-related quality of life, waist circumference, and fitness (P values <0.05). These findings provide promising initial support for the use of a real-time CGM to foster self-monitoring and exercise behavior in individuals living with prediabetes or T2D.

Wetlands monitoring - hydrological conditions and water quality in selected transects of Biebrza National Park.

NASA Astrophysics Data System (ADS)

Stelmaszczyk, Mateusz; Okruszko, Tomasz

2010-05-01

Water Framework Directive (WFD) obligates Member States to prevent further deterioration as well as to protect and enhance the status of aquatic ecosystems and wetlands. In order to fulfill one of the WFD objectives - to keep wetlands in good surface water and groundwater status (determined by good ecological, chemical and quantitative status) it is necessary to specify most favourable conditions for them. In that case monitoring of factors responsible for wetlands status in natural areas is a key issue. Further, achieved knowledge of existing relations in ecosystems can be implemented in protection and restoration projects. There are a number of factors influencing diversity of habitats responsible for developing different wetland ecosystems and their sustaining in good ecological status. It's believed that among significant factors such as hydrological conditions, water quality, nutrient availability in the soil, pH value and management (e.g. grazing, mowing) the hydrological conditions are the most important. In presented work authors concentrated on hydrological conditions and water quality and theirs influence on wetland vegetation of Biebrza National Park (BNP). BNP located north-east part of Poland is recognized by many scientist as a unique undisturbed wetland reference area. Five transects located in different basins of BNP were chosen. Transects consist of piezometers in which the water table levels and water quality were measured. Analysis of electroconductivity (EC), alkalinity (HCO3-) and pH were done directly in the field. In the laboratory anions (NO3-, PO43-, Cl-, SO42-) and cations (NH4+, Ca2+, Mg2+, Br+, Li+, Na+, K+) concentration was determined using High Performance Liquid Chromatography (HPLC). D-divers, electronic devices to permanent measurement of groundwater level changes were located in some of the piezometers. Piezometers were located in the sites characterized by different hydrological conditions, from groundwater fed to river fed areas. Studied locations were covered mainly by Magnocaricion vegetation (e.g. Caricetum gracilis and Caricetum elatae), Molinio-Arrhenatheretea vegetation (Molinietum caeruleae), and Scheuchzerio-Caricetea nigrae vegetation (e.g. Caricetum lasiocarpae). In presented work authors show results of water quality measurements and monitoring of hydrological conditions, characterized by changes of groundwater table, period and size of inundation. During six years long monitoring period (2004 - 2009 hydrological years) there were observed high diversification of groundwater and surface water levels among locations. They fluctuate in some places from very low groundwater levels, observed in late summer and in early autumn (over 1 m beneath the ground), to levels reaching surface of the ground or laying nearly below it, occurring in winter and spring. There are also places where quite high inundations in winter and spring are observed. Collected chemical and hydrological data were statistically analyzed using STATISTICA 8 software with a use of one of the multivariate analysis - Principal Component Analysis (PCA) method. Owing to the usage of PCA analysis it was possible to define most important parameters characterizing habitats were occurs selected vegetation. The impact of hydrological conditions (presented as a main factor) on forming particular wetland plant communities can be discussed. Authors determine that some other factors (e.g. management) can be more responsible for occurrence of particular plant communities and their sustaining in good status in specific locations.

On the use of temperature for online condition monitoring of geared systems - A review

NASA Astrophysics Data System (ADS)

Touret, T.; Changenet, C.; Ville, F.; Lalmi, M.; Becquerelle, S.

2018-02-01

Gear unit condition monitoring is a key factor for mechanical system reliability management. When they are subjected to failure, gears and bearings may generate excessive vibration, debris and heat. Vibratory, acoustic or debris analyses are proven approaches to perform condition monitoring. An alternative to those methods is to use temperature as a condition indicator to detect gearbox failure. The review focuses on condition monitoring studies which use this thermal approach. According to the failure type and the measurement method, it exists a distinction whether it is contact (e.g. thermocouple) or non-contact temperature sensor (e.g. thermography). Capabilities and limitations of this approach are discussed. It is shown that the use of temperature for condition monitoring has a clear potential as an alternative to vibratory or acoustic health monitoring.

[Change settings for visual analyzer of child users of mobile communication: longitudinal study].

PubMed

Khorseva, N I; Grigor'ev, Iu G; Gorbunova, N V

2014-01-01

The paper represents theresults of longitudinal monitoring of the changes in the parameters of simple visual-motor reaction, the visual acuity and the rate of the visual discrimination in the child users of mobile communication, which indicate the multivariability of the possible effects of radiation from mobile phones on the auditory system of children.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

USDA-ARS?s Scientific Manuscript database

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and ma...

Language and Cognitive Predictors of Text Comprehension: Evidence from Multivariate Analysis

ERIC Educational Resources Information Center

Kim, Young-Suk

2015-01-01

Using data from children in South Korea (N = 145, M[subscript age] = 6.08), it was determined how low-level language and cognitive skills (vocabulary, syntactic knowledge, and working memory) and high-level cognitive skills (comprehension monitoring and theory of mind [ToM]) are related to listening comprehension and whether listening…

Diagonal dominance for the multivariable Nyquist array using function minimization

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1977-01-01

A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.

Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis.

PubMed

Mercuri, A; Pagliari, M; Baxevanis, F; Fares, R; Fotaki, N

2017-02-25

In this study the selection of in vivo predictive in vitro dissolution experimental set-ups using a multivariate analysis approach, in line with the Quality by Design (QbD) principles, is explored. The dissolution variables selected using a design of experiments (DoE) were the dissolution apparatus [USP1 apparatus (basket) and USP2 apparatus (paddle)], the rotational speed of the basket/or paddle, the operator conditions (dissolution apparatus brand and operator), the volume, the pH, and the ethanol content of the dissolution medium. The dissolution profiles of two nifedipine capsules (poorly soluble compound), under conditions mimicking the intake of the capsules with i. water, ii. orange juice and iii. an alcoholic drink (orange juice and ethanol) were analysed using multiple linear regression (MLR). Optimised dissolution set-ups, generated based on the mathematical model obtained via MLR, were used to build predicted in vitro-in vivo correlations (IVIVC). IVIVC could be achieved using physiologically relevant in vitro conditions mimicking the intake of the capsules with an alcoholic drink (orange juice and ethanol). The multivariate analysis revealed that the concentration of ethanol used in the in vitro dissolution experiments (47% v/v) can be lowered to less than 20% v/v, reflecting recently found physiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

A multivariate assessment of changes in wetland habitat for waterbirds at Moosehorn National Wildlife Refuge, Maine, USA

USGS Publications Warehouse

Hierl, L.A.; Loftin, C.S.; Longcore, J.R.; McAuley, D.G.; Urban, D.L.

2007-01-01

We assessed changes in vegetative structure of 49 impoundments at Moosehorn National Wildlife Refuge (MNWR), Maine, USA, between the periods 1984-1985 to 2002 with a multivariate, adaptive approach that may be useful in a variety of wetland and other habitat management situations. We used Mahalanobis Distance (MD) analysis to classify the refuge?s wetlands as poor or good waterbird habitat based on five variables: percent emergent vegetation, percent shrub, percent open water, relative richness of vegetative types, and an interspersion juxtaposition index that measures adjacency of vegetation patches. Mahalanobis Distance is a multivariate statistic that examines whether a particular data point is an outlier or a member of a data cluster while accounting for correlations among inputs. For each wetland, we used MD analysis to quantify a distance from a reference condition defined a priori by habitat conditions measured in MNWR wetlands used by waterbirds. Twenty-five wetlands declined in quality between the two periods, whereas 23 wetlands improved. We identified specific wetland characteristics that may be modified to improve habitat conditions for waterbirds. The MD analysis seems ideal for instituting an adaptive wetland management approach because metrics can be easily added or removed, ranges of target habitat conditions can be defined by field-collected data, and the analysis can identify priorities for single or multiple management objectives.

Evaluating rehabilitation efforts following the Milford Flat Fire: successes, failures, and controlling factors

USGS Publications Warehouse

Duniway, Michael C.; Palmquist, Emily C.; Miller, Mark E.

2015-01-01

Uncontrolled wildfire in arid and semiarid ecosystems has become an increasing concern in recent decades. Active rehabilitation of fire-affected areas is often quickly initiated to minimize long-term ecosystem damage. However, the complex soil-geomorphic-vegetation patterns and low and variable moisture conditions in these regions makes restoration challenging. To further inform these post-fire management decisions, we present results from 5 years of vegetation and sediment flux monitoring following the Milford Flat Fire in west-central Utah, USA. Our sampling design includes monitoring plots in areas not burned, areas burned but where no rehabilitation was attempted, and burned areas where various rehabilitation approaches were implemented. At each of the 25 plots, vegetation cover and composition data were collected annually, and wind-driven sediment flux was measured using passive dust traps. To evaluate effectiveness of post-fire rehabilitation treatments in establishing desired species and limiting dominance of undesired species, we analyzed the temporal response of individual species and functional groups as well as community-level multivariate responses. The warm and dry conditions that persisted for approximately 12 months post-treatment, coupled with the surface disturbing rehabilitation approaches used, resulted in near-surface dust fluxes several orders of magnitude higher in treated areas than in unburned or burned areas where no rehabilitation occurred. These dry conditions and high surface sediment flux limited the establishment of seeded species in rehabilitation areas for nearly 3 years. Post-fire rehabilitation did not limit dominance by invasive annual species of concern. Perennial species composition in the areas burned but not subject to post-fire rehabilitation was relatively similar to unburned throughout the study period. In contrast, the burned plots where rehabilitation was attempted were characterized by no (<3%) perennial cover or, in response to moister conditions, seeded forage species. These results suggest the post-fire rehabilitation efforts conducted in the lower elevation regions affected by the Milford Flat Fire were not generally successful. Though dry conditions are likely to blame for the lack of success, the low and variable precipitation characteristic of these regions suggest future post-fire rehabilitation decisions must assume that precipitation is going to be insufficient and plan rehabilitation efforts that are resilient to dry conditions.

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

PubMed

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants. Copyright © 2018. Published by Elsevier Ltd.

An approach to online network monitoring using clustered patterns

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

Kim, Jinoh; Sim, Alex; Suh, Sang C.

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regardmore » to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.« less

Influence of social connectedness, communication and monitoring on adolescent sexual activity in Ghana.

PubMed

Kumi-Kyereme, Akwasi; Awusabo-Asare, Kofi; Biddlecom, Ann; Tanle, Augustine

2007-12-01

This paper examines connectedness to, communication with and monitoring of unmarried adolescents in Ghana by parents, other adults, friends and key social institutions and the roles these groups play with respect to adolescent sexual activity. The paper draws on 2004 nationally-representative survey data and qualitative evidence from focus group discussions and in-depth interviews with adolescents in 2003. Adolescents show high levels of connectedness to family, adults, friends, school and religious groups. High levels of adult monitoring are also observed, but communication with family about sex-related matters was not as high as with non-family members. The qualitative data highlight gender differences in communication. Multivariate analysis of survey data shows a strong negative relationship between parental monitoring and recent sexual activity for males and females, and limited effects of communication. Creating a supportive environment and showing interest in the welfare of adolescents appear to promote positive sexual and reproductive health outcomes.

An approach to online network monitoring using clustered patterns

DOE PAGES

Kim, Jinoh; Sim, Alex; Suh, Sang C.; ...

2017-03-13

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regardmore » to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.« less

A multivariate test of disease risk reveals conditions leading to disease amplification.

PubMed

Halliday, Fletcher W; Heckman, Robert W; Wilfahrt, Peter A; Mitchell, Charles E

2017-10-25

Theory predicts that increasing biodiversity will dilute the risk of infectious diseases under certain conditions and will amplify disease risk under others. Yet, few empirical studies demonstrate amplification. This contrast may occur because few studies have considered the multivariate nature of disease risk, which includes richness and abundance of parasites with different transmission modes. By combining a multivariate statistical model developed for biodiversity-ecosystem-multifunctionality with an extensive field manipulation of host (plant) richness, composition and resource supply to hosts, we reveal that (i) host richness alone could not explain most changes in disease risk, and (ii) shifting host composition allowed disease amplification, depending on parasite transmission mode. Specifically, as predicted from theory, the effect of host diversity on parasite abundance differed for microbes (more density-dependent transmission) and insects (more frequency-dependent transmission). Host diversity did not influence microbial parasite abundance, but nearly doubled insect parasite abundance, and this amplification effect was attributable to variation in host composition. Parasite richness was reduced by resource addition, but only in species-rich host communities. Overall, this study demonstrates that multiple drivers, related to both host community and parasite characteristics, can influence disease risk. Furthermore, it provides a framework for evaluating multivariate disease risk in other systems. © 2017 The Author(s).

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Technology review: prototyping platforms for monitoring ambient conditions.

PubMed

Afolaranmi, Samuel Olaiya; Ramis Ferrer, Borja; Martinez Lastra, Jose Luis

2018-05-08

The monitoring of ambient conditions in indoor spaces is very essential owing to the amount of time spent indoors. Specifically, the monitoring of air quality is significant because contaminated air affects the health, comfort and productivity of occupants. This research work presents a technology review of prototyping platforms for monitoring ambient conditions in indoor spaces. It involves the research on sensors (for CO 2 , air quality and ambient conditions), IoT platforms, and novel and commercial prototyping platforms. The ultimate objective of this review is to enable the easy identification, selection and utilisation of the technologies best suited for monitoring ambient conditions in indoor spaces. Following the review, it is recommended to use metal oxide sensors, optical sensors and electrochemical sensors for IAQ monitoring (including NDIR sensors for CO 2 monitoring), Raspberry Pi for data processing, ZigBee and Wi-Fi for data communication, and ThingSpeak IoT platform for data storage, analysis and visualisation.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

A fully automated health-care monitoring at home without attachment of any biological sensors and its clinical evaluation.

PubMed

Motoi, Kosuke; Ogawa, Mitsuhiro; Ueno, Hiroshi; Kuwae, Yutaka; Ikarashi, Akira; Yuji, Tadahiko; Higashi, Yuji; Tanaka, Shinobu; Fujimoto, Toshiro; Asanoi, Hidetsugu; Yamakoshi, Ken-ichi

2009-01-01

Daily monitoring of health condition is important for an effective scheme for early diagnosis, treatment and prevention of lifestyle-related diseases such as adiposis, diabetes, cardiovascular diseases and other diseases. Commercially available devices for health care monitoring at home are cumbersome in terms of self-attachment of biological sensors and self-operation of the devices. From this viewpoint, we have been developing a non-conscious physiological monitor installed in a bath, a lavatory, and a bed for home health care and evaluated its measurement accuracy by simultaneous recordings of a biological sensors directly attached to the body surface. In order to investigate its applicability to health condition monitoring, we have further developed a new monitoring system which can automatically monitor and store the health condition data. In this study, by evaluation on 3 patients with cardiac infarct or sleep apnea syndrome, patients' health condition such as body and excretion weight in the toilet and apnea and hypopnea during sleeping were successfully monitored, indicating that the system appears useful for monitoring the health condition during daily living.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

PubMed

Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

2015-01-01

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

PubMed Central

Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

2014-01-01

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

PubMed

Varekar, Vikas; Karmakar, Subhankar; Jha, Ramakar

2016-02-01

The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders approach outperforms FA/PCA when limited water quality and extensive watershed information is available. The available water quality dataset is limited and FA/PCA-based approach fails to identify monitoring locations with higher variation, as these multivariate statistical approaches are data-driven. The priority/hierarchy and number of sampling sites designed by modified Sanders approach are well justified by the land use practices and observed river basin characteristics of the study area.

Depressive symptoms in nonresident african american fathers and involvement with their sons.

PubMed

Davis, R Neal; Caldwell, Cleopatra Howard; Clark, Sarah J; Davis, Matthew M

2009-12-01

Our objective was to determine whether paternal depressive symptoms were associated with less father involvement among African American fathers not living with their children (ie, nonresident fathers). We analyzed survey data for 345 fathers enrolled in a program for nonresident African American fathers and their preteen sons. Father involvement included measures of contact, closeness, monitoring, communication, and conflict. We used bivariate analyses and multivariate logistic regression analysis to examine associations between father involvement and depressive symptoms. Thirty-six percent of fathers reported moderate depressive symptoms, and 11% reported severe depressive symptoms. In bivariate analyses, depressive symptoms were associated with less contact, less closeness, low monitoring, and increased conflict. In multivariate analyses controlling for basic demographic features, fathers with moderate depressive symptoms were more likely to have less contact (adjusted odds ratio: 1.7 [95% confidence interval: 1.1-2.8]), less closeness (adjusted odds ratio: 2.1 [95% confidence interval: 1.3-3.5]), low monitoring (adjusted odds ratio: 2.7 [95% confidence interval: 1.4-5.2]), and high conflict (adjusted odds ratio: 2.1 [95% confidence interval: 1.2-3.6]). Fathers with severe depressive symptoms also were more likely to have less contact (adjusted odds ratio: 3.1 [95% confidence interval: 1.4-7.2]), less closeness (adjusted odds ratio: 2.6 [95% confidence interval: 1.2-5.7]), low monitoring (adjusted odds ratio: 2.8 [95% confidence interval: 1.1-7.1]), and high conflict (adjusted odds ratio: 2.6 [95% confidence interval: 1.1-5.9]). Paternal depressive symptoms may be an important, but modifiable, barrier for nonresident African American fathers willing to be more involved with their children.

Oxidation management of white wines using cyclic voltammetry and multivariate process monitoring.

PubMed

Martins, Rui C; Oliveira, Raquel; Bento, Fatima; Geraldo, Dulce; Lopes, Vitor V; Guedes de Pinho, Paula; Oliveira, Carla M; Silva Ferreira, Antonio C

2008-12-24

The development of a fingerprinting strategy capable to evaluate the "oxidation status" of white wines based on cyclic voltammetry is proposed here. It is known that the levels of specific antioxidants and redox mechanisms may be evaluated by cyclic voltammetry. This electrochemical technique was applied on two sets of samples. One group was composed of normal aged white wines and a second group obtained from a white wine forced aging protocol with different oxygen, SO(2), pH, and temperature regimens. A study of antioxidant additions, namely ascorbic acid, was also made in order to establish a statistical link between voltammogram fingerprints and chemical antioxidant substances. It was observed that the oxidation curve presented typical features, which enables sample discrimination according to age, oxygen consumption, and antioxidant additions. In fact, it was possible to place the results into four significant orthogonal directions, compressing 99.8% of nonrandom features. Attempts were made to make voltammogram fingerprinting a tool for monitoring oxidation management. For this purpose, a supervised multivariate control chart was developed using a control sample as reference. When white wines are plotted onto the chart, it is possible to monitor the oxidation status and to diagnose the effects of oxygen regimes and antioxidant activity. Finally, quantification of substances implicated in the oxidation process as reagents (antioxidants) and products (off-flavors) was tried using a supervised algorithmic the partial least square regression analysis. Good correlations (r > 0.93) were observed for ascorbic acid, Folin-Ciocalteu index, total SO(2), methional, and phenylacetaldehyde. These results show that cyclic voltammetry fingerprinting can be used to monitor and diagnose the effects of wine oxidation.

Fire Effects at the Tundra-Boreal Ecotone in Interior Alaska

NASA Astrophysics Data System (ADS)

Howard, B. K.; Mack, M. C.; Johnstone, J. F.; Walker, X. J.; Roland, C.

2016-12-01

Climate warming in northern latitudes has led to an intensification of disturbance by wildfire. Little is known about the effects of fire on tundra vegetation. Changes in vegetation composition could have important implications for carbon cycling , and may feedback positively or negatively to future climate change (Randerson et al., 2006). Our study utilizes extensive pre-fire ecological data collected by the National Park Service (NPS) Inventory and Monitoring (I&M) program to assess the prefire conditions important in driving successional pathways within Denali National Park and Preserve. In 2013, the East Toklat fire burned 30,000 acres of tussock tundra and mixed white and black spruce forest at a high severity, which encompassed 50 NPS plots that were originally monitored in 2003. Our sampling occurred the summer of 2016 following the same NPS protocols to assess post-fire vegetation composition. In addition, we conducted a seeding experiment using locally collected white and black spruce seed to assess natural and potential tree regeneration in unburned and post fire environments. Seed traps were established along our transects to assess seed rain. A multivariate approach will be used to assess post-fire community dynamics and future field seasons will address tree germination and survival rates. These data will then be coupled with pre and post-fire ecological data to parse out important factors driving secondary succession.

Weathering steel as a potential source for metal contamination: Metal dissolution during 3-year of field exposure in a urban coastal site.

PubMed

Raffo, Simona; Vassura, Ivano; Chiavari, Cristina; Martini, Carla; Bignozzi, Maria C; Passarini, Fabrizio; Bernardi, Elena

2016-06-01

Surface and building runoff can significantly contribute to the total metal loading in urban runoff waters, with potential adverse effects on the receiving ecosystems. The present paper analyses the corrosion-induced metal dissolution (Fe, Mn, Cr, Ni, Cu) from weathering steel (Cor-Ten A) with or without artificial patinas, exposed for 3 years in unsheltered conditions at a marine urban site (Rimini, Italy). The influence of environmental parameters, atmospheric pollutants and surface finish on the release of dissolved metals in rain was evaluated, also by means of multivariate analysis (two-way and three-way Principal Component Analysis). In addition, surface and cross-section investigations were performed so as to monitor the patina evolution. The contribution provided by weathering steel runoff to the dissolved Fe, Mn and Ni loading at local level is not negligible and pre-patination treatments seem to worsen the performance of weathering steel in term of metal release. Metal dissolution is strongly affected by extreme events and shows seasonal variations, with different influence of seasonal parameters on the behaviour of bare or artificially patinated steel, suggesting that climate changes could significantly influence metal release from this alloy. Therefore, it is essential to perform a long-term monitoring of the performance, the durability and the environmental impact of weathering steel. Copyright © 2016 Elsevier Ltd. All rights reserved.

Seasonal trends in Ceratitis capitata reproductive potential derived from live-caught females in Greece

PubMed Central

Kouloussis, Nikos A.; Papadopoulos, Nikos T.; Katsoyannos, Byron I.; Müller, Hans-Georg; Wang, Jane-Ling; Su, Yu-Ru; Molleman, Freerk; Carey, James R.

2012-01-01

Reproductive data of individual insects are extremely hard to collect under natural conditions, thus the study of research questions related to oviposition has not advanced. Patterns of oviposition are often inferred only indirectly, through monitoring of host infestation, whereas the influence of age structure and several other factors on oviposition remains unknown. Using a new approach, in this article, we live-trapped wild Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) females on the Greek island of Chios during two field seasons. For their remaining lifetime, these females were placed individually in small cages and their daily oviposition was monitored. Reproduction rates between cohorts from different collection dates were then compared. The results showed that in the different captive cohorts the average remaining lifetime and reproduction were highly variable within and between seasons. Multivariate regression analysis showed that the month of capture had a significant effect on captive life span, average daily reproduction, and patterns of egg laying. The effect of year was significant on reproduction, but not on captive life span. These differences between sampling periods probably reflect differences in the availability of hosts and other factors that vary during the season and affect age structure and reproduction. Using a non-parametric generalized additive model, we found a statistically significant correlation between the captive life span and the average daily reproduction. These findings and the experimental approach have several important implications. PMID:22791908

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Vegetation monitoring and classification using NOAA/AVHRR satellite data

NASA Technical Reports Server (NTRS)

Greegor, D. H., Jr.; Norwine, J. R.

1983-01-01

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I + II + III supernatant in human albumin separation

NASA Astrophysics Data System (ADS)

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-01

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I + II + III (FI + II + III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (Rp2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501 g/L, 0.465 g/L and 5.57 for TP, and 0.969, 0.530 g/L, 0.341 g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI + II + III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I+II+III supernatant in human albumin separation.

PubMed

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-15

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I+II+III (FI+II+III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (R p 2 ), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501g/L, 0.465g/L and 5.57 for TP, and 0.969, 0.530g/L, 0.341g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI+II+III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

PubMed

Eide, Ingvar; Westad, Frank

2018-01-01

A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Kalman filter for statistical monitoring of forest cover across sub-continental regions

Treesearch

Raymond L. Czaplewski

1991-01-01

The Kalman filter is a multivariate generalization of the composite estimator which recursively combines a current direct estimate with a past estimate that is updated for expected change over time with a prediction model. The Kalman filter can estimate proportions of different cover types for sub-continental regions each year. A random sample of high-resolution...

The Gendered Monitoring of Juvenile Delinquents: A Test of Power-Control Theory Using a Retrospective Cohort Study

ERIC Educational Resources Information Center

Schulze, Corina; Bryan, Valerie

2017-01-01

Through the framework of power-control theory (PCT), we provide a model of juvenile offending that places the gendered-raced treatment of juveniles central to the analysis. We test the theory using a unique sample that is predominately African American, poor, and composed entirely of juvenile offenders. Multivariate models compare the predictive…

Validation of the concentration profiles obtained from the near infrared/multivariate curve resolution monitoring of reactions of epoxy resins using high performance liquid chromatography as a reference method.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2007-03-07

This paper reports the validation of the results obtained by combining near infrared spectroscopy and multivariate curve resolution-alternating least squares (MCR-ALS) and using high performance liquid chromatography as a reference method, for the model reaction of phenylglycidylether (PGE) and aniline. The results are obtained as concentration profiles over the reaction time. The trueness of the proposed method has been evaluated in terms of lack of bias. The joint test for the intercept and the slope showed that there were no significant differences between the profiles calculated spectroscopically and the ones obtained experimentally by means of the chromatographic reference method at an overall level of confidence of 5%. The uncertainty of the results was estimated by using information derived from the process of assessment of trueness. Such operational aspects as the cost and availability of instrumentation and the length and cost of the analysis were evaluated. The method proposed is a good way of monitoring the reactions of epoxy resins, and it adequately shows how the species concentration varies over time.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals.

PubMed

Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

2018-02-09

Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels. In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed. The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T 2 -statistic and Q 2 -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Objective classification of ecological status in marine water bodies using ecotoxicological information and multivariate analysis.

PubMed

Beiras, Ricardo; Durán, Iria

2014-12-01

Some relevant shortcomings have been identified in the current approach for the classification of ecological status in marine water bodies, leading to delays in the fulfillment of the Water Framework Directive objectives. Natural variability makes difficult to settle fixed reference values and boundary values for the Ecological Quality Ratios (EQR) for the biological quality elements. Biological responses to environmental degradation are frequently of nonmonotonic nature, hampering the EQR approach. Community structure traits respond only once ecological damage has already been done and do not provide early warning signals. An alternative methodology for the classification of ecological status integrating chemical measurements, ecotoxicological bioassays and community structure traits (species richness and diversity), and using multivariate analyses (multidimensional scaling and cluster analysis), is proposed. This approach does not depend on the arbitrary definition of fixed reference values and EQR boundary values, and it is suitable to integrate nonlinear, sensitive signals of ecological degradation. As a disadvantage, this approach demands the inclusion of sampling sites representing the full range of ecological status in each monitoring campaign. National or international agencies in charge of coastal pollution monitoring have comprehensive data sets available to overcome this limitation.

Set-up of a multivariate approach based on serum biomarkers as an alternative strategy for the screening evaluation of the potential abuse of growth promoters in veal calves.

PubMed

Pirro, Valentina; Girolami, Flavia; Spalenza, Veronica; Gardini, Giulia; Badino, Paola; Nebbia, Carlo

2015-01-01

A chemometric class modelling strategy (unequal dispersed classes - UNEQ) was applied for the first time as a possible screening method to monitor the abuse of growth promoters in veal calves. Five serum biomarkers, known to reflect the exposure to classes of compounds illegally used as growth promoters, were determined from 50 untreated animals in order to design a model of controls, representing veal calves reared under good, safe and highly standardised breeding conditions. The class modelling was applied to 421 commercially bred veal calves to separate them into 'compliant' and 'non-compliant' with respect to the modelled controls. Part of the non-compliant animals underwent further histological and chemical examinations to confirm the presence of either alterations in target tissues or traces of illegal substances commonly administered for growth-promoting purposes. Overall, the congruence between the histological or chemical methods and the UNEQ non-compliant outcomes was approximately 58%, likely underestimated due to the blindness nature of this examination. Further research is needed to confirm the validity of the UNEQ model in terms of sensitivity in recognising untreated animals as compliant to the controls, and specificity in revealing deviations from ideal breeding conditions, for example due to the abuse of growth promoters.

Predictive modeling in Clostridium acetobutylicum fermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

NASA Astrophysics Data System (ADS)

Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.

2016-05-01

The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

Physiological Strain in French Vineyard Workers Wearing Protective Equipment to Conduct Re-Entry Tasks in Humid Conditions.

PubMed

Grimbuhler, Sonia; Viel, Jean-François

2018-06-19

The proper use of personal protective equipment (PPE) plays an important role in reducing exposure to pesticides in vineyard farming activities, including re-entry tasks. However, discomfort from clothing systems may increase the physiological burden on workers. We compared the physiological burdens of vineyard workers wearing three different types of PPE during canopy management in field humid conditions while accounting for occupational, climatic, and geographical environments. The study was conducted in the Bordeaux vineyards of southern France during June 2012. A total of 42 workers from seven vineyards consented to field observations. The following PPE garments were randomly allocated: HF Estufa polyamide (Brisa®), Tyvek® Classic Plus, and Tychem® C Standard. Participant sociodemographic characteristics were collected using a structured questionnaire. Skin temperature and heart rate were monitored continuously using portable devices. Multivariate multilevel linear regression models were performed to account for the hierarchical structure of data. No significant difference was found for mean skin temperature during work. Regardless of the cardiac strain parameter considered, the Tyvek® Classic Plus garment produced the poorest results (P ≤ 0.03). Under the very humid conditions encountered during the field study, the thinness and breathability of the Tyvek® Classic Plus garment resulted in undergarment humidity, imposing additional physiological burden on vineyard workers. These results confirm that the idea of using generic coveralls in any farming activity is unsuitable. Compromises should be created between physiological costs and protection, depending on the agricultural task performed, the crop grown, and the environmental conditions encountered.

Comparing methods suitable for monitoring marine mammals in low visibility conditions during seismic surveys.

PubMed

Verfuss, Ursula K; Gillespie, Douglas; Gordon, Jonathan; Marques, Tiago A; Miller, Brianne; Plunkett, Rachael; Theriault, James A; Tollit, Dominic J; Zitterbart, Daniel P; Hubert, Philippe; Thomas, Len

2018-01-01

Loud sound emitted during offshore industrial activities can impact marine mammals. Regulations typically prescribe marine mammal monitoring before and/or during these activities to implement mitigation measures that minimise potential acoustic impacts. Using seismic surveys under low visibility conditions as a case study, we review which monitoring methods are suitable and compare their relative strengths and weaknesses. Passive acoustic monitoring has been implemented as either a complementary or alternative method to visual monitoring in low visibility conditions. Other methods such as RADAR, active sonar and thermal infrared have also been tested, but are rarely recommended by regulatory bodies. The efficiency of the monitoring method(s) will depend on the animal behaviour and environmental conditions, however, using a combination of complementary systems generally improves the overall detection performance. We recommend that the performance of monitoring systems, over a range of conditions, is explored in a modelling framework for a variety of species. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Examining Big Brother's Purpose for Using Electronic Performance Monitoring

ERIC Educational Resources Information Center

Bartels, Lynn K.; Nordstrom, Cynthia R.

2012-01-01

We examined whether the reason offered for electronic performance monitoring (EPM) influenced participants' performance, stress, motivation, and satisfaction. Participants performed a data-entry task in one of five experimental conditions. In one condition, participants were not electronically monitored. In the remaining conditions, participants…

Optical glucose monitoring using vertical cavity surface emitting lasers (VCSELs)

NASA Astrophysics Data System (ADS)

Talebi Fard, Sahba; Hofmann, Werner; Talebi Fard, Pouria; Kwok, Ezra; Amann, Markus-Christian; Chrostowski, Lukas

2009-08-01

Diabetes Mellitus is a common chronic disease that has become a public health issue. Continuous glucose monitoring improves patient health by stabilizing the glucose levels. Optical methods are one of the painless and promising methods that can be used for blood glucose predictions. However, having accuracies lower than what is acceptable clinically has been a major concern. Using lasers along with multivariate techniques such as Partial Least Square (PLS) can improve glucose predictions. This research involves investigations for developing a novel optical system for accurate glucose predictions, which leads to the development of a small, low power, implantable optical sensor for diabetes patients.

Crystallization tendency of active pharmaceutical ingredients following rapid solvent evaporation--classification and comparison with crystallization tendency from undercooled melts.

PubMed

Van Eerdenbrugh, Bernard; Baird, Jared A; Taylor, Lynne S

2010-09-01

In this study, the crystallization behavior of a variety of compounds was studied following rapid solvent evaporation using spin coating. Initial screening to determine model compound suitability was performed using a structurally diverse set of 51 compounds in three different solvent systems [dichloromethane (DCM), a 1:1 (w/w) dichloromethane/ethanol mixture (MIX), and ethanol (EtOH)]. Of this starting set of 153 drug-solvent combinations, 93 (40 compounds) were selected for further evaluation based on solubility, chemical solution stability, and processability criteria. These systems were spin coated and their crystallization was monitored using polarized light microscopy (7 days, dry conditions). The crystallization behavior of the samples could be classified as rapid (Class I: 39 cases), intermediate (Class II: 23 cases), or slow (Class III: 31 cases). The solvent system employed influenced the classification outcome for only four of the compounds. The various compounds showed very diverse crystallization behavior. Upon comparison of classification results with those of a previous study, where cooling from the melt was used as a preparation technique, a good similarity was found whereby 68% of the cases were identically classified. Multivariate analysis was performed using a set of relevant physicochemical compound characteristics. It was found that a number of these parameters tended to differ between the different classes. These could be further interpreted in terms of the nature of the crystallization process. Additional multivariate analysis on the separate classes of compounds indicated some potential in predicting the crystallization tendency of a given compound.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis.

PubMed

Ebrahimi, Milad; Gerber, Erin L; Rockaway, Thomas D

2017-05-15

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Who’s not driving among U.S. high school seniors: A closer look at race/ethnicity, socioeconomic factors, and driving status

PubMed Central

Shults, Ruth A.; Banerjee, Tanima; Perry, Timothy

2017-01-01

Objectives We examined associations among race/ethnicity, socioeconomic factors, and driving status in a nationally representative sample of >26,000 U.S. high school seniors. Methods Weighted data from the 2012 and 2013 Monitoring the Future surveys were combined and analyzed. We imputed missing values using fully conditional specification multiple imputation methods. Multivariate logistic regression modeling was conducted to explore associations among race/ethnicity, socioeconomic factors, and driving status, while accounting for selected student behaviors and location. Lastly, odds ratios were converted to prevalence ratios. Results 23% of high school seniors did not drive during an average week; 14% of white students were nondrivers compared to 40% of black students. Multivariate analysis revealed that minority students were 1.8 to 2.5 times more likely to be nondrivers than their white counterparts, and students who had no earned income were 2.8 times more likely to be nondrivers than those earning an average of ≥$36 a week. Driving status also varied considerably by student academic performance, number of parents in the household, parental education, census region, and urbanicity. Conclusions Our findings suggest that resources—both financial and time—influence when or whether a teen will learn to drive. Many young people from minority or lower socioeconomic families who learn to drive may be doing so after their 18th birthday and therefore would not take advantage of the safety benefits provided by graduated driver licensing. Innovative approaches may be needed to improve safety for these young novice drivers. PMID:27064697

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 970.522 - Monitoring requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... exploration activities in accordance with a monitoring plan approved and issued by the Administrator as... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Monitoring requirements. 970.522..., Conditions and Restrictions Terms, Conditions, and Restrictions § 970.522 Monitoring requirements. Each...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

15 CFR 971.424 - Monitoring requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Monitoring requirements. 971.424...: Terms, Conditions and Restrictions Terms, Conditions and Restrictions § 971.424 Monitoring requirements... recovery activities to: (1) Monitor activities at times, and to the extent, the Administrator deems...

Noncontacting measurement technologies for space propulsion condition monitoring

NASA Technical Reports Server (NTRS)

Randall, M. R.; Barkhoudarian, S.; Collins, J. J.; Schwartzbart, A.

1987-01-01

This paper describes four noncontacting measurement technologies that can be used in a turbopump condition monitoring system. The isotope wear analyzer, fiberoptic deflectometer, brushless torque-meter, and fiberoptic pyrometer can be used to monitor component wear, bearing degradation, instantaneous shaft torque, and turbine blade cracking, respectively. A complete turbopump condition monitoring system including these four technologies could predict remaining component life, thus reducing engine operating costs and increasing reliability.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Isolating the impact of septic systems on fecal pollution in streams of suburban watersheds in Georgia, United States.

PubMed

Sowah, Robert A; Habteselassie, Mussie Y; Radcliffe, David E; Bauske, Ellen; Risse, Mark

2017-01-01

The presence of multiple sources of fecal pollution at the watershed level presents challenges to efforts aimed at identifying the influence of septic systems. In this study multiple approaches including targeted sampling and monitoring of host-specific Bacteroidales markers were used to identify the impact of septic systems on microbial water quality. Twenty four watersheds with septic density ranging from 8 to 373 septic units/km 2 were monitored for water quality under baseflow conditions over a 3-year period. The levels of the human-associated HF183 marker, as well as total and ruminant Bacteroidales, were quantified using quantitative polymerase chain reaction. Human-associated Bacteroidales yield was significantly higher in high density watersheds compared to low density areas and was negatively correlated (r = -0.64) with the average distance of septic systems to streams in the spring season. The human marker was also positively correlated with the total Bacteroidales marker, suggesting that the human source input was a significant contributor to total fecal pollution in the study area. Multivariable regression analysis indicates that septic systems, along with forest cover, impervious area and specific conductance could explain up to 74% of the variation in human fecal pollution in the spring season. The results suggest septic system impact through contributions to groundwater recharge during baseflow or failing septic system input, especially in areas with >87 septic units/km 2 . This study supports the use of microbial source tracking approaches along with traditional fecal indicator bacteria monitoring and land use characterization in a tiered approach to isolate the influence of septic systems on water quality in mixed-use watersheds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessing sleepiness and sleep disorders in Australian long-distance commercial vehicle drivers: self-report versus an "at home" monitoring device.

PubMed

Sharwood, Lisa N; Elkington, Jane; Stevenson, Mark; Grunstein, Ronald R; Meuleners, Lynn; Ivers, Rebecca Q; Haworth, Narelle; Norton, Robyn; Wong, Keith K

2012-04-01

As obstructive sleep apnea (OSA) is associated with a higher risk of motor vehicle crashes, there is increasing regulatory interest in the identification of commercial motor vehicle (CMV) drivers with this condition. This study aimed to determine the relationship between subjective versus objective assessment of OSA in CMV drivers. Cross-sectional survey. Heavy vehicle truck stops located across the road network of 2 large Australian states. A random sample of long distance commercial vehicle drivers (n = 517). None. Drivers were interviewed regarding their driving experience, personal health, shift schedules, payments, and various questions on sleep and tiredness in order to describe their sleep health across a range of variables. In addition, home recordings using a flow monitor were used during one night of sleep. Only 4.4% of drivers reported a previous diagnosis of sleep apnea, while our at home diagnostic test found a further 41% of long-distance heavy vehicle drivers likely to have sleep apnea. The multivariable apnea prediction index, based on self-report measures, showed poor agreement with the home-monitor detected sleep apnea (AUC 0.58, 95%CI = 0.49-0.62), and only 12% of drivers reported daytime sleepiness (Epworth Sleepiness Scale score > 10). Thirty-six percent of drivers were overweight and a further 50% obese; 49% of drivers were cigarette smokers. Sleep apnea remains a significant and unrecognized problem in CMV drivers, who we found to have multiple health risks. Objective testing for this sleep disorder needs to be considered, as symptom reports and self-identification appear insufficient to accurately identify those at risk.

Joint Forward Area Air Defense Test Program Definition.

DTIC Science & Technology

1984-03-30

Visibility Conditions 23 CHAPTER 6. ACRONYMS LIST 24 . CHAPTER 7. REFERENCE 26 APPENDIX A. IDENTIFICATION ISSUE ANALAYSIS PLAN A-1 to A-17 B. C3...and kill ratios between single and multiple pass aircraft. A " multivariate analysis" will be performed to determine if there is any significant...killed will be compared for each set of identification procedure". A " multivariate analysis" will be performed on the number of hostile and friendly

An Improved Method to Control the Critical Parameters of a Multivariable Control System

NASA Astrophysics Data System (ADS)

Subha Hency Jims, P.; Dharmalingam, S.; Wessley, G. Jims John

2017-10-01

The role of control systems is to cope with the process deficiencies and the undesirable effect of the external disturbances. Most of the multivariable processes are highly iterative and complex in nature. Aircraft systems, Modern Power Plants, Refineries, Robotic systems are few such complex systems that involve numerous critical parameters that need to be monitored and controlled. Control of these important parameters is not only tedious and cumbersome but also is crucial from environmental, safety and quality perspective. In this paper, one such multivariable system, namely, a utility boiler has been considered. A modern power plant is a complex arrangement of pipework and machineries with numerous interacting control loops and support systems. In this paper, the calculation of controller parameters based on classical tuning concepts has been presented. The controller parameters thus obtained and employed has controlled the critical parameters of a boiler during fuel switching disturbances. The proposed method can be applied to control the critical parameters like elevator, aileron, rudder, elevator trim rudder and aileron trim, flap control systems of aircraft systems.

Hyponatremia in Guillain-Barré Syndrome.

PubMed

Rumalla, Kavelin; Reddy, Adithi Y; Letchuman, Vijay; Mittal, Manoj K

2017-06-01

To evaluate incidence, risk factors, and in-hospital outcomes associated with hyponatremia in patients hospitalized for Guillain-Barré Syndrome (GBS). We identified adult patients with GBS in the Nationwide Inpatient Sample (2002-2011). Univariate and multivariable analyses were used. Among 54,778 patients hospitalized for GBS, the incidence of hyponatremia was 11.8% (compared with 4.0% in non-GBS patients) and increased from 6.9% in 2002 to 13.5% in 2011 (P < 0.0001). Risk factors associated with hyponatremia in multivariable analysis included advanced age, deficiency anemia, alcohol abuse, hypertension, and intravenous immunoglobulin (all P < 0.0001). Hyponatremia was associated with prolonged length of stay (16.07 vs. 10.41, days), increased costs (54,001 vs. 34,125, $USD), and mortality (20.5% vs. 11.6%) (all P < 0.0001). In multivariable analysis, hyponatremia was independently associated with adverse discharge disposition (odds ratio: 2.07, 95% confidence interval, 1.91-2.25, P < 0.0001). Hyponatremia is prevalent in GBS and is detrimental to patient-centered outcomes and health care costs. Sodium levels should be carefully monitored in high-risk patients.

Deformation integrity monitoring for GNSS positioning services including local, regional and large scale hazard monitoring - the Karlsruhe approach and software(MONIKA)

NASA Astrophysics Data System (ADS)

Jaeger, R.

2007-05-01

GNSS-positioning services like SAPOS/ascos in Germany and many others in Europe, America and worldwide, usually yield in a short time their interdisciplinary and country-wide use for precise geo-referencing, replacing traditional low order geodetic networks. So it becomes necessary that possible changes of the reference stations' coordinates are detected ad hoc. The GNSS-reference-station MONitoring by the KArlsruhe approach and software (MONIKA) are designed for that task. The developments at Karlsruhe University of Applied Sciences in cooperation with the State Survey of Baden-Württemberg are further motivated by a the official resolution of the German state survey departments' association (Arbeitsgemeinschaft der Vermessungsverwaltungen Deutschland (AdV)) 2006 on coordinate monitoring as a quality-control duty of the GNSS-positioning service provider. The presented approach can - besides the coordinate control of GNSS-positioning services - also be used to set up any GNSS-service for the tasks of an area-wide geodynamical and natural disaster-prevention service. The mathematical model of approach, which enables a multivariate and multi-epochal design approach, is based on the GNSS-observations input of the RINEX-data of the GNSS service, followed by fully automatic processing of baselines and/or session, and a near-online setting up of epoch-state vectors and their covariance-matrices in a rigorous 3D network adjustment. In case of large scale and long-term monitoring situations, geodynamical standard trends (datum-drift, plate-movements etc.) are accordingly considered and included in the mathematical model of MONIKA. The coordinate-based deformation monitoring approach, as third step of the stepwise adjustments, is based on the above epoch-state vectors, and - splitting off geodynamics trends - hereby on a multivariate and multi-epochal congruency testing. So far, that no other information exists, all points are assumed as being stable and congruent reference points. Stations, which a priori assumed as moving - in that way local monitoring areas can be included- are to be monitored and analyzed in reference to the stable reference points. In that way, a high sensitivity for the detection of GNSS station displacements, both for assumed stable points, as well as for a priori moving points, can be achieved. The results for the concept are shown at the example of a monitoring using the MONINKA-software in the 300 x 300 km area of the state of Baden-Württemberg, Germany.

Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different...

Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions.

PubMed

Xu, Xiaoya; Zhong, Miao; Wan, Jiafu; Yi, Minglun; Gao, Tiancheng

2016-10-01

In adverse working conditions, environmental parameters such as metallic dust, noise, and environmental temperature, directly affect the health condition of manufacturing workers. It is therefore important to implement health monitoring and management based on important physiological parameters (e.g., heart rate, blood pressure, and body temperature). In recent years, new technologies, such as body area networks, cloud computing, and smart clothing, have allowed the improvement of the quality of services. In this article, we first give five-layer architecture for health monitoring and management of manufacturing workers. Then, we analyze the system implementation process, including environmental data processing, physical condition monitoring and system services and management, and present the corresponding algorithms. Finally, we carry out an evaluation and analysis from the perspective of insurance and compensation for manufacturing workers in adverse working conditions. The proposed scheme will contribute to the improvement of workplace conditions, realize health monitoring and management, and protect the interests of manufacturing workers.

A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

NASA Astrophysics Data System (ADS)

Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

2017-12-01

Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

Fitting and Testing Conditional Multinormal Partial Credit Models

ERIC Educational Resources Information Center

Hessen, David J.

2012-01-01

A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in "Psychometrika" 55:5-18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item…

Survey of Condition Indicators for Condition Monitoring Systems (Open Access)

DTIC Science & Technology

2014-09-29

Hinesburg, Vermont, 05461, USA jz@renewablenrgsystems.com ABSTRACT Currently, the wind energy industry is swiftly changing its maintenance strategy...from schedule based maintenance to predictive based maintenance . Condition monitoring systems (CMS) play an important role in the predictive... maintenance cycle. As condition monitoring systems are being adopted by more and more OEM and O&M service providers from the wind energy industry, it is

Language and cognitive predictors of text comprehension: evidence from multivariate analysis.

PubMed

Kim, Young-Suk

2015-01-01

Using data from children in South Korea (N = 145, Mage = 6.08), it was determined how low-level language and cognitive skills (vocabulary, syntactic knowledge, and working memory) and high-level cognitive skills (comprehension monitoring and theory of mind [ToM]) are related to listening comprehension and whether listening comprehension and word reading mediate the relations of language and cognitive skills to reading comprehension. Low-level skills predicted comprehension monitoring and ToM, which in turn predicted listening comprehension. Vocabulary and syntactic knowledge were also directly related to listening comprehension, whereas working memory was indirectly related via comprehension monitoring and ToM. Listening comprehension and word reading completely mediated the relations of language and cognitive skills to reading comprehension. © 2014 The Author. Child Development © 2014 Society for Research in Child Development, Inc.

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

NASA Astrophysics Data System (ADS)

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

2009-07-01

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

Empirical Mining of Large Data Sets Already Helps to Solve Practical Ecological Problems; A Panoply of Working Examples (Invited)

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Hoffman, F. M.; Kumar, J.; Spruce, J.; Norman, S. P.

2013-12-01

Here we present diverse examples where empirical mining and statistical analysis of large data sets have already been shown to be useful for a wide variety of practical decision-making problems within the realm of large-scale ecology. Because a full understanding and appreciation of particular ecological phenomena are possible only after hypothesis-directed research regarding the existence and nature of that process, some ecologists may feel that purely empirical data harvesting may represent a less-than-satisfactory approach. Restricting ourselves exclusively to process-driven approaches, however, may actually slow progress, particularly for more complex or subtle ecological processes. We may not be able to afford the delays caused by such directed approaches. Rather than attempting to formulate and ask every relevant question correctly, empirical methods allow trends, relationships and associations to emerge freely from the data themselves, unencumbered by a priori theories, ideas and prejudices that have been imposed upon them. Although they cannot directly demonstrate causality, empirical methods can be extremely efficient at uncovering strong correlations with intermediate "linking" variables. In practice, these correlative structures and linking variables, once identified, may provide sufficient predictive power to be useful themselves. Such correlation "shadows" of causation can be harnessed by, e.g., Bayesian Belief Nets, which bias ecological management decisions, made with incomplete information, toward favorable outcomes. Empirical data-harvesting also generates a myriad of testable hypotheses regarding processes, some of which may even be correct. Quantitative statistical regionalizations based on quantitative multivariate similarity have lended insights into carbon eddy-flux direction and magnitude, wildfire biophysical conditions, phenological ecoregions useful for vegetation type mapping and monitoring, forest disease risk maps (e.g., sudden oak death), global aquatic ecoregion risk maps for aquatic invasives, and forest vertical structure ecoregions (e.g., using extensive LiDAR data sets). Multivariate Spatio-Temporal Clustering, which quantitatively places alternative future conditions on a common footing with present conditions, allows prediction of present and future shifts in tree species ranges, given alternative climatic change forecasts. ForWarn, a forest disturbance detection and monitoring system mining 12 years of national 8-day MODIS phenology data, has been operating since 2010, producing national maps every 8 days showing many kinds of potential forest disturbances. Forest resource managers can view disturbance maps via a web-based viewer, and alerts are issued when particular forest disturbances are seen. Regression-based decadal trend analysis showing long-term forest thrive and decline areas, and individual-based, brute-force supercomputing to map potential movement corridors and migration routes across landscapes will also be discussed. As significant ecological changes occur with increasing rapidity, such empirical data-mining approaches may be the most efficient means to help land managers find the best, most-actionable policies and decision strategies.

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

NASA Astrophysics Data System (ADS)

Kong, Changduk; Koo, Youngju

2011-04-01

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms can be proposed. In this hybrid method, the Fuzzy Logic identifies easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

Multivariate evaluation of the effectiveness of treatment efficacy of cypermethrin against sea lice (Lepeophtheirus salmonis) in Atlantic salmon (Salmo salar)

PubMed Central

2013-01-01

Background The sea louse Lepeophtheirus salmonis is the most important ectoparasite of farmed Atlantic salmon (Salmo salar) in Norwegian aquaculture. Control of sea lice is primarily dependent on the use of delousing chemotherapeutants, which are both expensive and toxic to other wildlife. The method most commonly used for monitoring treatment effectiveness relies on measuring the percentage reduction in the mobile stages of Lepeophtheirus salmonis only. However, this does not account for changes in the other sea lice stages and may result in misleading or incomplete interpretation regarding the effectiveness of treatment. With the aim of improving the evaluation of delousing treatments, we explored multivariate analyses of bath treatments using the topical pyrethroid, cypermethrin, in salmon pens at five Norwegian production sites. Results Conventional univariate analysis indicated reductions of over 90% in mobile stages at all sites. In contrast, multivariate analyses indicated differing treatment effectiveness between sites (p-value < 0.01) based on changes in the proportion and abundance of the chalimus and PAAM (pre-adult and adult males) stages. Low water temperatures and shortened intervals between sampling after treatment may account for the differences in the composition of chalimus and PAAM stage groups following treatment. Using multivariate analysis, such factors could be separated from those which were attributable to inadequate treatment or chemotherapeutant failure. Conclusions Multivariate analyses for evaluation of treatment effectiveness against multiple life cycle stages of L. salmonis yield additional information beyond that derivable from univariate methods. This can aid in the identification of causes of apparent treatment failure in salmon aquaculture. PMID:24354936

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... internal occupational dose. 20.1502 Section 20.1502 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive...

[Monitoring method for macroporous resin column chromatography process of salvianolic acids based on near infrared spectroscopy].

PubMed

Hou, Xiang-Mei; Zhang, Lei; Yue, Hong-Shui; Ju, Ai-Chun; Ye, Zheng-Liang

2016-07-01

To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing. Copyright© by the Chinese Pharmaceutical Association.

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

PubMed Central

Dinov, Ivo D.; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2014-01-01

Summary Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students’ understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference. PMID:25419016

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution.

PubMed

Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2013-01-01

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.

Comparison of multispectral remote-sensing techniques for monitoring subsurface drain conditions. [Imperial Valley, California

NASA Technical Reports Server (NTRS)

Goettelman, R. C.; Grass, L. B.; Millard, J. P.; Nixon, P. R.

1983-01-01

The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

PubMed

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2017-08-01

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data

ERIC Educational Resources Information Center

Mistler, Stephen A.; Enders, Craig K.

2017-01-01

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional…

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress-response

USDA-ARS?s Scientific Manuscript database

To mitigate the effects of heat and drought stress, an understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in...

Effects of Missing Data Methods in SEM under Conditions of Incomplete and Nonnormal Data

ERIC Educational Resources Information Center

Li, Jian; Lomax, Richard G.

2017-01-01

Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate,…

Employment, Work Conditions, and the Home Environment in Single-Mother Families

ERIC Educational Resources Information Center

Lleras, Christy

2008-01-01

This study investigates the impact of employment status and work conditions on the quality of the home environment provided by single mothers of preschool-age children. Multivariate analyses were conducted using data from the National Longitudinal Survey of Youth. The results indicate that employment status is not a significant predictor of the…

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

PubMed Central

2018-01-01

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. PMID:29316731

Application of MCR-ALS to reveal intermediate conformations in the thermally induced α-β transition of poly-L-lysine monitored by FT-IR spectroscopy

NASA Astrophysics Data System (ADS)

Alcaráz, Mirta R.; Schwaighofer, Andreas; Goicoechea, Héctor; Lendl, Bernhard

2017-10-01

Temperature-induced conformational transitions of poly-L-lysine were monitored with Fourier-transform infrared (FT-IR) spectroscopy between 10 °C and 70 °C. Chemometric analysis of dynamic IR spectra was performed by multivariate curve analysis-alternating least squares (MCR-ALS) of the amide I‧ and amide II‧ spectral region. With this approach, the pure spectral and concentration profiles of the conformational transition were obtained. Beside the initial α-helical, the intermediate random coil/extended helices and the final β-sheet structure, an additional intermediate PLL conformation was identified and attributed to a transient β-sheet structure.

A-TEEMTM, a new molecular fingerprinting technique: simultaneous absorbance-transmission and fluorescence excitation-emission matrix method

NASA Astrophysics Data System (ADS)

Quatela, Alessia; Gilmore, Adam M.; Steege Gall, Karen E.; Sandros, Marinella; Csatorday, Karoly; Siemiarczuk, Alex; (Ben Yang, Boqian; Camenen, Loïc

2018-04-01

We investigate the new simultaneous absorbance-transmission and fluorescence excitation-emission matrix method for rapid and effective characterization of the varying components from a mixture. The absorbance-transmission and fluorescence excitation-emission matrix method uniquely facilitates correction of fluorescence inner-filter effects to yield quantitative fluorescence spectral information that is largely independent of component concentration. This is significant because it allows one to effectively monitor quantitative component changes using multivariate methods and to generate and evaluate spectral libraries. We present the use of this novel instrument in different fields: i.e. tracking changes in complex mixtures including natural water, wine as well as monitoring stability and aggregation of hormones for biotherapeutics.

Performance monitoring and error significance in patients with obsessive-compulsive disorder.

PubMed

Endrass, Tanja; Schuermann, Beate; Kaufmann, Christan; Spielberg, Rüdiger; Kniesche, Rainer; Kathmann, Norbert

2010-05-01

Performance monitoring has been consistently found to be overactive in obsessive-compulsive disorder (OCD). The present study examines whether performance monitoring in OCD is adjusted with error significance. Therefore, errors in a flanker task were followed by neutral (standard condition) or punishment feedbacks (punishment condition). In the standard condition patients had significantly larger error-related negativity (ERN) and correct-related negativity (CRN) ampliudes than controls. But, in the punishment condition groups did not differ in ERN and CRN amplitudes. While healthy controls showed an amplitude enhancement between standard and punishment condition, OCD patients showed no variation. In contrast, group differences were not found for the error positivity (Pe): both groups had larger Pe amplitudes in the punishment condition. Results confirm earlier findings of overactive error monitoring in OCD. The absence of a variation with error significance might indicate that OCD patients are unable to down-regulate their monitoring activity according to external requirements. Copyright 2010 Elsevier B.V. All rights reserved.

Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera.

PubMed

Jiang, Yu; Li, Changying; Paterson, Andrew H; Sun, Shangpeng; Xu, Rui; Robertson, Jon

2017-01-01

Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage ( R 2 = 0.35-0.71) and growth rates in early canopy development stages ( R 2 = 0.29-0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.

Distributed condition monitoring techniques of optical fiber composite power cable in smart grid

NASA Astrophysics Data System (ADS)

Sun, Zhihui; Liu, Yuan; Wang, Chang; Liu, Tongyu

2011-11-01

Optical fiber composite power cable such as optical phase conductor (OPPC) is significant for the development of smart grid. This paper discusses the distributed cable condition monitoring techniques of the OPPC, which adopts embedded single-mode fiber as the sensing medium. By applying optical time domain reflection and laser Raman scattering, high-resolution spatial positioning and high-precision distributed temperature measurement is executed. And the OPPC cable condition parameters including temperature and its location, current carrying capacity, and location of fracture and loss can be monitored online. OPPC cable distributed condition monitoring experimental system is set up, and the main parts including pulsed fiber laser, weak Raman signal reception, high speed acquisition and cumulative average processing, temperature demodulation and current carrying capacity analysis are introduced. The distributed cable condition monitoring techniques of the OPPC is significant for power transmission management and security.

Health Monitoring System for Composite Structures

NASA Technical Reports Server (NTRS)

Tang, S. S.; Riccardella, P. C.; Andrews, R. J.; Grady, J. E.; Mucciaradi, A. N.

1996-01-01

An automated system was developed to monitor the health status of composites. It uses the vibration characteristics of composites to identify a component's damage condition. The vibration responses are characterized by a set of signal features defined in the time, frequency and spatial domains. The identification of these changes in the vibration characteristics corresponding to different health conditions was performed using pattern recognition principles. This allows efficient data reduction and interpretation of vast amounts of information. Test components were manufactured from isogrid panels to evaluate performance of the monitoring system. The components were damaged by impact to simulate different health conditions. Free vibration response was induced by a tap test on the test components. The monitoring system was trained using these free vibration responses to identify three different health conditions. They are undamaged vs. damaged, damage location and damage zone size. High reliability in identifying the correct component health condition was achieved by the monitoring system.

The association of corporate work environment factors, health risks, and medical conditions with presenteeism among Australian employees.

PubMed

Musich, Shirley; Hook, Dan; Baaner, Stephanie; Spooner, Michelle; Edington, Dee W

2006-01-01

To investigate the impact of selected corporate environment factors, health risks, and medical conditions on job performance using a self-reported measure of presenteeism. A cross-sectional survey utilizing health risk appraisal (HRA) data merging presenteeism with corporate environment factors, health risks, and medical conditions. Approximately 8000 employees across ten diverse Australian corporations. Employees (N = 1523; participation rate, 19%) who completed an HRA questionnaire. Self-reported HRA data were used to test associations of defined adverse corporate environment factors with presenteeism. Stepwise multivariate logistic regression modeling assessed the relative associations of corporate environment factors, health risks, and medical conditions with increased odds of any presenteeism. Increased presenteeism was significantly associated with poor working conditions, ineffective management/leadership, and work/life imbalance (adjusting for age, gender, health risks, and medical conditions). In multivariate logistic regression models, work/life imbalance, poor working conditions, life dissatisfaction, high stress, back pain, allergies, and younger age were significantly associated with presenteeism. Although the study has some limitations, including a possible response bias caused by the relatively low participation rate across the corporations, the study does demonstrate significant associations between corporate environment factors, health risks, and medical conditions and self-reported presenteeism. The study provides initial evidence that health management programming may benefit on-the-job productivity outcomes if expanded to include interventions targeting work environments.

A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon

USGS Publications Warehouse

Kelly, Valerie J.; White, Seth

2016-04-19

This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.

A methodological approach to study the stability of selected watercolours for painting reintegration, through reflectance spectrophotometry, Fourier transform infrared spectroscopy and hyperspectral imaging.

PubMed

Pelosi, Claudia; Capobianco, Giuseppe; Agresti, Giorgia; Bonifazi, Giuseppe; Morresi, Fabio; Rossi, Sara; Santamaria, Ulderico; Serranti, Silvia

2018-06-05

The aim of this work is to investigate the stability to simulated solar radiation of some paintings samples through a new methodological approach adopting non-invasive spectroscopic techniques. In particular, commercial watercolours and iron oxide based pigments were used, these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Reflectance spectrophotometry in the visible range and Hyperspectral Imaging in the short wave infrared were chosen as non-invasive techniques for evaluation the stability to irradiation of the chosen pigments. These were studied before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the chosen techniques in identifying the variations on paint layers, induced by photo-degradation, before they could be observed by eye. Furthermore a supervised classification method for monitoring the painted surface changes adopting a multivariate approach was successfully applied. Copyright © 2018 Elsevier B.V. All rights reserved.

Fluorescence-based assay as a new screening tool for toxic chemicals

PubMed Central

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-01-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients. PMID:27653274

Fluorescence-based assay as a new screening tool for toxic chemicals.

PubMed

Moczko, Ewa; Mirkes, Evgeny M; Cáceres, César; Gorban, Alexander N; Piletsky, Sergey

2016-09-22

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

Fluorescence-based assay as a new screening tool for toxic chemicals

NASA Astrophysics Data System (ADS)

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-09-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

Assessment of river quality in a subtropical Austral river system: a combined approach using benthic diatoms and macroinvertebrates

NASA Astrophysics Data System (ADS)

Nhiwatiwa, Tamuka; Dalu, Tatenda; Sithole, Tatenda

2017-12-01

River systems constitute areas of high human population densities owing to their favourable conditions for agriculture, water supply and transportation network. Despite human dependence on river systems, anthropogenic activities severely degrade water quality. The main aim of this study was to assess the river health of Ngamo River using diatom and macroinvertebrate community structure based on multivariate analyses and community metrics. Ammonia, pH, salinity, total phosphorus and temperature were found to be significantly different among the study seasons. The diatom and macroinvertebrate taxa richness increased downstream suggesting an improvement in water as we moved away from the pollution point sources. Canonical correspondence analyses identified nutrients (total nitrogen and reactive phosphorus) as important variables structuring diatom and macroinvertebrate community. The community metrics and diversity indices for both bioindicators highlighted that the water quality of the river system was very poor. These findings indicate that both methods can be used for water quality assessments, e.g. sewage and agricultural pollution, and they show high potential for use during water quality monitoring programmes in other regions.

Statistical Significance and Baseline Monitoring.

DTIC Science & Technology

1984-07-01

impacted at once........................... 24 6 Observed versus nominal a levels for multivariate tests of data sets (50 runs of 4 groups each...cumulative proportion of the observations found for each nominal level. The results of the comparisons of the observed versus nominal a levels for the...a values are always higher than nominal levels. Virtual- . .,ly all nominal a levels are below 0.20. In other words, the discriminant analysis models

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

ADVANCED SURVEILLANCE OF ENVIROMENTAL RADIATION IN AUTOMATIC NETWORKS.

PubMed

Benito, G; Sáez, J C; Blázquez, J B; Quiñones, J

2018-06-01

The objective of this study is the verification of the operation of a radiation monitoring network conformed by several sensors. The malfunction of a surveillance network has security and economic consequences, which derive from its maintenance and could be avoided with an early detection. The proposed method is based on a kind of multivariate distance, and the verification for the methodology has been tested at CIEMAT's local radiological early warning network.

Early and late fracture following extensive limb lengthening in patients with achondroplasia and hypochondroplasia.

PubMed

Kitoh, H; Mishima, K; Matsushita, M; Nishida, Y; Ishiguro, N

2014-09-01

Two types of fracture, early and late, have been reported following limb lengthening in patients with achondroplasia (ACH) and hypochondroplasia (HCH). We reviewed 25 patients with these conditions who underwent 72 segmental limb lengthening procedures involving the femur and/or tibia, between 2003 and 2011. Gender, age at surgery, lengthened segment, body mass index, the shape of the callus, the amount and percentage of lengthening and the healing index were evaluated to determine predictive factors for the occurrence of early (within three weeks after removal of the fixation pins) and late fracture (> three weeks after removal of the pins). The Mann‑Whitney U test and Pearson's chi-squared test for univariate analysis and stepwise regression model for multivariate analysis were used to identify the predictive factor for each fracture. Only one patient (two tibiae) was excluded from the analysis due to excessively slow formation of the regenerate, which required supplementary measures. A total of 24 patients with 70 limbs were included in the study. There were 11 early fractures in eight patients. The shape of the callus (lateral or central callus) was the only statistical variable related to the occurrence of early fracture in univariate and multivariate analyses. Late fracture was observed in six limbs and the mean time between removal of the fixation pins and fracture was 18.3 weeks (3.3 to 38.4). Lengthening of the tibia, larger healing index, and lateral or central callus were related to the occurrence of a late fracture in univariate analysis. A multivariate analysis demonstrated that the shape of the callus was the strongest predictor for late fracture (odds ratio: 19.3, 95% confidence interval: 2.91 to 128). Lateral or central callus had a significantly larger risk of fracture than fusiform, cylindrical, or concave callus. Radiological monitoring of the shape of the callus during distraction is important to prevent early and late fracture of lengthened limbs in patients with ACH or HCH. In patients with thin callus formation, some measures to stimulate bone formation should be considered as early as possible. ©2014 The British Editorial Society of Bone & Joint Surgery.

Assessment of Platelet Function in Traumatic Brain Injury-A Retrospective Observational Study in the Neuro-Critical Care Setting.

PubMed

Lindblad, Caroline; Thelin, Eric Peter; Nekludov, Michael; Frostell, Arvid; Nelson, David W; Svensson, Mikael; Bellander, Bo-Michael

2018-01-01

Despite seemingly functional coagulation, hemorrhagic lesion progression is a common and devastating condition following traumatic brain injury (TBI), stressing the need for new diagnostic techniques. Multiple electrode aggregometry (MEA) measures platelet function and could aid in coagulopathy assessment following TBI. The aims of this study were to evaluate MEA temporal dynamics, influence of concomitant therapy, and its capabilities to predict lesion progression and clinical outcome in a TBI cohort. Adult TBI patients in a neurointensive care unit that underwent MEA sampling were retrospectively included. MEA was sampled if the patient was treated with antiplatelet therapy, bled heavily during surgery, or had abnormal baseline coagulation values. We assessed platelet activation pathways involving the arachidonic acid receptor (ASPI), P2Y 12 receptor, and thrombin receptor (TRAP). ASPI was the primary focus of analysis. If several samples were obtained, they were included. Retrospective data were extracted from hospital charts. Outcome variables were radiologic hemorrhagic progression and Glasgow Outcome Scale assessed prospectively at 12 months posttrauma. MEA levels were compared between patients on antiplatelet therapy. Linear mixed effect models and uni-/multivariable regression models were used to study longitudinal dynamics, hemorrhagic progression and outcome, respectively. In total, 178 patients were included (48% unfavorable outcome). ASPI levels increased from initially low values in a time-dependent fashion ( p  < 0.001). Patients on cyclooxygenase inhibitors demonstrated low ASPI levels ( p  < 0.001), while platelet transfusion increased them ( p  < 0.001). The first ASPI ( p  = 0.039) and TRAP ( p  = 0.009) were significant predictors of outcome, but not lesion progression, in univariate analyses. In multivariable analysis, MEA values were not independently correlated with outcome. A general longitudinal trend of MEA is identified in this TBI cohort, even in patients without known antiplatelet therapies. Values appear also affected by platelet inhibitory treatment and by platelet transfusions. While significant in univariate models to predict outcome, MEA values did not independently correlate to outcome or lesion progression in multivariable analyses. Further prospective studies to monitor coagulation in TBI patients are warranted, in particular the interpretation of pathological MEA values in patients without antiplatelet therapies.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Functional Heartburn Overlaps With Irritable Bowel Syndrome More Often than GERD.

PubMed

de Bortoli, Nicola; Frazzoni, Leonardo; Savarino, Edoardo V; Frazzoni, Marzio; Martinucci, Irene; Jania, Aleksandra; Tolone, Salvatore; Scagliarini, Michele; Bellini, Massimo; Marabotto, Elisa; Furnari, Manuele; Bodini, Giorgia; Russo, Salvatore; Bertani, Lorenzo; Natali, Veronica; Fuccio, Lorenzo; Savarino, Vincenzo; Blandizzi, Corrado; Marchi, Santino

2016-12-01

We aimed to evaluate the prevalence of irritable bowel syndrome (IBS) in patients with typical reflux symptoms as distinguished into gastroesophageal reflux disease (GERD), hypersensitive esophagus (HE), and functional heartburn (FH) by means of endoscopy and multichannel intraluminal impedance (MII)-pH monitoring. The secondary aim was to detect pathophysiological and clinical differences between different sub-groups of patients with heartburn. Patients underwent a structured interview based on questionnaires for GERD, IBS, anxiety, and depression. Off-therapy upper-gastrointestinal (GI) endoscopy and 24 h MII-pH monitoring were performed in all cases. In patients with IBS, fecal calprotectin was measured and colonoscopy was scheduled for values >100 mg/kg to exclude organic disease. Multivariate logistic regression analysis was performed to identify independent risk factors for FH. Of the 697 consecutive heartburn patients who entered the study, 454 (65%) had reflux-related heartburn (GERD+HE), whereas 243 (35%) had FH. IBS was found in 147/454 (33%) GERD/HE but in 187/243 (77%) FH patients (P<0.001). At multivariate analysis, IBS and anxiety were independent risk factors for FH in comparison with reflux-related heartburn (GERD+HE). IBS overlaps more frequently with FH than with GERD and HE, suggesting common pathways and treatment. HE showed intermediate characteristic between GERD and FH.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Monitoring Forest Condition in Europe: Impacts of Nitrogen and Sulfur Depositions on Forest Ecosystems

Treesearch

M. Lorenz; G. Becher; V. Mues; E. Ulrich

2006-01-01

Forest condition in Europe has been monitored over 19 years jointly by the United Nations Economic Commission for Europe (UNECE) and the European Union (EU). Large-scale variations of forest condition over space and time in relation to natural and anthropogenic factors are assessed on about 6,000 plots systematically spread across Europe. This large-scale monitoring...

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

On the reliability of Shewhart-type control charts for multivariate process variability

NASA Astrophysics Data System (ADS)

Djauhari, Maman A.; Salleh, Rohayu Mohd; Zolkeply, Zunnaaim; Li, Lee Siaw

2017-05-01

We show that in the current practice of multivariate process variability monitoring, the reliability of Shewhart-type control charts cannot be measured except when the sub-group size n tends to infinity. However, the requirement of large n is meaningless not only in manufacturing industry where n is small but also in service industry where n is moderate. In this paper, we introduce a new definition of control limits in the two most appreciated control charts in the literature, i.e., the improved generalized variance chart (IGV-chart) and vector variance chart (VV-chart). With the new definition of control limits, the reliability of the control charts can be determined. Some important properties of new control limits will be derived and the computational technique of probability of false alarm will be delivered.

Random forests as cumulative effects models: A case study of lakes and rivers in Muskoka, Canada.

PubMed

Jones, F Chris; Plewes, Rachel; Murison, Lorna; MacDougall, Mark J; Sinclair, Sarah; Davies, Christie; Bailey, John L; Richardson, Murray; Gunn, John

2017-10-01

Cumulative effects assessment (CEA) - a type of environmental appraisal - lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regression trees, which model response variables by recursive partitioning. Random forests were used to model a series of candidate ecological indicators that described lakes and rivers from a case study watershed (The Muskoka River Watershed, Canada). Suitability of the candidate indicators for use in cumulative effects assessment and watershed monitoring was assessed according to how well they could be predicted from natural habitat features and how sensitive they were to human land-use. The best models explained 75% of the variation in a multivariate descriptor of lake benthic-macroinvertebrate community structure, and 76% of the variation in the conductivity of river water. Similar results were obtained by cross-validation. Several candidate indicators detected a simulated doubling of urban land-use in their catchments, and a few were able to detect a simulated doubling of agricultural land-use. The paper demonstrates that random forests can be used to describe the combined and singular effects of multiple stressors and natural environmental factors, and furthermore, that random forests can be used to evaluate the performance of monitoring indicators. The numerical methods presented are applicable to any ecosystem and indicator type, and therefore represent a step forward for CEA. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Next-Generation Sequencing-Based Detection of Circulating Tumour DNA After Allogeneic Stem Cell Transplantation for Lymphoma

PubMed Central

Herrera, Alex F.; Kim, Haesook T.; Kong, Katherine A.; Faham, Malek; Sun, Heather; Sohani, Aliyah R.; Alyea, Edwin P.; Carlton, Victoria E.; Chen, Yi-Bin; Cutler, Corey S.; Ho, Vincent T.; Koreth, John; Kotwaliwale, Chitra; Nikiforow, Sarah; Ritz, Jerome; Rodig, Scott J.; Soiffer, Robert J.; Antin, Joseph H.; Armand, Philippe

2016-01-01

Summary Next-generation sequencing (NGS)-based circulating tumour DNA (ctDNA) detection is a promising monitoring tool for lymphoid malignancies. We evaluated whether the presence of ctDNA was associated with outcome after allogeneic haematopoietic stem cell transplantation (HSCT) in lymphoma patients. We studied 88 patients drawn from a phase 3 clinical trial of reduced-intensity conditioning HSCT in lymphoma. Conventional restaging and collection of peripheral blood samples occurred at pre-specified time points before and after HSCT and were assayed for ctDNA by sequencing of the immunoglobulin or T-cell receptor genes. Tumour clonotypes were identified in 87% of patients with adequate tumour samples. Sixteen of 19 (84%) patients with disease progression after HSCT had detectable ctDNA prior to progression at a median of 3.7 months prior to relapse/progression. Patients with detectable ctDNA 3 months after HSCT had inferior progression-free survival (PFS) (2-year PFS 58% versus 84% in ctDNA-negative patients, p=0.033). In multivariate models, detectable ctDNA was associated with increased risk of progression/death (Hazard ratio 3.9, p=0.003) and increased risk of relapse/progression (Hazard ratio 10.8, p=0.0006). Detectable ctDNA is associated with an increased risk of relapse/progression, but further validation studies are necessary to confirm these findings and determine the clinical utility of NGS-based minimal residual disease monitoring in lymphoma patients after HSCT. PMID:27711974

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Rack protection monitor

DOEpatents

Orr, Stanley G.

2000-01-01

A hardwired, fail-safe rack protection monitor utilizes electromechanical relays to respond to the detection by condition sensors of abnormal or alarm conditions (such as smoke, temperature, wind or water) that might adversely affect or damage equipment being protected. When the monitor is reset, the monitor is in a detection mode with first and second alarm relay coils energized. If one of the condition sensors detects an abnormal condition, the first alarm relay coil will be de-energized, but the second alarm relay coil will remain energized. This results in both a visual and an audible alarm being activated. If a second alarm condition is detected by another one of the condition sensors while the first condition sensor is still detecting the first alarm condition, both the first alarm relay coil and the second alarm relay coil will be de-energized. With both the first and second alarm relay coils de-energized, both a visual and an audible alarm will be activated. In addition, power to the protected equipment will be terminated and an alarm signal will be transmitted to an alarm central control. The monitor can be housed in a separate enclosure so as to provide an interface between a power supply for the protected equipment and the protected equipment.

Adaptation to high throughput batch chromatography enhances multivariate screening.

PubMed

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Methods and apparatus for rotor blade ice detection

DOEpatents

LeMieux, David Lawrence

2006-08-08

A method for detecting ice on a wind turbine having a rotor and one or more rotor blades each having blade roots includes monitoring meteorological conditions relating to icing conditions and monitoring one or more physical characteristics of the wind turbine in operation that vary in accordance with at least one of the mass of the one or more rotor blades or a mass imbalance between the rotor blades. The method also includes using the one or more monitored physical characteristics to determine whether a blade mass anomaly exists, determining whether the monitored meteorological conditions are consistent with blade icing; and signaling an icing-related blade mass anomaly when a blade mass anomaly is determined to exist and the monitored meteorological conditions are determined to be consistent with icing.

Expert monitoring and verbal feedback as sources of performance pressure.

PubMed

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

2018-05-01

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

Environmental and biological monitoring of occupational exposure to organic micropollutants in gasoline.

PubMed

Senzolo, C; Frignani, S; Pavoni, B

2001-07-01

An exposure risk assessment of workers in a refinery production unit was undertaken. Gasoline and its main components were investigated through environmental and biological monitoring. Measured variables were environmental benzene, toluene, pentane and hexane; benzene and toluene in blood and urine; tt-MA (metabolite of benzene) in urine. Multivariate statistical analysis of the data showed that worker's exposure to the above substances fell within the limits specified by organisations such as ACGIH. Also, biological values complied with reference values (RV) for non-occupationally-exposed population. Different values of biological variables were determined by separating smokers from non-smokers: smokers had hematic and urinary benzene values significantly higher than non-smokers. During a 3-yr sampling, it was possible to identify a significant decrease of benzene in the workplace air and of hematic benzene for non-smokers. The most exposed department, one in which tank-lorries were loaded, needs further investigation and extended monitoring.

Shared decision making among parents of children with mental health conditions compared to children with chronic physical conditions.

PubMed

Butler, Ashley M; Elkins, Sara; Kowalkowski, Marc; Raphael, Jean L

2015-02-01

High quality care in pediatrics involves shared decision making (SDM) between families and providers. The extent to which children with common mental health disorders experience SDM is not well known. The objectives of this study were to examine how parent-reported SDM varies by child health (physical illness, mental health condition, and comorbid mental and physical conditions) and to examine whether medical home care attenuates any differences. We analyzed data on children (2-17 years) collected through the 2009/2010 National Survey of Children with Special Health Care Needs. The sample consisted of parents of children in one of three child health categories: (1) children with a chronic physical illness but no mental health condition; (2) children with a common mental health condition but no chronic physical condition; and (3) children with comorbid mental and chronic physical conditions. The primary dependent variable was parent-report of provider SDM. The primary independent variable was health condition category. Multivariate linear regression analyses were conducted. Multivariate analyses controlling for sociodemographic variables and parent-reported health condition impact indicated lower SDM among children with a common mental health condition-only (B = -0.40; p < 0.01) and children with comorbid conditions (B = -0.67; p < 0.01) compared to children with a physical condition-only. Differences in SDM for children with a common mental health condition-only were no longer significant in the model adjusting for medical home care. However, differences in SDM for children with comorbid conditions persisted after adjusting for medical home care. Increasing medical home care may help mitigate differences in SDM for children with mental health conditions-only. Other interventions may be needed to improve SDM among children with comorbid mental and physical conditions.

Eosinophil count, allergies, and rejection in pediatric heart transplant recipients.

PubMed

Arbon, Kate S; Albers, Erin; Kemna, Mariska; Law, Sabrina; Law, Yuk

2015-08-01

Allograft rejection and long-term immunosuppression remain significant challenges in pediatric heart transplantation. Pediatric recipients are known to have fewer rejection episodes and to develop more allergic conditions than adults. A T-helper 2 cell dominant phenotype, manifested clinically by allergies and an elevated eosinophil count, may be associated with immunologic quiescence in transplant recipients. This study assessed whether the longitudinal eosinophil count and an allergic phenotype were associated with freedom from rejection. This single-center, longitudinal, observational study included 86 heart transplant patients monitored from 1994 to 2011. Post-transplant biannual complete blood counts, allergic conditions, and clinical characteristics related to rejection risk were examined. At least 1 episode of acute cellular rejection (ACR) occurred in 38 patients (44%), antibody-mediated rejection (AMR) occurred in 11 (13%), and 49 patients (57%) were diagnosed with an allergic condition. Patients with ACR or AMR had a lower eosinophil count compared with non-rejectors (p = 0.011 and p = 0.022, respectively). In the multivariable regression analysis, the presence of panel reactive antibodies to human leukocyte antigen I (p = 0.014) and the median eosinophil count (p = 0.011) were the only independent covariates associated with AMR. Eosinophil count (p = 0.010) and female sex (p = 0.009) were independent risk factors for ACR. Allergic conditions or young age at transplant were not protective from rejection. This study demonstrates a novel association between a high eosinophil count and freedom from rejection. Identifying a biomarker for low rejection risk may allow a reduction in immunosuppression. Further investigation into the role of the T-helper 2 cell phenotype and eosinophils in rejection quiescence is warranted. Copyright © 2015 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Bioprospecting Chemical Diversity and Bioactivity in a Marine Derived Aspergillus terreus.

PubMed

Adpressa, Donovon A; Loesgen, Sandra

2016-02-01

A comparative metabolomic study of a marine derived fungus (Aspergillus terreus) grown under various culture conditions is presented. The fungus was grown in eleven different culture conditions using solid agar, broth cultures, or grain based media (OSMAC). Multivariate analysis of LC/MS data from the organic extracts revealed drastic differences in the metabolic profiles and guided our subsequent isolation efforts. The compound 7-desmethylcitreoviridin was isolated and identified, and is fully described for the first time. In addition, 16 known fungal metabolites were also isolated and identified. All compounds were elucidated by detailed spectroscopic analysis and tested for antibacterial activities against five human pathogens and tested for cytotoxicity. This study demonstrates that LC/MS based multivariate analysis provides a simple yet powerful tool to analyze the metabolome of a single fungal strain grown under various conditions. This approach allows environmentally-induced changes in metabolite expression to be rapidly visualized, and uses these differences to guide the discovery of new bioactive molecules. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

Notebook computer use with different monitor tilt angle: effects on posture, muscle activity and discomfort of neck pain users.

PubMed

Chiou, Wen-Ko; Chou, Wei-Ying; Chen, Bi-Hui

2012-01-01

This study aimed to evaluate the posture, muscle activities, and self reported discomforts of neck pain notebook computer users on three monitor tilt conditions: 100°, 115°, and 130°. Six subjects were recruited in this study to completed typing tasks. Results showed subjects have a trend to show the forward head posture in the condition that monitor was set at 100°, and the significant less neck and shoulder discomfort were noted in the condition that monitor was set at 130°. These result suggested neck pain notebook user to set their monitor tilt angle at 130°.

Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

NASA Astrophysics Data System (ADS)

Jaramillo, Víctor H.; Ottewill, James R.; Dudek, Rafał; Lepiarczyk, Dariusz; Pawlik, Paweł

2017-03-01

In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of each individual element, as well as the entire piece of machinery.

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Impact of remote monitoring on the management of arrhythmias in patients with implantable cardioverter-defibrillator.

PubMed

Marcantoni, Lina; Toselli, Tiziano; Urso, Giulia; Pratola, Claudio; Ceconi, Claudio; Bertini, Matteo

2015-11-01

In the last decade, there has been an exponential increase in cardioverter-defibrillator (ICD) implants. Remote monitoring systems, allow daily follow-ups of patients with ICD. To evaluate the impact of remote monitoring on the management of cardiovascular events associated with supraventricular and ventricular arrhythmias during long-term follow-up. A total of 207 patients undergoing ICD implantation/replacement were enrolled: 79 patients received remote monitoring systems and were followed up every 12 months, and 128 patients were followed up conventionally every 6 months. All patients were followed up and monitored for the occurrence of supraventricular and ventricular arrhythmia-related cardiovascular events (ICD shocks and/or hospitalizations). During a median follow-up of 842 days (interquartile range 476-1288 days), 32 (15.5%) patients experienced supraventricular arrhythmia-related events and 51 (24.6%) patients experienced ventricular arrhythmia-related events. Remote monitoring had a significant role in the reduction of supraventricular arrhythmia-related events, but it had no effect on ventricular arrhythmia-related events. In multivariable analysis, remote monitoring remained as an independent protective factor, reducing the risk of supraventricular arrhythmia-related events of 67% [hazard ratio, 0.33; 95% confidence interval (CI), 0.13-0.82; P = 0.017]. Remote monitoring systems improved outcomes in patients with supraventricular arrhythmias by reducing the risk of cardiovascular events, but no benefits were observed in patients with ventricular arrhythmias.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Advanced multivariable control of a turboexpander plant

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

Altena, D.; Howard, M.; Bullin, K.

1998-12-31

This paper describes an application of advanced multivariable control on a natural gas plant and compares its performance to the previous conventional feed-back control. This control algorithm utilizes simple models from existing plant data and/or plant tests to hold the process at the desired operating point in the presence of disturbances and changes in operating conditions. The control software is able to accomplish this due to effective handling of process variable interaction, constraint avoidance and feed-forward of measured disturbances. The economic benefit of improved control lies in operating closer to the process constraints while avoiding significant violations. The South Texasmore » facility where this controller was implemented experienced reduced variability in process conditions which increased liquids recovery because the plant was able to operate much closer to the customer specified impurity constraint. An additional benefit of this implementation of multivariable control is the ability to set performance criteria beyond simple setpoints, including process variable constraints, relative variable merit and optimizing use of manipulated variables. The paper also details the control scheme applied to the complex turboexpander process and some of the safety features included to improve reliability.« less

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Dosimetric Predictors of Hypothyroidism After Radical Intensity-modulated Radiation Therapy for Non-metastatic Nasopharyngeal Carcinoma.

PubMed

Lee, V; Chan, Sum-Yin; Choi, Cheuk-Wai; Kwong, D; Lam, Ka-On; Tong, Chi-Chung; Sze, Chun-Kin; Ng, S; Leung, To-Wai; Lee, A

2016-08-01

To investigate dosimetric predictors of hypothyroidism after radical intensity-modulated radiation therapy (IMRT) for non-metastatic nasopharyngeal carcinoma (NPC). Patients with non-metastatic NPC treated with radical IMRT from 2008 to 2013 were reviewed. Serum thyroid function tests before and after IMRT were regularly monitored. Univariable and multivariable analyses were carried out for predictors of biochemical and clinical hypothyroidism. In total, 149 patients were recruited. After a median follow-up duration of 3.1 years, 33 (22.1%) and 21 (14.1%) patients developed biochemical and clinical hypothyroidism, respectively. Eight (24.2%) patients who had biochemical hypothyroidism developed clinical hypothyroidism later. Univariable and multivariable analyses revealed that the volume of the thyroid (P=0.002, multivariable), VS60 (the absolute thyroid volume spared from 60 Gy or less) (P<0.001, multivariable) and VS45 (P<0.001, multivariable) of the thyroid were significant predictors of biochemical hypothyroidism. The freedom from biochemical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (mean 90.9 versus 62.6 months; P<0.001) and VS45 ≥ 5 cm(3) (mean 91.9 versus 65.2 months; P=0.001). Similarly multivariable analyses revealed that VS60 (P=0.001) and VS45 (P=0.003) were significant predictors of clinical hypothyroidism. The freedom from clinical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (91.5 versus 73.3 months; P=0.002) and VS45 ≥ 5 cm(3) (91.5 versus 75.9 months; P=0.007). VS60 and VS45 of the thyroid should be considered important dose constraints against hypothyroidism without compromising target coverage during IMRT optimisation for NPC. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Adolescents' leisure activities, parental monitoring and cigarette smoking - a cross-sectional study

PubMed Central

2011-01-01

Background Adolescent participation in leisure activities is developmentally beneficial, but certain activities may increase health compromising behaviours, such as tobacco smoking. A limited range of leisure activities has been studied, with little research on out-of-school settings where parental supervision is a potential protective factor. Tobacco smoking is an important, potentially modifiable health determinant, so understanding associations between adolescent leisure activities, parental monitoring, demographic factors and daily smoking may inform preventive strategies. These associations are reported for a New Zealand adolescent sample. Methods Randomly selected schools (n = 145) participated in the 2006 Youth In-depth Survey, a national, biennial study of Year 10 students (predominantly 14-15 years). School classes were randomly selected and students completed a self-report questionnaire in class time. Adjustment for clustering at the school level was included in all analyses. Since parental monitoring and demographic variables potentially confound relations between adolescent leisure activities and smoking, variables were screened before multivariable modelling. Given prior indications of demographic differences, gender and ethnic specific regression models were built. Results and Discussion Overall, 8.5% of the 3,161 students were daily smokers, including more females (10.5%) than males (6.5%). In gender and ethnic specific multivariate analysis of associations with daily smoking (adjusted for age, school socioeconomic decile rating, leisure activities and ethnicity or gender, respectively), parental monitoring exhibited a consistently protective, dose response effect, although less strongly among Māori. Attending a place of worship and going to the movies were protective for non-Māori, as was watching sports, whereas playing team sport was protective for all, except males. Attending a skate park was a risk factor for females and Māori which demonstrated a strong dose response effect. Conclusions There were significant differences in the risk of daily smoking across leisure activities by gender and ethnicity. This reinforces the need to be alert for, and respond to, gender and ethnic differences in the pattern of risk and protective factors. However, given the consistently protective, dose response effect of parental monitoring, our findings confirm that assisting oversight of adolescent leisure activities may be a key component in public health policy and prevention programmes. PMID:21645407

Adolescents' leisure activities, parental monitoring and cigarette smoking--a cross-sectional study.

PubMed

Guo, Hui; Reeder, Anthony I; McGee, Rob; Darling, Helen

2011-06-06

Adolescent participation in leisure activities is developmentally beneficial, but certain activities may increase health compromising behaviours, such as tobacco smoking. A limited range of leisure activities has been studied, with little research on out-of-school settings where parental supervision is a potential protective factor. Tobacco smoking is an important, potentially modifiable health determinant, so understanding associations between adolescent leisure activities, parental monitoring, demographic factors and daily smoking may inform preventive strategies. These associations are reported for a New Zealand adolescent sample. Randomly selected schools (n = 145) participated in the 2006 Youth In-depth Survey, a national, biennial study of Year 10 students (predominantly 14-15 years). School classes were randomly selected and students completed a self-report questionnaire in class time. Adjustment for clustering at the school level was included in all analyses. Since parental monitoring and demographic variables potentially confound relations between adolescent leisure activities and smoking, variables were screened before multivariable modelling. Given prior indications of demographic differences, gender and ethnic specific regression models were built. Overall, 8.5% of the 3,161 students were daily smokers, including more females (10.5%) than males (6.5%). In gender and ethnic specific multivariate analysis of associations with daily smoking (adjusted for age, school socioeconomic decile rating, leisure activities and ethnicity or gender, respectively), parental monitoring exhibited a consistently protective, dose response effect, although less strongly among Māori. Attending a place of worship and going to the movies were protective for non-Māori, as was watching sports, whereas playing team sport was protective for all, except males. Attending a skate park was a risk factor for females and Māori which demonstrated a strong dose response effect. There were significant differences in the risk of daily smoking across leisure activities by gender and ethnicity. This reinforces the need to be alert for, and respond to, gender and ethnic differences in the pattern of risk and protective factors. However, given the consistently protective, dose response effect of parental monitoring, our findings confirm that assisting oversight of adolescent leisure activities may be a key component in public health policy and prevention programmes.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

A classification of freshwater Louisiana lakes based on water quality and user perception data.

PubMed

Burden, D G; Malone, R F

1987-09-01

An index system developed for Louisiana lakes was based on correlations between measurable water quality parameters and perceived lake quality. Support data was provided by an extensive monitoring program of 30 lakes coordinated with opinion surveys undertaken during summer 1984. Lakes included in the survey ranged from 4 to 735 km(2) in surface area with mean depths ranging from 0.5 to 8.0 m. Water quality data indicated most of these lakes are eutrophic, although many have productive fisheries and are considered recreational assets. Perception ratings of fishing quality and its associated water quality were obtained by distributing approximately 1200 surveys to Louisiana Bass Club Associaton members. The ability of Secchi disc transparency, total organic carbon, total Kjeldahl nitrogen, total phosphorus, and chlorophyll a to discriminate between perception classes was examined using probability distributions and multivariate analyses. Secchi disc and total organic carbon best reflected perceived lake conditions; however, these parameters did not provide the discrimination necessary for developing a quantitative risk assessment of lake trophic state. Consequently, an interim lakes index system was developed based on total organic carbon and perceived lake conditions. The developed index system will aid State officials in interpretating and evaluating regularly collected lake quality data, recognizing potential problem areas, and identifying proper management policies for protecting fisheries usage within the State.

Set-up of a multivariate approach based on serum biomarkers as an alternative strategy for the screening evaluation of the potential abuse of growth promoters in veal calves

PubMed Central

Pirro, Valentina; Girolami, Flavia; Spalenza, Veronica; Gardini, Giulia; Badino, Paola; Nebbia, Carlo

2015-01-01

A chemometric class modelling strategy (unequal dispersed classes – UNEQ) was applied for the first time as a possible screening method to monitor the abuse of growth promoters in veal calves. Five serum biomarkers, known to reflect the exposure to classes of compounds illegally used as growth promoters, were determined from 50 untreated animals in order to design a model of controls, representing veal calves reared under good, safe and highly standardised breeding conditions. The class modelling was applied to 421 commercially bred veal calves to separate them into ‘compliant’ and ‘non-compliant’ with respect to the modelled controls. Part of the non-compliant animals underwent further histological and chemical examinations to confirm the presence of either alterations in target tissues or traces of illegal substances commonly administered for growth-promoting purposes. Overall, the congruence between the histological or chemical methods and the UNEQ non-compliant outcomes was approximately 58%, likely underestimated due to the blindness nature of this examination. Further research is needed to confirm the validity of the UNEQ model in terms of sensitivity in recognising untreated animals as compliant to the controls, and specificity in revealing deviations from ideal breeding conditions, for example due to the abuse of growth promoters. PMID:25730172

International comparisons of disparities in access to care for people with mental health conditions.

PubMed

Corscadden, Lisa; Callander, Emily J; Topp, Stephanie M

2018-06-21

Relatively little is known about experiences of barriers in access to overall care for people with mental health conditions (MHCs), or disparities between people with and without MHCs, or how patterns vary across countries. The 2016 Commonwealth Fund International Health Policy Survey of adults was used to compare access barriers for people with MHCs across 11 countries, and disparities within countries between people with and without an MHC, using normalized scores. Disparities were also assessed by using multivariable models adjusting for age, sex, immigrant status, income, and self-rated health. On average, people with MHCs had a higher prevalence of barriers, with a gap of 7 percentage points between people with and without MHCs. The gap ranged from 5 to 9% across countries. For people with an MHC, the most common access barriers were skipping care due to cost (26%) and receiving conflicting information from providers (26%). For all countries, having an MHC was associated with higher odds of experiencing barriers of access to care on several measures, with at least 1 case where the adjusted odds were greater than 2. There is an imperative to improve monitoring of access to overall health care for people with MHCs and an opportunity learn from countries with fewer barriers and disparities in access to care. Copyright © 2018 John Wiley & Sons, Ltd.

Signatures of subacute potentially catastrophic illness in the intensive care unit: model development and validation

PubMed Central

Moss, Travis J.; Lake, Douglas E.; Forrest Calland, J; Enfield, Kyle B; Delos, John B.; Fairchild, Karen D.; Randall Moorman, J.

2016-01-01

Objective Patients in intensive care units are susceptible to subacute, potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early, subclinical signatures of incipient life-threatening illness. Design We report a study of model development and validation of a retrospective observational cohort using resampling (TRIPOD Type 1b internal validation), and a study of model validation using separate data (Type 2b internal/external validation). Setting University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Patients Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical intensive care units with available physiologic monitoring data. Interventions None. Measurements and Main Results We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent, unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic, individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally-validated C-statistics of 0.61 to 0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Conclusions Subacute, potentially catastrophic illnesses in 3 diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes. PMID:27452809

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.

PubMed

Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

2015-12-15

As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. Copyright © 2015 Elsevier B.V. All rights reserved.

Observational needs for estimating Alaskan soil carbon stocks under current and future climate

DOE PAGES

Vitharana, U. W. A.; Mishra, U.; Jastrow, J. D.; ...

2017-01-24

Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil-forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole-profile depths, respectively, at a confidence interval of 5 kg C m -2. Using the spatialmore » correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole-profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole-profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil-forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.« less

Observational needs for estimating Alaskan soil carbon stocks under current and future climate

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

Vitharana, U. W. A.; Mishra, U.; Jastrow, J. D.

Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil-forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole-profile depths, respectively, at a confidence interval of 5 kg C m -2. Using the spatialmore » correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole-profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole-profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil-forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.« less

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

Ninety-day mortality after resection for lung cancer is nearly double 30-day mortality.

PubMed

Pezzi, Christopher M; Mallin, Katherine; Mendez, Andres Samayoa; Greer Gay, Emmelle; Putnam, Joe B

2014-11-01

To evaluate 30-day and 90-day mortality after major pulmonary resection for lung cancer including the relationship to hospital volume. Major lung resections from 2007 to 2011 were identified in the National Cancer Data Base. Mortality was compared according to annual volume and demographic and clinical covariates using univariate and multivariable analyses, and included information on comorbidity. Statistical significance (P<.05) and 95% confidence intervals were assessed. There were 124,418 major pulmonary resections identified in 1233 facilities. The 30-day mortality rate was 2.8%. The 90-day mortality rate was 5.4%. Hospital volume was significantly associated with 30-day mortality, with a mortality rate of 3.7% for volumes less than 10, and 1.7% for volumes of 90 or more. Other variables significantly associated with 30-day mortality include older age, male sex, higher stage, pneumonectomy, a previous primary cancer, and multiple comorbidities. Similar results were found for 90-day mortality rates. In the multivariate analysis, hospital volume remained significant with adjusted odds ratios of 2.1 (95% confidence interval [CI], 1.7-2.6) for 30-day mortality and 1.3 (95% CI, 1.1-1.6) for conditional 90-day mortality for the hospitals with the lowest volume (<10) compared with those with the highest volume (>90). Hospitals with a volume less than 30 had an adjusted odds ratio for 30-day mortality of 1.3 (95% CI, 1.2-1.5) compared with those with a volume greater than 30. Mortality at 30 and 90 days and hospital volume should be monitored by institutions performing major pulmonary resection and benchmarked against hospitals performing at least 30 resections per year. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Novel Molecular Spectroscopic Multimethod Approach for Monitoring Water Absorption/Desorption Kinetics of CAD/CAM Poly(Methyl Methacrylate) Prosthodontics.

PubMed

Wiedemair, Verena; Mayr, Sophia; Wimmer, Daniel S; Köck, Eva Maria; Penner, Simon; Kerstan, Andreas; Steinmassl, Patricia-Anca; Dumfahrt, Herbert; Huck, Christian W

2017-07-01

Water absorbed to poly(methyl methacrylate) (PMMA)-based CAD/CAM (computer-assisted design/computer-assisted manufacturing) prosthodontics can alter their properties including hardness and stability. In the present contribution, water absorption and desorption kinetics under defined experimental conditions were monitored employing several supplementary and advanced Fourier transform infrared (FT-IR) spectroscopic techniques in combination with multivariate analysis (MVA). In this synergistic vibrational spectroscopic multimethod approach, first a novel near-infrared (NIR) diffuse fiber optic probe reflection spectroscopic method was established for time-resolved analysis of water uptake within seven days under controlled conditions. Near-infrared water absorbance spectra in a wavenumber range between 5288-5100 cm -1 (combination band) and 5424-5352 cm -1 (second overtone) were used establishing corresponding calibration and validation models to quantify the amount of water in the milligram range. Therefore, 14 well-defined samples exposed to prior optimized experimental conditions were taken into consideration. The average daily water uptake conducting reference analysis was calculated as 22 mg/day for one week. Additionally, in this study for the first time NIR two-dimensional correlation spectroscopy (2D-COS) was conducted to monitor and interpret the spectral dynamics of water absorption on the prosthodontics in a wavenumber range of 5100-5300 cm -1 . For sensitive time-resolved recording of water desorption, a recently developed high-temperature, high-pressure FT-IR reaction cell with water-free ultra-dry in situ and operando operation was applied. The reaction cell, as well as the sample holder, was fully made of quartz glass, with no hot metal or ceramic parts in the vicinity of the high temperature zone. Applying a temperature gradient in the range of 25-150 ℃, mid-infrared (MIR) 2D-COS was successfully conducted to get insights into the dynamic behavior of O-H (1400-1800 cm -1 ) absorption bands with increasing temperature over time and the release of CO 2 (2450 cm -1 ) from the polymers. In addition, an ATR FT-IR imaging setup was optimized in order to investigate the surface homogeneity of the PMMA-based resins with a spatial resolution to 2 µm. From this vibrational spectroscopic multimethod approach and the collection of several analytical data, conclusions were drawn as to which degree the surface structure and/or its porosity have an impact onto the amount of water absorption.

BIRD COMMUNITIES AND HABITAT AS ECOLOGICAL INDICATORS OF FOREST CONDITION IN REGIONAL MONITORING

EPA Science Inventory

Ecological indicators for long-term monitoring programs are needed to detect and assess changing environmental conditions, We developed and tested community-level environmental indicators for monitoring forest bird populations and associated habitat. We surveyed 197 sampling plo...

CHARACTERIZATION OF SMALL ESTUARIES AS A COMPONENT OF A REGIONAL-SCALE MONITORING PROGRAM

EPA Science Inventory

Large-scale environmental monitoring programs, such as EPA's Environmental Monitoring and Assessment Program (EMAP), by nature focus on estimating the ecological condition of large geographic areas. Generally missing is the ability to provide estimates of condition of individual ...

Instrument for analysis of electric motors based on slip-poles component

DOEpatents

Haynes, Howard D.; Ayers, Curtis W.; Casada, Donald A.

1996-01-01

A new instrument for monitoring the condition and speed of an operating electric motor from a remote location. The slip-poles component is derived from a motor current signal. The magnitude of the slip-poles component provides the basis for a motor condition monitor, while the frequency of the slip-poles component provides the basis for a motor speed monitor. The result is a simple-to-understand motor health monitor in an easy-to-use package. Straightforward indications of motor speed, motor running current, motor condition (e.g., rotor bar condition) and synthesized motor sound (audible indication of motor condition) are provided. With the device, a relatively untrained worker can diagnose electric motors in the field without requiring the presence of a trained engineer or technician.

Instrument for analysis of electric motors based on slip-poles component

DOEpatents

Haynes, H.D.; Ayers, C.W.; Casada, D.A.

1996-11-26

A new instrument is described for monitoring the condition and speed of an operating electric motor from a remote location. The slip-poles component is derived from a motor current signal. The magnitude of the slip-poles component provides the basis for a motor condition monitor, while the frequency of the slip-poles component provides the basis for a motor speed monitor. The result is a simple-to-understand motor health monitor in an easy-to-use package. Straightforward indications of motor speed, motor running current, motor condition (e.g., rotor bar condition) and synthesized motor sound (audible indication of motor condition) are provided. With the device, a relatively untrained worker can diagnose electric motors in the field without requiring the presence of a trained engineer or technician. 4 figs.

Context cue focality influences strategic prospective memory monitoring.

PubMed

Hunter Ball, B; Bugg, Julie M

2018-02-12

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

Effect of quality metric monitoring and colonoscopy performance.

PubMed

Razzak, Anthony; Smith, Dineen; Zahid, Maliha; Papachristou, Georgios; Khalid, Asif

2016-10-01

Background and aims: Adenoma detection rate (ADR) and cecal withdrawal time (CWT) have been identified as measures of colonoscopy quality. This study evaluates the impact of monitoring these measures on provider performance. Methods: Six blinded gastroenterologists practicing at a Veterans Affairs Medical Center were prospectively monitored over 9 months. Data for screening, adenoma surveillance, and fecal occult blood test positive (FOBT +) indicated colonoscopies were obtained, including exam preparation quality, cecal intubation rate, CWT, ADR, adenomas per colonoscopy (APC), and adverse events. Metrics were continuously monitored after a period of informed CWT monitoring and informed CWT + ADR monitoring. The primary outcome was impact on ADR and APC. Results: A total of 1671 colonoscopies were performed during the study period with 540 before informed monitoring, 528 during informed CWT monitoring, and 603 during informed CWT + ADR monitoring. No statistically significant impact on ADR was noted across each study phase. Multivariate regression revealed a trend towards fewer adenomas removed during the CWT monitoring phase (OR = 0.79; 95 %CI 0.62 - 1.02, P  = 0.065) and a trend towards more adenomas removed during the CWT + ADR monitoring phase when compared to baseline (OR = 1.26; 95 %CI 0.99 - 1.61, P  = 0.062). Indication for examination and provider were significant predictors for higher APC. Provider-specific data demonstrated a direct relationship between high ADR performers and increased CWT. Conclusions: Monitoring quality metrics did not significantly alter colonoscopy performance across a small heterogeneous group of providers. Non-significant trends towards higher APC were noted with CWT + ADR monitoring. Providers with a longer CWT had a higher ADR. Further studies are needed to determine the impact of monitoring on colonoscopy performance.

Fast Multivariate Search on Large Aviation Datasets

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

PubMed

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Characterization of dominant hydrologic events: the role of spatial, temporal and climatic forces in generating the greatest sediment loads

NASA Astrophysics Data System (ADS)

Squires, A. L.; Boll, J.; Brooks, E. S.

2013-12-01

Soil erosion and the ensuing elevated sediment loads in surface water bodies result in impaired water quality and unsuitable habitat for salmonid species and other cold water biota. Increased sediment loads also relate to high nutrient levels in streams at downstream locations. Identification of the most sensitive factors leading to major sediment loads is useful in selection and placement of agricultural best management practices (BMPs), especially those that are management oriented such as nutrient management plans and the timing of tillage. Many BMPs work well for average storms but do not achieve desired results during the large storms, when hydrologically sensitive areas contribute the greatest amount of runoff and erosion. Research has shown that the majority of sediment loads in streams and rivers occur during a small proportion of the year, specifically during a few large storm events. In this research, we look beyond the conclusion that large events contribute the majority of sediment loads by investigating the driving forces behind each event. Long-term monitoring data were used from two monitoring stations in a small, mixed land use watershed in northern Idaho. The upper monitoring station is below mostly agricultural land use, and the lower monitoring station is below mostly urban land use. The watershed in question, Paradise Creek in Idaho, is the subject of a sediment TMDL which has not yet been consistently achieved and is currently up for review by the Idaho Department of Environmental Quality. We statistically analyzed the influence of multiple interacting variables on the magnitude of sediment loads during hydrologic events from 2002 to 2012. Spatial (i.e., above and below monitoring station data), temporal (i.e., seasonality), and climatic effects (i.e., precipitation, snowfall and snow melt) were examined, as well as the presence of frozen soils and the timing of events relative to each other. We hypothesized that (1) the events with the greatest sediment loads are flow-limited but occur after mass-limited events, (2) an event that is of long duration and is slow to peak, especially during frozen soil conditions, will contribute the greatest sediment load in a given year, and (3) urban land use generates greater sediment loads than rural land use. Multivariate analysis determined which factors lead to major sediment loads. Our presentation will focus on synthesizing the interacting variables and conditions that tend to result in dominant hydrologic events and suggestions for watershed management. This research will contribute to a more accurate assessment of the hydrology and water quality in the watershed to aid in improvement of the TMDL.

Maintaining High Ambulatory Activity Levels of Sedentary Adults with a Reinforcement Thinning Schedule

PubMed Central

Andrade, Leonardo F.; Barry, Danielle; Litt, Mark D.; Petry, Nancy M.

2016-01-01

Physical inactivity is a leading cause of mortality. Reinforcement interventions appear useful for increasing activity and preventing adverse consequences of sedentary lifestyles. This study evaluated a reinforcement thinning schedule for maintaining high activity levels. Sedentary adults (n=77) were given pedometers and encouraged to walk ≥10,000 steps/day. Initially, all participants earned rewards for each day they walked ≥10,000 steps. Subsequently, 61 participants were randomized to a monitoring only condition or a monitoring plus reinforcement thinning condition, in which frequencies of monitoring and reinforcing walking decreased over 12 weeks. The mean ± SD percentage of participants in the monitoring plusreinforcement thinning condition who met walking goals was 83% ± 24% versus. 55% ± 31% for participants in the monitoring only condition, p < .001. Thus, this monitoring plusreinforcement thinning schedule maintained high rates of walking when it was in effect; however, groups did not differ at a 24-week follow-up. Monitoring plus reinforcement thinning schedules, nevertheless, hold potential to extend benefits of reinforcement interventions at low costs. PMID:25041789

The Active Metabolite of Warfarin (3'-Hydroxywarfarin) and Correlation with INR, Warfarin and Drug Weekly Dosage in Patients under Oral Anticoagulant Therapy: A Pharmacogenetics Study.

PubMed

Gemmati, Donato; Burini, Francesco; Talarico, Anna; Fabbri, Matteo; Bertocco, Cesare; Vigliano, Marco; Moratelli, Stefano; Cuneo, Antonio; Serino, Maria Luisa; Avato, Francesco Maria; Tisato, Veronica; Gaudio, Rosa Maria

2016-01-01

Warfarin oral anticoagulant therapy (OAT) requires regular and frequent drug adjustment monitored by INR. Interindividual variability, drug and diet interferences, and genetics (VKORC1 and CYP2C9) make the maintenance/reaching of stable INR a not so easy task. HPLC assessment of warfarin/enantiomers was suggested as a valid monitoring-tool along with INR, but definite results are still lacking. We evaluated possible correlations between INR, warfarin/3'-hydroxywarfarin, and drug weekly dosage aimed at searching novel alternatives to OAT monitoring. VKORC1/CYP2C9 pharmacogenetics investigation was performed to account for the known influence on warfarin homeostasis. 133 OAT patients were recruited and assessed for warfarin/3'-hydroxywarfarin serum levels (HPLC), INR, and VKORC1 and CYP2C9 genotypes. A subgroup of 52 patients were monitored in detail (5 consecutive controls; c0-c4) till the target INR was reached. Correlation analyses were performed in both groups. In the whole OAT group both warfarin and 3'-hydroxywarfarin correlate with INR at comparable degree (r2 = 0.0388 and 0.0362 respectively). Conversely, warfarin weekly dosage better correlates with warfarin than with 3'-hydroxywarfarin (r2 = 0.0975 and r2 = 0.0381 respectively), but considering together warfarin plus 3'-hydroxywarfarin the correlation strongly increased (r2 = 0.1114; p<0.0001). Interestingly, 3'-hydroxywarfarin reached a strong correlation at c4 respect to warfarin (r2 = 0.2157 and r2 = 0.0549; p = 0.0005 and p = 0.0944 respectively) seeming less affected by drug adjustments in the subgroup of 52 patients who started OAT. The multivariate analyses aimed at estimating the true contribution of 3'-hydroxywarfarin on INR value ascribed it the unique significant value (p = 0.0021) in spite of warfarin who lost association. The pharmacogenetics studies confirmed that patients carrying the VKORC1 variant-allele required lower warfarin maintenance dosage and that the combination of VKORC1 and CYP2C9 yielded a warfarin responsive index (WRI) inversely related to the number variant alleles. Our results overall suggest that 3'-hydroxywarfarin monitoring could be of great advantage in INR monitoring respect to classical warfarin assessment showing significant contribution also in multivariate analysis. Therefore, additional active metabolites should be recognized and investigated as novel useful indicators.

Development of a simple and rapid solid phase microextraction-gas chromatography-triple quadrupole mass spectrometry method for the analysis of dopamine, serotonin and norepinephrine in human urine.

PubMed

Naccarato, Attilio; Gionfriddo, Emanuela; Sindona, Giovanni; Tagarelli, Antonio

2014-01-31

The work aims at developing a simple and rapid method for the quantification of dopamine (DA), serotonin (5-HT) and norepinephrine (NE) in human urine. The urinary levels of these biogenic amines can be correlated with several pathological conditions concerning heart disease, stress, neurological disorders and cancerous tumors. The proposed analytical approach is based on the use of solid phase microextraction (SPME) combined with gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS) after a fast derivatization of both aliphatic amino and phenolic moieties by propyl chloroformate. The variables influencing the derivatization reaction were reliably optimized by the multivariate approach of "Experimental design". The optimal conditions were obtained by performing derivatization with 100μL of propyl chloroformate and 100μL of pyridine. The extraction ability of five commercially available SPME fibers was evaluated in univariate mode and the best results were obtained using the polyacrylate fiber. The variables affecting the efficiency of SPME analysis were again optimized by the multivariate approach of "Experimental design" and, in particular, a central composite design (CCD) was applied. The optimal values were extraction in 45min at room temperature, desorption temperature at 300°C, no addition of NaCl. Assay of derivatized analytes was performed by using a gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS) system in selected reaction monitoring (SRM) acquisition. An evaluation of all analytical parameters demonstrates that the developed method provides satisfactory results. Indeed, very good linearities were achieved in the tested calibration range with correlation coefficient values of 0.9995, 0.9999 and 0.9997 for DA, 5-HT and NE, respectively. Accuracies and RSDs calculated for between-run and tested at concentrations of 30, 200, and 800μg L(-1) were in the range from 92.8% to 103.0%, and from 0.67 to 4.5%, respectively. Finally, the LOD values obtained can be considered very good (0.587, 0.381 and 1.23μg L(-1) for DA, 5-HT and NE, respectively). Copyright © 2013 Elsevier B.V. All rights reserved.

Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy.

PubMed

Roux, Aurélie; Thévenot, Etienne A; Seguin, François; Olivier, Marie-Françoise; Junot, Christophe

There is a lack of comprehensive studies documenting the impact of sample collection conditions on metabolic composition of human urine. To address this issue, two experiments were performed at a 3-month interval, in which midstream urine samples from healthy individuals were collected, pooled, divided into several aliquots and kept under specific conditions (room temperature, 4 °C, with or without preservative) up to 72 h before storage at -80 °C. Samples were analyzed by high-performance liquid chromatography coupled to high-resolution mass spectrometry and bacterial contamination was monitored by turbidimetry. Multivariate analyses showed that urinary metabolic fingerprints were affected by the presence of preservatives and also by storage at room temperature from 24 to 72 h, whereas no change was observed for urine samples stored at 4 °C over a 72-h period. Investigations were then focused on 280 metabolites previously identified in urine: 19 of them were impacted by the kind of sample collection protocol in both experiments, including 12 metabolites affected by bacterial contamination and 7 exhibiting poor chemical stability. Finally, our results emphasize that the use of preservative prevents bacterial overgrowth, but does not avoid metabolite instability in solution, whereas storage at 4 °C inhibits bacterial overgrowth at least over a 72-h period and slows the chemical degradation process. Consequently, and for further LC/MS analyses, human urine samples should be kept at 4 °C if their collection is performed over 24 h.

Analysis of proximal contact loss between implant-supported fixed dental prostheses and adjacent teeth in relation to influential factors and effects. A cross-sectional study.

PubMed

Byun, Soo-Jung; Heo, Seok-Mo; Ahn, Seung-Geun; Chang, Moontaek

2015-06-01

The aim was to analyze influential factors and effects of proximal contact loss between implant-supported fixed dental prostheses (FDP) and adjacent teeth. Ninety-four subjects treated with 135 FDPs supported by 188 implants were included. Degree of proximal contact tightness, food impaction, and periodontal/peri-implant tissue conditions were assessed in 191 proximal embrasures between implant-supported FDPs and adjacent teeth. Potential factors influencing proximal contact loss were estimated with the generalized estimation equation (GEE) procedure. Thirty-four percent of the proximal embrasures between implant-supported FDPs and teeth lost a proximal contact. The proximal contact loss rate continuously increased over the follow-up periods. A longer follow-up period, splinted implants, and mesial aspect of proximal contact were significant factors influencing the proximal contact loss in the univariate GEE analysis, whereas a longer follow-up period was the only significant factor in the multivariate GEE analysis. Food impaction was more frequently reported in the proximal contact loss group than the proximal contact group (odds ratio: 2.2). However, the proximal contact loss did not appear to affect the periodontal/peri-implant tissue conditions. Proximal contact loss between implant-supported FDPs and teeth occurred frequently and increased continuously over the follow-up period. The proximal contact loss significantly affected food impaction, but not the periodontal/peri-implant tissue conditions. Proximal contact loss should be carefully monitored during follow-up examinations in relation to food impaction. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Risk of and risk factors for hypoglycemia and associated arrhythmias in patients with type 2 diabetes and cardiovascular disease: a cohort study under real-world conditions.

PubMed

Pistrosch, Frank; Ganz, Xenia; Bornstein, Stefan R; Birkenfeld, Andreas L; Henkel, Elena; Hanefeld, Markolf

2015-10-01

Severe hypoglycemia is one of the strongest predictors of adverse clinical outcomes in patients with type 2 diabetes. Our study addressed the question whether there is a relationship between hypoglycemic events (HE) and severe cardiac arrhythmias in type 2 diabetic patients with established clinical risk factors under real-world conditions. We included 94 patients with type 2 diabetes and documented cardiovascular disease, in which interstitial glucose values and Holter ECG were recorded for 5 days in parallel. Patients received a stable treatment with insulin and/or sulfonylurea and were instructed to record symptoms of hypoglycemia or arrhythmias. Continuous glucose monitoring revealed 54 HE (interstitial glucose <3.1 mmol/l) in a total of 26 patients. Patients perceived only 39 % of HE during the day and 11 % of HE during the night. Patients with HE had a significantly higher number of severe ventricular arrhythmias [ventricular tachycardia (VT) 32.8 ± 60 vs. 0.9 ± 4.2, p = 0.019], and multivariate regression analysis revealed the duration of severe HE and TSH level as independent predictors of the occurrence of a VT. In conclusion, our study suggests that hypoglycemia might be able to trigger at least under certain circumstances, such as low TSH, ventricular arrhythmias under real-world conditions. The large number of unrecognized HE and VT in vulnerable patients treated with insulin or sulfonylurea should encourage the practitioner to focus on stable glucose control and to search for silent HE.

Favourable and Unfavourable Conditions for Children's Confidence Judgments

ERIC Educational Resources Information Center

Roebers, Claudia M.; von der Linden, Nicole; Howie, Pauline

2007-01-01

Two studies are presented in which favourable and unfavourable conditions for children's meta-cognitive monitoring processes are examined. Previously reported findings have shown that especially children's uncertainty monitoring (in contrast to certainty monitoring) poses specific problems for children in their elementary school years. When…

REGIONAL MONITORING OF CORAL CONDITION IN THE FLORIDA KEYS

EPA Science Inventory

Fisher, William S. and Deborah L. Santavy. 2004. Regional Monitoring of Coral Condition in Florida Keys (Abstract). Presented at the Monitoring Science and Technology Symposium, 20-24 September 2004, Denver, CO. 1 p. (ERL,GB R1020).

Coral reefs have experienced unpreceden...

Health Monitoring System for Car Seat

NASA Technical Reports Server (NTRS)

Elrod, Susan Vinz (Inventor); Dabney, Richard W. (Inventor)

2004-01-01

A health monitoring system for use with a child car seat has sensors mounted in the seat to monitor one or more health conditions of the seat's occupant. A processor monitors the sensor's signals and generates status signals related to the monitored conditions. A transmitter wireless transmits the status signals to a remotely located receiver. A signaling device coupled to the receiver produces at least one sensory (e.g., visual, audible, tactile) output based on the status signals.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

Bugg, Julie M; Ball, B Hunter

2017-07-01

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

Validation of MODIS FLH and In Situ Chlorophyll a from Tampa Bay, Florida (USA)

NASA Technical Reports Server (NTRS)

Fischer, Andrew; MorenoMadrinan, Max J.

2012-01-01

Satellite observation of phytoplankton concentration or chlorophyll-a (chla) is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chl-a inaccurate. Measurement of suninduced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum may, provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms or compared their accuracy against bluegreen ratio algorithms . In an unprecedented analysis using a long term (2003-2011) in situ monitoring data set from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer against a suite of water quality parameters taken in a variety of conditions throughout this large optically complex estuarine system. . Overall, the results show a 106% increase in the validity of chla concentration estimation using FLH over the standard chla estimate from the blue-green OC3M algorithm. Additionally, a systematic analysis of sampling sites throughout the bay is undertaken to understand how the FLH product responds to varying conditions in the estuary and correlations are conducted to see how the relationships between satellite FLH and in situ chlorophyll-a change with depth, distance from shore, from structures like bridges, and nutrient concentrations and turbidity. Such analysis illustrates that the correlations between FLH and in situ chla measurements increases with increasing distance between monitoring sites and structures like bridges and shore. Due probably to confounding factors, expected improvement in the FLH- chla relationship was not clearly noted when increasing depth and distance from shore alone (not including bridges). Correlations between turbidity and nutrient concentrations are discussed further and principle component analyses are employed to address the relationships between the multivariate data sets. A thorough understanding of how satellite FLH algorithms relate to in situ water quality parameters will enhance our understanding of how MODIS s global FLH algorithm can be used empirically to monitor coastal waters worldwide.

Remote Monitor Alarm System

NASA Technical Reports Server (NTRS)

Stute, Robert A. (Inventor); Galloway, F. Houston (Inventor); Medelius, Pedro J. (Inventor); Swindle, Robert W. (Inventor); Bierman, Tracy A. (Inventor)

1996-01-01

A remote monitor alarm system monitors discrete alarm and analog power supply voltage conditions at remotely located communications terminal equipment. A central monitoring unit (CMU) is connected via serial data links to each of a plurality of remote terminal units (RTUS) that monitor the alarm and power supply conditions of the remote terminal equipment. Each RTU can monitor and store condition information of both discrete alarm points and analog power supply voltage points in its associated communications terminal equipment. The stored alarm information is periodically transmitted to the CMU in response to sequential polling of the RTUS. The number of monitored alarm inputs and permissible voltage ranges for the analog inputs can be remotely configured at the CMU and downloaded into programmable memory at each RTU. The CMU includes a video display, a hard disk memory, a line printer and an audio alarm for communicating and storing the alarm information received from each RTU.

Flow cytometer jet monitor system

DOEpatents

Van den Engh, Ger

1997-01-01

A direct jet monitor illuminates the jet of a flow cytometer in a monitor wavelength band which is substantially separate from the substance wavelength band. When a laser is used to cause fluorescence of the substance, it may be appropriate to use an infrared source to illuminate the jet and thus optically monitor the conditions within the jet through a CCD camera or the like. This optical monitoring may be provided to some type of controller or feedback system which automatically changes either the horizontal location of the jet, the point at which droplet separation occurs, or some other condition within the jet in order to maintain optimum conditions. The direct jet monitor may be operated simultaneously with the substance property sensing and analysis system so that continuous monitoring may be achieved without interfering with the substance data gathering and may be configured so as to allow the front of the analysis or free fall area to be unobstructed during processing.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Towards the development of a miniaturized fiberless optofluidic biosensor for glucose.

PubMed

Cocovi-Solberg, David J; Miró, Manuel; Cerdà, Víctor; Pokrzywnicka, Marta; Tymecki, Lukasz; Koncki, Robert

2012-07-15

A miniaturized fiberless optical sensor integrated in an automated sequential injection (SI) manifold for mesofluidic handling of sample, conditioning and regeneration solutions is herein proposed for monitoring glucose (as a model analyte) in human serum. The optofluidic biosensor capitalizes on the co-immobilization of Prussian Blue (PB) and glucose oxidase (GOx) on a polyester film working concomitantly as a chemo- and bioreceptor. The oxidation of β-glucose at the receptor surface by GOx yields hydrogen peroxide whereby reoxidizing the reduced form of PB (the so-called Prussian White) so as to generate a deep blue color. The change in the optical properties of the film was continuously monitored by red paired emitter-detector diodes (PEDDs). A full factorial design followed by a Doehlert matrix-based response surface was exploited for multivariate optimization of the optofluidic PB-GOx-PEDD biosensor. The most significant variables influencing sensor's response were the current powering the light emitting diode (LED) emitter and the surface concentration of GOx. The optosensor was proven rugged as the response varies by merely 5% from the optimal value whenever the GOx concentration increases or decreases by 17% or the current powering the LED by 18.5%. Under the optimized physicochemical conditions, the limits of detection and quantification at the 3s(blank) and 10s(blank) levels, respectively, were estimated to be 23.8μmolL(-1) and 79.3μmolL(-1), respectively, with a dynamic working range spanning from 0.1 to 2.5mmolL(-1) of glucose. The trueness of the biosensor measurements was assessed with certified pathological and physiological human serum materials and compared against the spectrophotometric Trinder method. The devised enzymatic biosensor is affordable (less than 0.2€), sturdy, and versatile inasmuch as the chemical composition of the receptor and pair of LEDs might be customized at will. Copyright © 2011 Elsevier B.V. All rights reserved.

Excess risk of chronic physical conditions associated with depression and anxiety

PubMed Central

2014-01-01

Background Depression and anxiety have been reported to be associated with chronic physical conditions. We examined the excess risk of chronic physical conditions associated with depression and/or anxiety within a multivariate framework controlling for demographic and modifiable lifestyle risk factors. Methods We used a retrospective cross-sectional study design. Study participants were adults aged 22–64 years from 2007 and 2009 Medical Expenditure Panel Survey. We defined presence of depression-anxiety based on self-reported depression and anxiety and classified adults into 4 groups: 1) depression only; 2) anxiety only; 3) comorbid depression and anxiety 4) no depression and no anxiety. We included presence/absence of arthritis, asthma, chronic obstructive pulmonary disorder, diabetes, heart disease, hypertension, and osteoporosis as dependent variables. Complementary log-log regressions were used to examine the excess risk associated with depression and/or anxiety for chronic physical conditions using a multivariate framework that controlled for demographic (gender, age, race/ethnicity) and modifiable lifestyle (obesity, lack of physical activity, smoking) risk factors. Bonferroni correction for multiple comparisons was applied and p ≤0.007 was considered statistically significant. Results Overall, 7% had only depression, 5.2% had only anxiety and 2.5% had comorbid depression and anxiety. Results from multivariable regressions indicated that compared to individuals with no depression and no anxiety, individuals with comorbid depression and anxiety, with depression only and with anxiety only, all had higher risk of all the chronic physical conditions. ARRs for comorbid depression and anxiety ranged from 2.47 (95% CI: 1.47, 4.15; P = 0.0007) for osteoporosis to 1.64 (95% CI: 1.33, 2.04; P < 0.0001) for diabetes. Presence of depression only was also found to be significantly associated with all chronic conditions except for osteoporosis. Individuals with anxiety only were found to have a higher risk for arthritis, COPD, heart disease and hypertension. Conclusion Presence of depression and/or anxiety conferred an independent risk for having chronic physical conditions after adjusting for demographic and modifiable lifestyle risk factors. PMID:24433257

A NATIONAL PROGRAM FOR MONITORING STREAM CONDITION IN THE WESTERN UNITED STATES

EPA Science Inventory

The U.S. Environmental Protection Agency recently initiated a four-year survey of streams in the Western United States as a component of the Environmental Monitoring and Assessment Program (EMAP). EMAP is developing indicators to monitor and assess the condition of ecological...

Scoping review: national monitoring frameworks for social determinants of health and health equity

PubMed Central

Pedrana, Leo; Pamponet, Marina; Walker, Ruth; Costa, Federico; Rasella, Davide

2016-01-01

Background The strategic importance of monitoring social determinants of health (SDH) and health equity and inequity has been a central focus in global discussions around the 2011 Rio Political Declaration on SDH and the Millennium Development Goals. This study is part of the World Health Organization (WHO) equity-oriented analysis of linkages between health and other sectors (EQuAL) project, which aims to define a framework for monitoring SDH and health equity. Objectives This review provides a global summary and analysis of the domains and indicators that have been used in recent studies covering the SDH. These studies are considered here within the context of indicators proposed by the WHO EQuAL project. The objectives are as follows: to describe the range of international and national studies and the types of indicators most frequently used; report how they are used in causal explanation of the SDH; and identify key priorities and challenges reported in current research for national monitoring of the SDH. Design We conducted a scoping review of published SDH studies in the PubMed® database to obtain evidence of socio-economic indicators. We evaluated, selected, and extracted data from national scale studies published from 2004 to 2014. The research included papers published in English, Italian, French, Portuguese, and Spanish. Results The final sample consisted of 96 articles. SDH monitoring is well reported in the scientific literature independent of the economic level of the country and magnitude of deprivation in population groups. The research methods were mostly quantitative and many papers used multilevel and multivariable statistical analyses and indexes to measure health inequalities and SDH. In addition to the usual economic indicators, a high number of socio-economic indicators were used. The indicators covered a broad range of social dimensions, which were given consideration within and across different social groups. Many indicators included in the WHO EQuAL framework were not common in the studies in this review due to their intersectoral and interdisciplinary nature. Conclusions Our review illustrates that the attention to SDH monitoring has grown in terms of its importance and complexity within the scientific health literature. We identified a need to make indicators more wide-ranging in order to include a broader range of social conditions. The WHO EQuAL framework can provide intersectoral and interdisciplinary means of building a more comprehensive standardised approach to monitoring the SDH and improving equity in health. PMID:26853896

Fourier transform mid infrared spectroscopy applications for monitoring the structural plasticity of plant cell walls

PubMed Central

Largo-Gosens, Asier; Hernández-Altamirano, Mabel; García-Calvo, Laura; Alonso-Simón, Ana; Álvarez, Jesús; Acebes, José L.

2014-01-01

Fourier transform mid-infrared (FT-MIR) spectroscopy has been extensively used as a potent, fast and non-destructive procedure for analyzing cell wall architectures, with the capacity to provide abundant information about their polymers, functional groups, and in muro entanglement. In conjunction with multivariate analyses, this method has proved to be a valuable tool for tracking alterations in cell walls. The present review examines recent progress in the use of FT-MIR spectroscopy to monitor cell wall changes occurring in muro as a result of various factors, such as growth and development processes, genetic modifications, exposition or habituation to cellulose biosynthesis inhibitors and responses to other abiotic or biotic stresses, as well as its biotechnological applications. PMID:25071791

Community-Acquired Pneumonia Hospitalization among Children with Neurologic Disorders.

PubMed

Millman, Alexander J; Finelli, Lyn; Bramley, Anna M; Peacock, Georgina; Williams, Derek J; Arnold, Sandra R; Grijalva, Carlos G; Anderson, Evan J; McCullers, Jonathan A; Ampofo, Krow; Pavia, Andrew T; Edwards, Kathryn M; Jain, Seema

2016-06-01

To describe and compare the clinical characteristics, outcomes, and etiology of pneumonia among children hospitalized with community-acquired pneumonia (CAP) with neurologic disorders, non-neurologic underlying conditions, and no underlying conditions. Children <18 years old hospitalized with clinical and radiographic CAP were enrolled at 3 US children's hospitals. Neurologic disorders included cerebral palsy, developmental delay, Down syndrome, epilepsy, non-Down syndrome chromosomal abnormalities, and spinal cord abnormalities. We compared the epidemiology, etiology, and clinical outcomes of CAP in children with neurologic disorders with those with non-neurologic underlying conditions, and those with no underlying conditions using bivariate, age-stratified, and multivariate logistic regression analyses. From January 2010-June 2012, 2358 children with radiographically confirmed CAP were enrolled; 280 (11.9%) had a neurologic disorder (52.1% of these individuals also had non-neurologic underlying conditions), 934 (39.6%) had non-neurologic underlying conditions only, and 1144 (48.5%) had no underlying conditions. Children with neurologic disorders were older and more likely to require intensive care unit (ICU) admission than children with non-neurologic underlying conditions and children with no underlying conditions; similar proportions were mechanically ventilated. In age-stratified analysis, children with neurologic disorders were less likely to have a pathogen detected than children with non-neurologic underlying conditions. In multivariate analysis, having a neurologic disorder was associated with ICU admission for children ≥2 years of age. Children with neurologic disorders hospitalized with CAP were less likely to have a pathogen detected and more likely to be admitted to the ICU than children without neurologic disorders. Published by Elsevier Inc.

A real time study on condition monitoring of distribution transformer using thermal imager

NASA Astrophysics Data System (ADS)

Mariprasath, T.; Kirubakaran, V.

2018-05-01

The transformer is one of the critical apparatus in the power system. At any cost, a few minutes of outages harshly influence the power system. Hence, prevention-based maintenance technique is very essential. The continuous conditioning and monitoring technology significantly increases the life span of the transformer, as well as reduces the maintenance cost. Hence, conditioning and monitoring of transformer's temperature are very essential. In this paper, a critical review has been made on various conditioning and monitoring techniques. Furthermore, a new method, hot spot indication technique, is discussed. Also, transformer's operating condition is monitored by using thermal imager. From the thermal analysis, it is inferred that major hotspot locations are appearing at connection lead out; also, the bushing of the transformer is the very hottest spot in transformer, so monitoring the level of oil is essential. Alongside, real time power quality analysis has been carried out using the power analyzer. It shows that industrial drives are injecting current harmonics to the distribution network, which causes the power quality problem on the grid. Moreover, the current harmonic limit has exceeded the IEEE standard limit. Hence, the adequate harmonics suppression technique is need an hour.

Frontotemporal hypoactivity during a reality monitoring paradigm is associated with delusions in patients with schizophrenia spectrum disorders.

PubMed

Thoresen, Christian; Endestad, Tor; Sigvartsen, Niels Petter B; Server, Andres; Bolstad, Ingeborg; Johansson, Mikael; Andreassen, Ole A; Jensen, Jimmy

2014-01-01

Impaired monitoring of internally generated information has been proposed to be one component in the development and maintenance of delusions. The present study investigated the neural correlates underlying the monitoring processes and whether they were associated with delusions. Twenty healthy controls and 19 patients with schizophrenia spectrum disorders were administrated a reality monitoring paradigm during functional magnetic resonance imaging. During encoding participants were instructed to associate a statement with either a presented (viewed condition) or an imagined picture (imagined condition). During the monitoring session in the scanner, participants were presented with old and new statements and their task was to identify whether a given statement was associated with the viewed condition, imagined condition, or if it was new. Patients showed significantly reduced accuracy in the imagined condition with performance negatively associated with degree of delusions. This was accompanied with reduced activity in the left dorsolateral prefrontal cortex and left hippocampus in the patient group. The severity of delusions was negatively correlated with the blood-oxygenation-level dependent response in the left hippocampus. The results suggest that weakened monitoring is associated with delusions in patients with schizophrenia spectrum disorder, and that this may be mediated by a frontotemporal dysfunction.

Native crustacean species as a bioindicator of freshwater ecosystem pollution: A multivariate and integrative study of multi-biomarker response in active river monitoring.

PubMed

Bertrand, Lidwina; Monferrán, Magdalena Victoria; Mouneyrac, Catherine; Amé, María Valeria

2018-05-04

The aim of this study was to evaluate the ability of Palaemonetes argentinus to evidence the environmental degradation due to pollutants mixture in a freshwater aquatic ecosystem. For this purpose, an active monitoring (96 h exposure) was carried out in seven sites along the Ctalamochita River basin (Córdoba, Argentina), as a case of study. Our results evidenced sewage discharges impact in the water quality index, as well as metal pollution in water (Ag, Al, B, Pb, Hg) and sediments (Hg) with a potential effect on aquatic biota. The accumulation of total metals measured in exposed P. argentinus showed significant correlation with metals in water. Also, metallothioneins in cephalothorax showed significant changes along the basin, correlating with soluble concentrations of Cr, Zn, Cd, Hg, and V measured in shrimp tissues, which would be reflecting their bioavailability in the environment. In addition, the increase in antioxidant and detoxifying enzymes suggests the occurrence of oxidative stress in exposed shrimps. The integrative biomarker response index (IBR) pointed out the effect of metals on P. argentinus but also the occurrence of others pollutants. Finally, a high consensus was observed for water, sediments, and shrimps through the multivariate analysis (90%), indicating that P. argentinus can reflect changes in the abiotic matrixes. Moreover, studied sites were grouped according to their environmental quality. The use of active biomonitoring and the integration of biological responses through an IBR confirm that native biota could be a useful monitoring tool for bioavailable pollutants in aquatic ecosystems constituting a highly valuable approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

GEOGLAM Crop Monitor Assessment Tool: Developing Monthly Crop Condition Assessments

NASA Astrophysics Data System (ADS)

McGaughey, K.; Becker Reshef, I.; Barker, B.; Humber, M. L.; Nordling, J.; Justice, C. O.; Deshayes, M.

2014-12-01

The Group on Earth Observations (GEO) developed the Global Agricultural Monitoring initiative (GEOGLAM) to improve existing agricultural information through a network of international partnerships, data sharing, and operational research. This presentation will discuss the Crop Monitor component of GEOGLAM, which provides the Agricultural Market Information System (AMIS) with an international, multi-source, and transparent consensus assessment of crop growing conditions, status, and agro-climatic conditions likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soybean) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. This presentation will focus on the building of international networks, data collection, and data dissemination.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Multivariable bio-inspired photonic sensors for non-condensable gases

NASA Astrophysics Data System (ADS)

Potyrailo, Radislav A.; Karker, Nicholas; Carpenter, Michael A.; Minnick, Andrew

2018-02-01

Existing gas sensors often lose their measurement accuracy in practical field applications. To mitigate this significant problem, here, we report a demonstration of fabricated multivariable photonic sensors inspired by a known nanostructure of Morpho butterfly scales for detection of exemplary non-condensable gases such as H2, CO, and CO2. We fabricated bio-inspired nanostructures using conventional photolithography and chemical etching and detected individual gases that were difficult or unrealistic to detect using natural Morpho nanostructures. Such bio-inspired gas sensors are the critical step in the development of new sensors with improved accuracy for diverse operational scenarios. While this report is our initial demonstration of responses of fabricated multivariable sensors to individual gases in pristine laboratory conditions, it is a significant milestone in understanding the next steps toward field tests and practical applications of these sensors.

Understanding Early-Onset Drug and Alcohol Outcomes among Youth: The Role of Family Structure, Social Factors, and Interpersonal Perceptions of Use

PubMed Central

Hemovich, Vanessa; Lac, Andrew; Crano, William D.

2011-01-01

Research on adolescents focuses increasingly on features of the family in predicting and preventing substance use. Multivariate analyses of data from the National Survey of Parents and Youth (N = 4,173) revealed numerous significant differences on risk variables associated with family structure on adolescent drug-related perceptions and illicit substance use. Youth from dual-parent households were least likely to use drugs and were monitored more closely than single-parent youth (p < .001). A path analytic model estimated to illuminate linkages among theoretically implicated variables revealed that family income and child’s gender (p < .001), along with family structure (p < .05), affected parental monitoring, but not parental warmth. Monitoring and warmth, in turn, predicted adolescents’ social and interpersonal perceptions of drug use (p < .001), and both variables anticipated adolescents’ actual drug use one year later (p < .001). Results reconfirm the importance of parental monitoring and warmth and demonstrate the link between these variables, adolescents’ social and intrapersonal beliefs, and their use of illicit substances. PMID:21491334

On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters

NASA Astrophysics Data System (ADS)

Han, Fenghua; Xie, Feng

2017-07-01

In the process of cutting tools, it is very important to monitor the working state of the tools. On the basis of acceleration signal acquisition under the constant speed, time domain and frequency domain analysis of relevant indicators monitor the online of tool wear condition. The analysis results show that the method can effectively judge the tool wear condition in the process of machining. It has certain application value.

GEOGRAPHIC-SPECIFIC WATER QUALITY CRITERIA DEVELOPMENT WITH MONITORING DATA USING CONDITIONAL PROBABILITIES - A PROPOSED APPROACH

EPA Science Inventory

A conditional probability approach using monitoring data to develop geographic-specific water quality criteria for protection of aquatic life is presented. Typical methods to develop criteria using existing monitoring data are limited by two issues: (1) how to extrapolate to an...

Chemiluminescence-based multivariate sensing of local equivalence ratios in premixed atmospheric methane-air flames

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

Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.

Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using themore » leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.« less

Estimating the decomposition of predictive information in multivariate systems

NASA Astrophysics Data System (ADS)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

NASA Astrophysics Data System (ADS)

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

2016-12-01

The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites, spatial variations in water quality are likely from the agricultural inputs (fertilises) and for the Cluster 2 sites, the differences in hydrological transport is responsible for large spatial variations in water quality. These findings can be applied to water quality assessment along with establish effective water and land management in the future.

Studies and analyses of the space shuttle main engine

NASA Technical Reports Server (NTRS)

Tischer, Alan E.; Glover, R. C.

1987-01-01

The primary objectives were to: evaluate ways to maximize the information yield from the current Space Shuttle Main Engine (SSME) condition monitoring sensors, identify additional sensors or monitoring capabilities which would significantly improve SSME data, and provide continuing support of the Main Engine Cost/Operations (MECO) model. In the area of SSME condition monitoring, the principal tasks were a review of selected SSME failure data, a general survey of condition monitoring, and an evaluation of the current engine monitoring system. A computerized data base was developed to assist in modeling engine failure information propagations. Each of the above items is discussed in detail. Also included is a brief discussion of the activities conducted in support of the MECO model.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

Brief Mindfulness Meditation Training Reduces Mind-Wandering: The Critical Role of Acceptance

PubMed Central

Rahl, Hayley A.; Lindsay, Emily K.; Pacilio, Laura E.; Brown, Kirk W.; Creswell, J. David

2016-01-01

Mindfulness meditation programs, which train individuals to monitor their present moment experience in an open or accepting way, have been shown to reduce mind-wandering on standardized tasks in several studies. Here we test two competing accounts for how mindfulness training reduces mind-wandering, evaluating whether the attention monitoring component of mindfulness training alone reduces mind-wandering or whether the acceptance training component is necessary for reducing mind-wandering. Healthy young adults (N=147) were randomized to either a 3-day brief mindfulness training condition incorporating instruction in both attention monitoring and acceptance, a mindfulness training condition incorporating attention monitoring instruction only, a relaxation training condition, or a reading control condition. Participants completed measures of dispositional mindfulness and treatment expectancies before the training session on Day 1 and then completed a 6-minute Sustained Attention Response Task (SART) measuring mind-wandering after the training session on Day 3. Acceptance training was important for reducing mind-wandering, such that the monitoring + acceptance mindfulness training condition had the lowest mind-wandering relative to the other conditions, including significantly lower mind-wandering relative to the monitor-only mindfulness training condition. In one of the first experimental mindfulness training dismantling studies to-date, we show that training in acceptance is a critical driver of mindfulness training reductions in mind-wandering. This effect suggests that acceptance skills may facilitate emotion regulation on boring and frustrating sustained attention tasks that foster mind-wandering, such as the SART. PMID:27819445

Infrared thermography for condition monitoring - A review

NASA Astrophysics Data System (ADS)

Bagavathiappan, S.; Lahiri, B. B.; Saravanan, T.; Philip, John; Jayakumar, T.

2013-09-01

Temperature is one of the most common indicators of the structural health of equipment and components. Faulty machineries, corroded electrical connections, damaged material components, etc., can cause abnormal temperature distribution. By now, infrared thermography (IRT) has become a matured and widely accepted condition monitoring tool where the temperature is measured in real time in a non-contact manner. IRT enables early detection of equipment flaws and faulty industrial processes under operating condition thereby, reducing system down time, catastrophic breakdown and maintenance cost. Last three decades witnessed a steady growth in the use of IRT as a condition monitoring technique in civil structures, electrical installations, machineries and equipment, material deformation under various loading conditions, corrosion damages and welding processes. IRT has also found its application in nuclear, aerospace, food, paper, wood and plastic industries. With the advent of newer generations of infrared camera, IRT is becoming a more accurate, reliable and cost effective technique. This review focuses on the advances of IRT as a non-contact and non-invasive condition monitoring tool for machineries, equipment and processes. Various conditions monitoring applications are discussed in details, along with some basics of IRT, experimental procedures and data analysis techniques. Sufficient background information is also provided for the beginners and non-experts for easy understanding of the subject.

Environmental quality of the operating theaters in Campania Region: long lasting monitoring results.

PubMed

Triassi, M; Novi, C; Nardone, A; Russo, I; Montuori, P

2015-01-01

The health risk level in the operating theaters is directly correlated to the safety level offered by the healthcare facilities. This is the reason why the national Authorities released several regulations in order to monitor better environmental conditions of the operating theaters, to prevent occupational injuries and disease and to optimize working conditions. For the monitoring of environmental quality of the operating theaters following parameters are considered: quantity of supplied gases, anesthetics concentration, operating theatres volume measurement, air change rate, air conditioning system and air filtration. The objective is to minimize the risks in the operating theaters and to provide the optimal environmental working conditions. This paper reports the environmental conditions of operating rooms performed for several years in the public hospitals of the Campania Region. Investigation of environmental conditions of 162 operating theaters in Campania Region from January 2012 till July 2014 was conducted. Monitoring and analysis of physical and chemical parameters was done. The analysis of the results has been made considering specific standards suggested by national and international regulations. The study showed that 75% of the operating theaters presented normal values for microclimatic monitoring, while the 25% of the operating theaters had at least one parameter outside the limits. The monitoring of the anesthetics gases showed that in 9% of measurements of nitrous oxides and 4% of measurements of halogenated was not within the normal values.

Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

PubMed

Catelani, Tiago A; Santos, João Rodrigo; Páscoa, Ricardo N M J; Pezza, Leonardo; Pezza, Helena R; Lopes, João A

2018-03-01

This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T 2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time. Copyright © 2017 Elsevier B.V. All rights reserved.

Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis.

NASA Astrophysics Data System (ADS)

Gaudio, P.; Malizia, A.; Gelfusa, M.; Martinelli, E.; Di Natale, C.; Poggi, L. A.; Bellecci, C.

2017-01-01

Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

Pregnancy Intention and Post-partum Depressive Affect in Louisiana Pregnancy Risk Assessment Monitoring System.

PubMed

Suh, Elizabeth Y; Ma, Ping; Dunaway, Lauren Futrell; Theall, Katherine P

2016-05-01

Postpartum depression is associated with negative physical and mental health outcomes for both the mother and infant. This study examines the relationship between a mother and/or her partner's pregnancy intentions and reported post-partum depressive symptoms (PPDs). Using Louisiana pregnancy risk assessment monitoring system, 2000-2003, a secondary cross-sectional analysis was conducted on 5549 mothers, stratified by race, who delivered a singleton, live birth and whose infant was still alive at the time of the survey. Bivariate and multivariable logistic regressions were conducted, taking into account the complex survey design. In multivariable models, unwanted pregnancies were associated with severe PPDs (aOR 1.76, 95 % CI 1.23-2.53). Furthermore, the association between husbands/partners' who did not want or care about the pregnancy and mild PPDs remained for White women (aOR 1.32, 95 % CI 1.02-1.69); while among Black women, neither parent's pregnancy intention were associated with mild or severe PPDs. This study supports existing research demonstrating the association between pregnancy intention and PPDs. This study contributes to the limited information on the role that partner pregnancy intention plays on maternal mental health outcomes, however further discussion is needed on the impact of this role across races. Findings can be used in programs aiming to reduce adverse mental health outcomes among high-risk mothers.

Evaluation of the response of tritium-in-air instrumentation to HT in dry and humid conditions and to HTO vapor

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

Phillips, H.; Dean, J.; Privas, E.

2015-03-15

Nuclear plant operators (power generation, decommissioning and reprocessing operations) are required to monitor releases of tritium species for regulatory compliance and radiation protection purposes. Tritium monitoring is performed using tritium-in-air gas monitoring instrumentation based either on flow-through ion chambers or proportional counting systems. Tritium-in-air monitors are typically calibrated in dry conditions but in service may operate at elevated levels of relative humidity. The NPL (National Physical Laboratory) radioactive gas-in-air calibration system has been used to study the effect of humidity on the response to tritium of two tritium-in-air ion chamber based monitors and one proportional counting system which uses amore » P10/air gas mixture. The response of these instruments to HTO vapour has also been evaluated. In each case, instrument responses were obtained for HT in dry conditions (relative humidity (RH) about 2%), HT in 45% RH, and finally HTO at 45% RH. Instrumentation response to HT in humid conditions has been found to slightly exceed that in dry conditions. (authors)« less

Technical guide for monitoring selected conditions related to wilderness character

Treesearch

Peter Landres; Steve Boutcher; Liese Dean; Troy Hall; Tamara Blett; Terry Carlson; Ann Mebane; Carol Hardy; Susan Rinehart; Linda Merigliano; David N. Cole; Andy Leach; Pam Wright; Deb Bumpus

2009-01-01

The purpose of monitoring wilderness character is to improve wilderness stewardship by providing managers a tool to assess how selected actions and conditions related to wilderness character are changing over time. Wilderness character monitoring provides information to help answer two key questions about wilderness character and wilderness stewardship: 1. How is...

The prevalence of anxiety and depression in patients with or without hyperhidrosis (HH).

PubMed

Bahar, Rayeheh; Zhou, Pingyu; Liu, Yudan; Huang, Yuanshen; Phillips, Arlie; Lee, Tim K; Su, Mingwan; Yang, Sen; Kalia, Sunil; Zhang, Xuejun; Zhou, Youwen

2016-12-01

There are conflicting data about the correlation between hyperhidrosis (HH) and anxiety and depression. We sought to determine the prevalence of anxiety and depression in patients with or without HH. We examined 2017 consecutive dermatology outpatients from Vancouver, British Columbia, Canada, and Shanghai, China, using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 scales for anxiety and depression assessments. Multivariable logistic regression analysis was performed to evaluate if the impact of HH on anxiety and depression is dependent on demographic factors and diagnoses of the patients' presenting skin conditions. The prevalence of anxiety and depression was 21.3% and 27.2% in patients with HH, respectively, and 7.5% and 9.7% in patients without HH, respectively (P value <.001 for both). There were positive correlations between HH severity and the prevalence of anxiety and depression. Multivariable analysis showed that HH-associated increase in anxiety and depression prevalence is independent of demographic factors and presenting skin conditions. The data from the questionnaires relied on the accuracy of patients' self-reports. Both single variant and multivariable analyses showed a significant association between HH and the prevalence of anxiety and depression in a HH severity-dependent manner. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

A bispectral q-hypergeometric basis for a class of quantum integrable models

NASA Astrophysics Data System (ADS)

Baseilhac, Pascal; Martin, Xavier

2018-01-01

For the class of quantum integrable models generated from the q-Onsager algebra, a basis of bispectral multivariable q-orthogonal polynomials is exhibited. In the first part, it is shown that the multivariable Askey-Wilson polynomials with N variables and N + 3 parameters introduced by Gasper and Rahman [Dev. Math. 13, 209 (2005)] generate a family of infinite dimensional modules for the q-Onsager algebra, whose fundamental generators are realized in terms of the multivariable q-difference and difference operators proposed by Iliev [Trans. Am. Math. Soc. 363, 1577 (2011)]. Raising and lowering operators extending those of Sahi [SIGMA 3, 002 (2007)] are also constructed. In the second part, finite dimensional modules are constructed and studied for a certain class of parameters and if the N variables belong to a discrete support. In this case, the bispectral property finds a natural interpretation within the framework of tridiagonal pairs. In the third part, eigenfunctions of the q-Dolan-Grady hierarchy are considered in the polynomial basis. In particular, invariant subspaces are identified for certain conditions generalizing Nepomechie's relations. In the fourth part, the analysis is extended to the special case q = 1. This framework provides a q-hypergeometric formulation of quantum integrable models such as the open XXZ spin chain with generic integrable boundary conditions (q ≠ 1).

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Monitoring the ripening process of Cheddar cheese based on hydrophilic component profiling using gas chromatography-mass spectrometry.

PubMed

Ochi, H; Sakai, Y; Koishihara, H; Abe, F; Bamba, T; Fukusaki, E

2013-01-01

We proposed an application methodology that combines metabolic profiling with multiple appropriate multivariate analyses and verified it on the industrial scale of the ripening process of Cheddar cheese to make practical use of hydrophilic low-molecular-weight compound profiling using gas chromatography-mass spectrometry to design optimal conditions and quality monitoring of the cheese ripening process. Principal components analysis provided an overview of the effect of sodium chloride content and kind of lactic acid bacteria starter on the metabolic profile in the ripening process of Cheddar cheese and orthogonal partial least squares-discriminant analysis unveiled the difference in characteristic metabolites. When the sodium chloride contents were different (1.6 and 0.2%) but the same lactic acid bacteria starter was used, the 2 cheeses were classified by orthogonal partial least squares-discriminant analysis from their metabolic profiles, but were not given perfect discrimination. Not much difference existed in the metabolic profile between the 2 cheeses. Compounds including lactose, galactose, lactic acid, 4-aminobutyric acid, and phosphate were identified as contents that differed between the 2 cheeses. On the other hand, in the case of the same salt content of 1.6%, but different kinds of lactic acid bacteria starter, an excellent distinctive discrimination model was obtained, which showed that the difference of lactic acid bacteria starter caused an obvious difference in metabolic profiles. Compounds including lactic acid, lactose, urea, 4-aminobutyric acid, galactose, phosphate, proline, isoleucine, glycine, alanine, lysine, leucine, valine, and pyroglutamic acid were identified as contents that differed between the 2 cheeses. Then, a good sensory prediction model for "rich flavor," which was defined as "thick and rich, including umami taste and soy sauce-like flavor," was constructed based on the metabolic profile during ripening using partial least squares regression analysis. The amino acids proline, leucine, valine, isoleucine, pyroglutamic acid, alanine, glutamic acid, glycine, lysine, tyrosine, serine, phenylalanine, methionine, aspartic acid, and ornithine were extracted as ripening process markers. The present study is not limited to Cheddar cheese and can be applied to various maturation-type natural cheeses. This study provides the technical platform for designing optimal conditions and quality monitoring of the cheese ripening process. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Changes in water quality along the course of a river - Classic monitoring versus patrol monitoring

NASA Astrophysics Data System (ADS)

Absalon, Damian; Kryszczuk, Paweł; Rutkiewicz, Paweł

2017-11-01

Monitoring of water quality is a tool necessary to assess the condition of waterbodies in order to properly formulate water management plans. The paper presents the results of patrol monitoring of a 40-kilometre stretch of the Oder between Racibórz and Koźle. It has been established that patrol monitoring is a good tool for verifying the distribution of points of classic stationary monitoring, particularly in areas subject to varied human impact, where tributaries of the main river are very diversified as regards hydrochemistry. For this reason the results of operational monitoring carried out once every few years may not be reliable and the presented condition of the monitored waterbodies may be far from reality.

Social Network Type and Long-Term Condition Management Support: A Cross-Sectional Study in Six European Countries.

PubMed

Vassilev, Ivaylo; Rogers, Anne; Kennedy, Anne; Wensing, Michel; Koetsenruijter, Jan; Orlando, Rosanna; Portillo, Maria Carmen; Culliford, David

2016-01-01

Network types and characteristics have been linked to the capacity of inter-personal environments to mobilise and share resources. The aim of this paper is to examine personal network types in relation to long-term condition management in order to identify the properties of network types most likely to provide support for those with a long-term condition. A cross-sectional observational survey of people with type 2 diabetes using interviews and questionnaires was conducted between April and October 2013 in six European countries: Greece, Spain, Bulgaria, Norway, United Kingdom, and Netherlands. 1862 people with predominantly lower socio-economic status were recruited from each country. We used k-means clustering analysis to derive the network types, and one-way analysis of variance and multivariate logistic regression analysis to explore the relationship between network type socio-economic characteristics, self-management monitoring and skills, well-being, and network member work. Five network types of people with long-term conditions were identified: restricted, minimal family, family, weak ties, and diverse. Restricted network types represented those with the poorest self-management skills and were associated with limited support from social network members. Restricted networks were associated with poor indicators across self-management capacity, network support, and well-being. Diverse networks were associated with more enhanced self-management skills amongst those with a long-term condition and high level of emotional support. It was the three network types which had a large number of network members (diverse, weak ties, and family) where healthcare utilisation was most likely to correspond to existing health needs. Our findings suggest that type of increased social involvement is linked to greater self-management capacity and potentially lower formal health care costs indicating that diverse networks constitute the optimal network type as a policy in terms of the design of LTCM interventions and building support for people with LTCs.

Wireless sensing system for bridge condition assessment and health monitoring

NASA Astrophysics Data System (ADS)

Gangone, Michael V.; Whelan, Matthew J.; Janoyan, Kerop D.

2009-03-01

Discussed in this paper is the deployment of a universal and low-cost dense wireless sensor system for structural monitoring, load rating and condition assessment of bridges. The wireless sensor system developed is designed specifically for diagnostic bridge monitoring, providing independent conditioning for both accelerometers and strain transducers in addition to high-rate wireless data transmission. The system was field deployed on a three span simply supported bridge superstructure, where strain and acceleration measurements were obtained simultaneously and in realtime at critical locations under several loading conditions, providing reliable quantitative information as to the actual performance level of the bridge. Monitoring was also conducted as the bridge was subjected to various controlled damage scenarios on the final day of testing. Select cases of detected damage using strain and modal based analysis are presented.

System and Method for Monitoring Distributed Asset Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry (Inventor)

2015-01-01

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

Racial differences in abnormal ambulatory blood pressure monitoring measures: Results from the Coronary Artery Risk Development in Young Adults (CARDIA) study.

PubMed

Muntner, Paul; Lewis, Cora E; Diaz, Keith M; Carson, April P; Kim, Yongin; Calhoun, David; Yano, Yuichiro; Viera, Anthony J; Shimbo, Daichi

2015-05-01

Several ambulatory blood pressure monitoring (ABPM) measures have been associated with increased cardiovascular disease risk independent of clinic blood pressure (BP). African Americans have higher clinic BP compared with Whites but few data are available on racial differences in ABPM measures. We compared ABPM measures between African American (n = 178) and White (n = 103) participants at the Year 5 Coronary Artery Risk Development in Young Adults study visit. BP was measured during a study visit and the second and third measurements were averaged. ABPM was conducted over the following 24 hours. Mean ± SD age of participants was 29.8 ± 3.8 years and 30.8 ± 3.5 years for African Americans and Whites, respectively. Mean daytime systolic BP (SBP) was 3.90 (SD 1.18) mm Hg higher among African Americans compared with Whites (P < 0.001) after age-gender adjustment and 1.71 (SD 1.03) mm Hg higher after multivariable adjustment including mean clinic SBP (P = 0.10). After multivariable adjustment including mean clinic SBP, nighttime SBP was 4.83 (SD 1.11) mm Hg higher among African Americans compared with Whites (P < 0.001). After multivariable adjustment, the African Americans were more likely than Whites to have nocturnal hypertension (prevalence ratio: 2.44, 95% CI: 0.99-6.05) and nondipping (prevalence ratio: 2.50, 95% CI: 1.39-4.48). The prevalence of masked hypertension among African Americans and Whites was 4.4% and 2.1%, respectively, (P = 0.49) and white coat hypertension was 3.3% and 3.9%, respectively (P = 0.99). Twenty-four hour BP variability on ABPM was higher among African Americans compared with Whites. These data suggest racial differences in several ABPM measures exist. © American Journal of Hypertension, Ltd 2014. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

Optimisation of resolution in micellar electrokinetic chromatography by multivariate evaluation of electrolytes.

PubMed

Mikaeli, S; Thorsén, G; Karlberg, B

2001-01-12

A novel approach to multivariate evaluation of separation electrolytes for micellar electrokinetic chromatography is presented. An initial screening of the experimental parameters is performed using a Plackett-Burman design. Significant parameters are further evaluated using full factorial designs. The total resolution of the separation is calculated and used as response. The proposed scheme has been applied to the optimisation of the separation of phenols and the chiral separation of (+)-1-(9-anthryl)-2-propyl chloroformate-derivatized amino acids. A total of eight experimental parameters were evaluated and optimal conditions found in less than 48 experiments.

Multivariable manual control with simultaneous visual and auditory presentation of information. [for improved compensatory tracking performance of human operator

NASA Technical Reports Server (NTRS)

Uhlemann, H.; Geiser, G.

1975-01-01

Multivariable manual compensatory tracking experiments were carried out in order to determine typical strategies of the human operator and conditions for improvement of his performance if one of the visual displays of the tracking errors is supplemented by an auditory feedback. Because the tracking error of the system which is only visually displayed is found to decrease, but not in general that of the auditorally supported system, it was concluded that the auditory feedback unloads the visual system of the operator who can then concentrate on the remaining exclusively visual displays.

New Fast Beam Conditions Monitoring (BCM1F) system for CMS

NASA Astrophysics Data System (ADS)

Zagozdzinska, A. A.; Bell, A. J.; Dabrowski, A. E.; Hempel, M.; Henschel, H. M.; Karacheban, O.; Przyborowski, D.; Leonard, J. L.; Penno, M.; Pozniak, K. T.; Miraglia, M.; Lange, W.; Lohmann, W.; Ryjov, V.; Lokhovitskiy, A.; Stickland, D.; Walsh, R.

2016-01-01

The CMS Beam Radiation Instrumentation and Luminosity (BRIL) project is composed of several systems providing the experiment protection from adverse beam conditions while also measuring the online luminosity and beam background. Although the readout bandwidth of the Fast Beam Conditions Monitoring system (BCM1F—one of the faster monitoring systems of the CMS BRIL), was sufficient for the initial LHC conditions, the foreseen enhancement of the beams parameters after the LHC Long Shutdown-1 (LS1) imposed the upgrade of the system. This paper presents the new BCM1F, which is designed to provide real-time fast diagnosis of beam conditions and instantaneous luminosity with readout able to resolve the 25 ns bunch structure.

41.4 DEPLOYMENT OF DEDICATED NURSING STAFF TO STIMULATE THE INITIATION OF CLOZAPINE. A CLUSTER-RANDOMIZED TRIAL

PubMed Central

Van der Zalm, Yvonne; Schulte, Raphael; Bogers, Jan; Marcelis, Machteld; Sommer, Iris; Selten, Jean-Paul

2018-01-01

Abstract Background For patients with refractory schizophrenia, clozapine is the drug of first choice. However, many refractory patients never receive this drug. The underutilization of clozapine may be caused by the labour-intensive white blood cell monitoring during the first months and the concerns about the safety of outpatient clozapine initiation. A recent survey concluded that professionals “perceived the presence of dedicated staff to arrange and monitor the initiation of clozapine in outpatients as the factor that would enable the use of clozapine most”. We examined whether the presence of such staff in Dutch teams for ambulatory care makes a difference. The primary objective is to examine whether clozapine monitoring by a Nurse Practitioner (NP) is at least as safe as monitoring by a physician. The secondary objective is to examine whether physicians are more likely to prescribe clozapine if they can delegate the monitoring tasks to a NP. Methods In this cluster-randomized trial, 23 Dutch ambulatory care teams were randomized into 2 conditions: (A) coordination of clozapine monitoring by a Nurse Practitioner, versus (B) Treatment As Usual: coordination of clozapine monitoring by the responsible physician (usually a psychiatrist). We followed the teams for 15 months, during which period we counted the numbers of patients who started with clozapine. We assessed the safety of the clozapine monitoring by measuring the number of weekly lab exams performed during the first 18 weeks of treatment and counting serious adverse events (SAE). It is important to note that the staff of teams remained blind to the secondary research question. Results Of the 2643 patients with a diagnosis of non-affective psychotic disorder, 66 patients started using clozapine during the follow-up, 48 in condition A and 18 in condition B (RR: 2.14, 95% CI: 1.24–3.70; p=.005). The provisional results showed no significant differences between conditions A and B in the mean number of lab exams performed. In condition A, 65% of the mandatory lab exams were carried out compared to 60% in condition B. No agranulocytosis or other SAE occurred in Conditions A or B. Discussion Physicians prescribed over 2 times more often clozapine to patients when they could delegate the white blood cell monitoring to a NP. Clozapine-monitoring by an NP appears to be just as safe as monitoring by a physician. These results strongly support the idea that the presence of dedicated staff to arrange and monitor the initiation of clozapine enables the use of this drug.

Normal streamflows and water levels continue—Summary of hydrologic conditions in Georgia, 2014

USGS Publications Warehouse

Knaak, Andrew E.; Ankcorn, Paul D.; Peck, Michael F.

2016-03-31

The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 350 real-time, continuous-record, streamflow-gaging stations (streamgages). The network includes 14 real-time lake-level monitoring stations, 72 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 204 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits this monitoring network provides is a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia.Streamflow and groundwater data are verified throughout the year by USGS hydrographers and made available to water-resource managers, recreationists, and Federal, State, and local agencies. Hydrologic conditions are determined by comparing the statistical analyses of data collected during the current water year to historical data. Changing hydrologic conditions underscore the need for accurate, timely data to allow informed decisions about the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and in protecting life and property.

Use of email and telephone prompts to increase self-monitoring in a Web-based intervention: randomized controlled trial.

PubMed

Greaney, Mary L; Sprunck-Harrild, Kim; Bennett, Gary G; Puleo, Elaine; Haines, Jess; Viswanath, K Vish; Emmons, Karen M

2012-07-27

Self-monitoring is a key behavior change mechanism associated with sustained health behavior change. Although Web-based interventions can offer user-friendly approaches for self-monitoring, engagement with these tools is suboptimal. Increased use could encourage, promote, and sustain behavior change. To determine whether email prompts or email plus telephone prompts increase self-monitoring of behaviors on a website created for a multiple cancer risk reduction program. We recruited and enrolled participants (N = 100) in a Web-based intervention during a primary care well visit at an urban primary care health center. The frequency of daily self-monitoring was tracked on the study website. Participants who tracked at least one behavior 3 or more times during week 1 were classified as meeting the tracking threshold and were assigned to the observation-only group (OO, n = 14). This group was followed but did not receive prompts. Participants who did not meet the threshold during week 1 were randomly assigned to one of 2 prompting conditions: automated assistance (AA, n = 36) or automated assistance + calls (AAC, n = 50). During prompting periods (weeks 2-3), participants in the AA and AAC conditions received daily automated emails that encouraged tracking and two tailored self-monitoring reports (end of week 2, end of week 3) that provided feedback on tracking frequency. Individuals in the AAC condition also received two technical assistance calls from trained study staff. Frequency of self-monitoring was tracked from week 2 through week 17. Self-monitoring rates increased in both intervention conditions during prompting and declined when prompting ceased. Over the 16 weeks of observation, there was a significant between-group difference in the percentage who met the self-monitoring threshold each week, with better maintenance in the AAC than in the AA condition (P < .001). Self-monitoring rates were greater in the OO group than in either the AA or AAC condition (P < .001). Prompting can increase self-monitoring rates. The decrease in self-monitoring after the promoting period suggests that additional reminder prompts would be useful. The use of technical assistance calls appeared to have a greater effect in promoting self-monitoring at a therapeutic threshold than email reminders and the tailored self-monitoring reports alone. ClinicalTrials.gov NCT01415492; http://clinicaltrials.gov/ct2/show/NCT01415492 (Archived by WebCite at http://www.webcitation.org/68LOXOMe2).

Molecular Fingerprinting of Cyanobacteria from River Biofilms as a Water Quality Monitoring Tool

PubMed Central

Loza, Virginia; Perona, Elvira

2013-01-01

Benthic cyanobacterial communities from Guadarrama River (Spain) biofilms were examined using temperature gradient gel electrophoresis (TGGE), comparing the results with microscopic analyses of field-fixed samples and the genetic characterization of cultured isolates from the river. Changes in the structure and composition of cyanobacterial communities and their possible association with eutrophication in the river downstream were studied by examining complex TGGE patterns, band extraction, and subsequent sequencing of 16S rRNA gene fragments. Band profiles differed among sampling sites depending on differences in water quality. The results showed that TGGE band richness decreased in a downstream direction, and there was a clear clustering of phylotypes on the basis of their origins from different locations according to their ecological requirements. Multivariate analyses (cluster analysis and canonical correspondence analysis) corroborated these differences. Results were consistent with those obtained from microscopic observations of field-fixed samples. According to the phylogenetic analysis, morphotypes observed in natural samples were the most common phylotypes in the TGGE sequences. These phylotypes were closely related to Chamaesiphon, Aphanocapsa, Pleurocapsa, Cyanobium, Pseudanabaena, Phormidium, and Leptolyngbya. Differences in the populations in response to environmental variables, principally nutrient concentrations (dissolved inorganic nitrogen and soluble reactive phosphorus), were found. Some phylotypes were associated with low nutrient concentrations and high levels of dissolved oxygen, while other phylotypes were associated with eutrophic-hypertrophic conditions. These results support the view that once a community has been characterized and its genetic fingerprint obtained, this technique could be used for the purpose of monitoring rivers. PMID:23263954

Integrated fiber optical and thermal sensor for noninvasive monitoring of blood and human tissue

NASA Astrophysics Data System (ADS)

Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Schiffner, Gerhard

2007-05-01

A novel concept of noninvasive monitoring of human tissue and blood based on optical diffuse reflective spectroscopy combined with metabolic heat measurements has been under development. A compact integrated fiber optical and thermal sensor has been developed. The idea of the method was to evaluate by optical spectroscopy haemoglobin and derivative concentrations and supplement with data associated with the oxidative metabolism of glucose. Body heat generated by glucose oxidation is based on the balance of capillary glucose and oxygen supply to the cells. The variation in glucose concentration is followed also by a difference from a distance (or depth) of scattered through the body radiation. So, blood glucose can be estimated by measuring the body heat and the oxygen supply. The sensor pickup contains of halogen lamp and LEDs combined with fiber optical bundle to deliver optical radiation inside and through the patient body, optical and thermal detectors. Fiber optical probe allows diffuse scattering measurement down to a depth of 2.5 mm in the skin including vascular system, which contributes to the control of the body temperature. The sensor pickup measures thermal generation, heat balance, blood flow rate, haemoglobin and derivative concentrations, environmental conditions. Multivariate statistical analysis was applied to convert various signals from the sensor pickup into physicochemical variables. By comparing the values from the noninvasive measurement with the venous plasma result, analytical functions for patient were obtained. Cluster analysis of patient groups was used to simplify a calibration procedure. Clinical testing of developed sensor is being performed.

Temporal trends in algae, benthic invertebrate, and fish assemblages in streams and rivers draining basins of varying land use in the south-central United States, 1993-2007

USGS Publications Warehouse

Miller, Matthew P.; Kennen, Jonathan G.; Mabe, Jeffrey A.; Mize, Scott V.

2012-01-01

Site-specific temporal trends in algae, benthic invertebrate, and fish assemblages were investigated in 15 streams and rivers draining basins of varying land use in the south-central United States from 1993–2007. A multivariate approach was used to identify sites with statistically significant trends in aquatic assemblages which were then tested for correlations with assemblage metrics and abiotic environmental variables (climate, water quality, streamflow, and physical habitat). Significant temporal trends in one or more of the aquatic assemblages were identified at more than half (eight of 15) of the streams in the study. Assemblage metrics and abiotic environmental variables found to be significantly correlated with aquatic assemblages differed between land use categories. For example, algal assemblages at undeveloped sites were associated with physical habitat, while algal assemblages at more anthropogenically altered sites (agricultural and urban) were associated with nutrient and streamflow metrics. In urban stream sites results indicate that streamflow metrics may act as important controls on water quality conditions, as represented by aquatic assemblage metrics. The site-specific identification of biotic trends and abiotic–biotic relations presented here will provide valuable information that can inform interpretation of continued monitoring data and the design of future studies. In addition, the subsets of abiotic variables identified as potentially important drivers of change in aquatic assemblages provide policy makers and resource managers with information that will assist in the design and implementation of monitoring programs aimed at the protection of aquatic resources.

Integrated condition monitoring of a fleet of offshore wind turbines with focus on acceleration streaming processing

NASA Astrophysics Data System (ADS)

Helsen, Jan; Gioia, Nicoletta; Peeters, Cédric; Jordaens, Pieter-Jan

2017-05-01

Particularly offshore there is a trend to cluster wind turbines in large wind farms, and in the near future to operate such a farm as an integrated power production plant. Predictability of individual turbine behavior across the entire fleet is key in such a strategy. Failure of turbine subcomponents should be detected well in advance to allow early planning of all necessary maintenance actions; Such that they can be performed during low wind and low electricity demand periods. In order to obtain the insights to predict component failure, it is necessary to have an integrated clean dataset spanning all turbines of the fleet for a sufficiently long period of time. This paper illustrates our big-data approach to do this. In addition, advanced failure detection algorithms are necessary to detect failures in this dataset. This paper discusses a multi-level monitoring approach that consists of a combination of machine learning and advanced physics based signal-processing techniques. The advantage of combining different data sources to detect system degradation is in the higher certainty due to multivariable criteria. In order to able to perform long-term acceleration data signal processing at high frequency a streaming processing approach is necessary. This allows the data to be analysed as the sensors generate it. This paper illustrates this streaming concept on 5kHz acceleration data. A continuous spectrogram is generated from the data-stream. Real-life offshore wind turbine data is used. Using this streaming approach for calculating bearing failure features on continuous acceleration data will support failure propagation detection.

Next-generation sequencing-based detection of circulating tumour DNA After allogeneic stem cell transplantation for lymphoma.

PubMed

Herrera, Alex F; Kim, Haesook T; Kong, Katherine A; Faham, Malek; Sun, Heather; Sohani, Aliyah R; Alyea, Edwin P; Carlton, Victoria E; Chen, Yi-Bin; Cutler, Corey S; Ho, Vincent T; Koreth, John; Kotwaliwale, Chitra; Nikiforow, Sarah; Ritz, Jerome; Rodig, Scott J; Soiffer, Robert J; Antin, Joseph H; Armand, Philippe

2016-12-01

Next-generation sequencing (NGS)-based circulating tumour DNA (ctDNA) detection is a promising monitoring tool for lymphoid malignancies. We evaluated whether the presence of ctDNA was associated with outcome after allogeneic haematopoietic stem cell transplantation (HSCT) in lymphoma patients. We studied 88 patients drawn from a phase 3 clinical trial of reduced-intensity conditioning HSCT in lymphoma. Conventional restaging and collection of peripheral blood samples occurred at pre-specified time points before and after HSCT and were assayed for ctDNA by sequencing of the immunoglobulin or T-cell receptor genes. Tumour clonotypes were identified in 87% of patients with adequate tumour samples. Sixteen of 19 (84%) patients with disease progression after HSCT had detectable ctDNA prior to progression at a median of 3·7 months prior to relapse/progression. Patients with detectable ctDNA 3 months after HSCT had inferior progression-free survival (PFS) (2-year PFS 58% vs. 84% in ctDNA-negative patients, P = 0·033). In multivariate models, detectable ctDNA was associated with increased risk of progression/death (Hazard ratio 3·9, P = 0·003) and increased risk of relapse/progression (Hazard ratio 10·8, P = 0·0006). Detectable ctDNA is associated with an increased risk of relapse/progression, but further validation studies are necessary to confirm these findings and determine the clinical utility of NGS-based minimal residual disease monitoring in lymphoma patients after HSCT. © 2016 John Wiley & Sons Ltd.

NMR fingerprinting as a tool to evaluate post-harvest time-related changes of peaches, tomatoes and plums.

PubMed

Santucci, Claudio; Tenori, Leonardo; Luchinat, Claudio

2015-09-01

The time-related changes of three agricultural products, coming from two distribution routes, have been followed using NMR fingerprinting to monitor metabolic variations occurring during several days of cold storage. An NMR profiling approach was employed to evaluate the variations in metabolic profile and metabolite content in three different agricultural products highly consumed in Italy (peaches, tomatoes and plums) coming from Tuscanian farms and how they change with time after collection. For each product, we followed the time-related changes during cold storage along three different collection periods. We monitored the variations in metabolic fingerprint and the trend of a set of metabolites, focusing our attention on nutritive and health-promoting metabolites (mainly, essential amino acids and antioxidants) as well as metabolites that contribute to the taste. Concurrently, for comparison, the time-dependent changes of the same kind of products coming from large-scale distribution have been also analyzed under the same conditions. In this second category, only slight variations in the metabolic fingerprint and metabolite levels were seen during cold storage. Unsupervised and supervised multivariate statistics was also employed to enlighten the differences between the three collections. In particular it seems that the metabolic fingerprint of large-scale distribution products is quite similar in the early, middle and late collection, while peaches and plums locally collected are markedly different among the three periods. The metabolic profiles of the agricultural products belonging to these two different distribution routes are intrinsically different, and they show different changes during the time of cold storage. Copyright © 2015 Elsevier Ltd. All rights reserved.

A multiplexed analysis approach identifies new association of inflammatory proteins in patients with overactive bladder

PubMed Central

Ma, Emily; Vetter, Joel; Bliss, Laura; Lai, H. Henry; Mysorekar, Indira U.

2016-01-01

Overactive bladder (OAB) is a common debilitating bladder condition with unknown etiology and limited diagnostic modalities. Here, we explored a novel high-throughput and unbiased multiplex approach with cellular and molecular components in a well-characterized patient cohort to identify biomarkers that could be reliably used to distinguish OAB from controls or provide insights into underlying etiology. As a secondary analysis, we determined whether this method could discriminate between OAB and other chronic bladder conditions. We analyzed plasma samples from healthy volunteers (n = 19) and patients diagnosed with OAB, interstitial cystitis/bladder pain syndrome (IC/BPS), or urinary tract infections (UTI; n = 51) for proinflammatory, chemokine, cytokine, angiogenesis, and vascular injury factors using Meso Scale Discovery (MSD) analysis and urinary cytological analysis. Wilcoxon rank-sum tests were used to perform univariate and multivariate comparisons between patient groups (controls, OAB, IC/BPS, and UTI). Multivariate logistic regression models were fit for each MSD analyte on 1) OAB patients and controls, 2) OAB and IC/BPS patients, and 3) OAB and UTI patients. Age, race, and sex were included as independent variables in all multivariate analysis. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic potential of a given analyte. Our findings demonstrate that five analytes, i.e., interleukin 4, TNF-α, macrophage inflammatory protein-1β, serum amyloid A, and Tie2 can reliably differentiate OAB relative to controls and can be used to distinguish OAB from the other conditions. Together, our pilot study suggests a molecular imbalance in inflammatory proteins may contribute to OAB pathogenesis. PMID:27029431

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Multivariable control altitude demonstration on the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.

1979-01-01

The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

The Provision of Diabetes-Monitoring Exams to Older Latinos

PubMed Central

Herrera, Angelica P.; Lee Smith, Matthew; Ory, Marcia G.; Rodriguez, Hector P.; Warre, Ruth; Thompson, Wesley K.; Azcue, Annette; Romero, Jairo A.

2012-01-01

Objectives To explore factors associated with the provision of diabetes-monitoring practices among older Latinos with type 2 diabetes. Method Data from 547 Latinos (≥55 years) were analyzed from the 2007 California Health Interview Survey. Multivariate logistic regression modeled the relationship between health status and sociodemographic factors and the receipt of semiannual HbA1c tests, annual foot exams, and annual retinal exams. Results The majority of older Latino diabetics received foot exams (87%) and retinal exams (77%), but the provision of semiannual HbA1c tests (30%) was low. Higher English-language proficiency and health insurance coverage were associated with the provision of HbA1c tests and foot exams, but not retinal exams. Insulin therapy was positively associated with semiannual HbA1c testing, but negatively associated with foot exams. Discussion There are considerable missed opportunities in the provision of diabetes monitoring for older Latinos, particularly those with limited English proficiency, less comprehensive insurance, and noninsulin therapy. PMID:21948771

Space and time resolved monitoring of airborne particulate matter in proximity of a traffic roundabout in Sweden.

PubMed

Wilkinson, Kai E; Lundkvist, Johanna; Netrval, Julia; Eriksson, Mats; Seisenbaeva, Gulaim A; Kessler, Vadim G

2013-11-01

Concerns over exposure to airborne particulate matter (PM) are on the rise. Currently monitoring of PM is done on the basis of interpolating a mass of PM by volume (μg/m(3)) but has the drawback of not taking the chemical nature of PM into account. Here we propose a method of collecting PM at its emission source and employing automated analysis with scanning electron microscopy associated with EDS-analysis together with light scattering to discern the chemical composition, size distribution, and time and space resolved structure of PM emissions in a heavily trafficated roundabout in Sweden. Multivariate methods (PCA, ANOVA) indicate that the technogenic marker Fe follows roadside dust in spreading from the road, and depending on time and location of collection, a statistically significant difference can be seen, adding a useful tool to the repertoiré of detailed PM monitoring and risk assessment of local emission sources. Copyright © 2013 Elsevier Ltd. All rights reserved.

Brief mindfulness meditation training reduces mind wandering: The critical role of acceptance.

PubMed

Rahl, Hayley A; Lindsay, Emily K; Pacilio, Laura E; Brown, Kirk W; Creswell, J David

2017-03-01

Mindfulness meditation programs, which train individuals to monitor their present-moment experience in an open or accepting way, have been shown to reduce mind wandering on standardized tasks in several studies. Here we test 2 competing accounts for how mindfulness training reduces mind wandering, evaluating whether the attention-monitoring component of mindfulness training alone reduces mind wandering or whether the acceptance training component is necessary for reducing mind wandering. Healthy young adults (N = 147) were randomized to either a 3-day brief mindfulness training condition incorporating instruction in both attention monitoring and acceptance, a mindfulness training condition incorporating attention monitoring instruction only, a relaxation training condition, or an active reading-control condition. Participants completed measures of dispositional mindfulness and treatment expectancies before the training session on Day 1 and then completed a 6-min Sustained Attention to Response Task (SART) measuring mind wandering after the training session on Day 3. Acceptance training was important for reducing mind wandering, such that the attention-monitoring plus acceptance mindfulness training condition had the lowest mind wandering relative to the other conditions, including significantly lower mind wandering than the attention-monitoring only mindfulness training condition. In one of the first experimental mindfulness training dismantling studies to-date, we show that training in acceptance is a critical driver of mindfulness-training reductions in mind wandering. This effect suggests that acceptance skills may facilitate emotion regulation on boring and frustrating sustained attention tasks that foster mind wandering, such as the SART. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

LANDSCAPE METRICS THAT ARE USEFUL FOR EXPLAINING ESTUARINE ECOLOGICAL RESPONSES

EPA Science Inventory

We investigated whether land use/cover characteristics of watersheds associated with estuaries exhibit a strong enough signal to make landscape metrics useful for predicting estuarine ecological condition. We used multivariate logistic regression models to discriminate between su...

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

PubMed

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H; Fischl, Bruce

2016-07-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?" Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Statistical Analysis of Diffusion Imaging Parameters using Partial Least Squares: Application to White Matter Variations in Alzheimer’s Disease

PubMed Central

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H.; Fischl, Bruce

2016-01-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer’s and Huntington’s diseases1,2. The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as Diffusion Tensor Imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer’s disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: “are there regions in the white matter where Alzheimer’s disease has a different effect than aging or similar effect as aging?” and “are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer’s disease but with differing multivariate effects?” PMID:27103138

Kinetics of lisinopril intramolecular cyclization in solid phase monitored by Fourier transform infrared microscopy.

PubMed

Widjaja, Effendi; Tan, Wei Jian

2008-08-01

The solid-state intramolecular cyclization of lisinopril to diketopiperazine was investigated by in situ Fourier transform infrared (FT-IR) microscopy. Using a controllable heating cell, the isothermal transformation was monitored in situ at 147.5, 150, 152.5, 155, and 157.5 degrees C. The collected time-dependent FT-IR spectra at each isothermal temperature were preprocessed and analyzed using a multivariate chemometric approach. The pure component spectra of the observable component (lisinopril and diketopiperazine) were resolved and their time-dependent relative contributions were also determined. Model-free and various model fitting methods were implemented in the kinetic analysis to estimate the activation energy of the intramolecular cyclization reaction. Arrhenius plots indicate that the activation energy is circa 327 kJ/mol.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

PubMed

Nespeca, Maurilio Gustavo; Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

PubMed Central

Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

Compound effects of temperature and precipitation in making droughts more frequent in Marathwada, India

NASA Astrophysics Data System (ADS)

Mondal, A.; Zachariah, M.; Achutarao, K. M.; Otto, F. E. L.

2017-12-01

The Marathwada region in Maharashtra, India is known to suffer significantly from agrarian crisis including farmer suicides resulting from persistent droughts. Drought monitoring in India is commonly based on univariate indicators that consider the deficiency in precipitation alone. However, droughts may involve complex interplay of multiple physical variables, necessitating an integrated, multivariate approach to analyse their behaviour. In this study, we compare the behaviour of drought characteristics in Marathwada in the recent years as compared to the first half of the twentieth century, using a joint precipitation and temperature-based Multivariate Standardized Drought Index (MSDI). Drought events in the recent times are found to exhibit exceptional simultaneous anomalies of high temperature and precipitation deficits in this region, though studies on precipitation alone show that these events are within the range of historically observed variability. Additionally, we also develop multivariate copula-based Severity-Duration-Frequency (SDF) relationships for droughts in this region and compare their natures pre- and post- 1950. Based on multivariate return periods considering both temperature and precipitation anomalies, as well as the severity and duration of droughts, it is found that droughts have become more frequent in the post-1950 period. Based on precipitation alone, such an observation cannot be made. This emphasizes the sensitivity of droughts to temperature and underlines the importance of considering compound effects of temperature and precipitation in order to avoid an underestimation of drought risk. This observation-based analysis is the first step towards investigating the causal mechanisms of droughts, their evolutions and impacts in this region, particularly those influenced by anthropogenic climate change.

Reconfigurable Sensor Monitoring System

NASA Technical Reports Server (NTRS)

Alhorn, Dean C. (Inventor); Dutton, Kenneth R. (Inventor); Howard, David E. (Inventor); Smith, Dennis A. (Inventor)

2017-01-01

A reconfigurable sensor monitoring system includes software tunable filters, each of which is programmable to condition one type of analog signal. A processor coupled to the software tunable filters receives each type of analog signal so-conditioned.

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

PubMed Central

Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

2014-01-01

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Environmental characteristics associated with the occurrence of avian botulism in wetlands of a northern California refuge

USGS Publications Warehouse

Rocke, Tonie E.; Euliss, Ned H.; Samuel, Michael D.

1999-01-01

Avian botulism is an important disease affecting many species of waterbirds in North America, but the environmental conditions that initiate outbreaks are poorly understood. To determine wetland attributes associated with outbreaks of avian botulism in waterbirds at the Sacramento National Wildlife Refuge (SNWR), California, we compared environmental characteristics between wetlands where outbreaks occurred (outbreak wetlands) and did not occur (nonoutbreak wetlands). In June through October, 1987 89, we monitored the occurrence of avian botulism via observations for sick or dead sentinel mallards (Anas platyrhynchos) placed in 4 wetland enclosures. During this same time period, we collected environmental data from the water column and sediments of each wetland enclosure at 10 14-day sampling intervals. Multivariate analysis was used to reduce 22 environmental variables to 7 factors for inclusion in subsequent statistical analyses. We found that outbreak wetlands had significantly lower redox potential than nonoutbreak wetlands. The probability of botulism in sentinel mallards was associated with increasing temperature, increasing invertebrate abundance or biomass, and decreasing turbidity. However, because these factors were not consistently higher in outbreak wetlands compared to nonoutbreak wetlands, they may have a more proximate effect in initiating an outbreak.

Environmental characteristics associated with the occurrence of avian botulism in wetlands on a northern California refuge

USGS Publications Warehouse

Rocke, Tonie E.; Euliss, Ned H.; Samuel, Michael D.

1999-01-01

Avian botulism is an important disease affecting many species of waterbirds in North America, but the environmental conditions that initiate outbreaks are poorly understood. To determine wetland attributes associated with outbreaks of avian botulism in waterbirds at the Sacramento National Wildlife Refuge (SNWR), California, we compared environmental characteristics between wetlands where outbreaks occurred (outbreak wetlands) and did not occur (nonoutbreak wetlands). In June through October 1987-89, we monitored the occurrence of avian botulism via observations for sick or dead sentinel mallards (Anas platyrhynchos) placed in 4 wetland enclosures. During this same time period, we collected environmental data from the water column and sediments of each wetland enclosure at 10-14-day sampling intervals. Multivariate analysis was used to reduce 22 environmental variables to 7 factors for inclusion in subsequent statistical analyses. We found that outbreak wetlands had significantly lower redox potential than nonoutbreak wetlands. The probability of botulism in sentinel mallards was associated with increasing temperature, increasing invertebrate abundance or biomass, and decreasing turbidity. However, because these factors were not consistently higher in outbreak wetlands compared to nonoutbreak wetlands, they may have a more proximate effect in initiating an outbreak.

Epidemiology of leisure, transportation, occupational, and household physical activity: prevalence and associated factors.

PubMed

Florindo, Alex Antonio; Guimarães, Vanessa Valente; Cesar, Chester Luiz Galvão; Barros, Marilisa Berti de Azevedo; Alves, Maria Cecília Goi Porto; Goldbaum, Moisés

2009-09-01

To estimate the prevalence of and identify factors associated with physical activity in leisure, transportation, occupational, and household settings. This was a cross-sectional study aimed at investigating living and health conditions among the population of São Paulo, Brazil. Data on 1318 adults aged 18 to 65 years were used. To assess physical activity, the long version of the International Physical Activity Questionnaire was applied. Multivariate analysis was conducted using a hierarchical model. The greatest prevalence of insufficient activity related to transportation (91.7%), followed by leisure (77.5%), occupational (68.9%), and household settings (56.7%). The variables associated with insufficient levels of physical activity in leisure were female sex, older age, low education level, nonwhite skin color, smoking, and self-reported poor health; in occupational settings were female sex, white skin color, high education level, self-reported poor health, nonsmoking, and obesity; in transportation settings were female sex; and in household settings, with male sex, separated, or widowed status and high education level. Physical activity in transportation and leisure settings should be encouraged. This study will serve as a reference point in monitoring different types of physical activities and implementing public physical activity policies in developing countries.

LC-MS phenolic profiling combined with multivariate analysis as an approach for the characterization of extra virgin olive oils of four rare Tunisian cultivars during ripening.

PubMed

Ben Brahim, Samia; Kelebek, Hasim; Ammar, Sonda; Abichou, Mounir; Bouaziz, Mohamed

2017-08-15

In this work, the phenolic composition of four rare cultivars grown under the same agronomical and environmental conditions was studied. This is to test the effects of cultivars and ripening index essentially on phenolic composition in olive oils as well as tocopherols composition, organoleptic profiling and oxidative properties. Furthermore, some agronomical traits were determined in which a general increase in the size of the fruit and oil contents were recorded for all cultivars. The phenolic fractions were identified and quantified using liquid chromatography coupled to diode array detection and electrospray ionization tandem mass spectrometry (LC-DAD-ESI-MS/MS) in multiple reaction monitoring mode (MRM). A total of 13 phenolic compounds belonging to different chemical families were determined. Qualitative and quantitative differences in phenolic composition were observed among cultivars and also among sampling times. On the contrary to the agronomical traits, a general decrease (p<0.05) of total phenolic compounds was observed during maturation. Likewise, a decrease in tocopherols concentrations and oxidative properties was observed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

PubMed Central

Nardelli, Mimma; Valenza, Gaetano; Cristea, Ioana A.; Gentili, Claudio; Cotet, Carmen; David, Daniel; Lanata, Antonio; Scilingo, Enzo P.

2015-01-01

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions. PMID:25859212

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

NASA Astrophysics Data System (ADS)

Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali

2017-07-01

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.

Robust Control of Multivariable and Large Scale Systems.

DTIC Science & Technology

1986-03-14

AD-A175 $5B ROBUST CONTROL OF MULTIVRRIALE AND LARG SCALE SYSTEMS V2 R75 (U) HONEYWELL SYSTEMS AND RESEARCH CENTER MINNEAPOLIS MN J C DOYLE ET AL...ONIJQ 86 R alFS ja ,.AMIECFOEPF:ORMING ORGANIZATION So OFFICE SYMBOL 7a NAME OF MONITORING ORGANIZATI ON jonevwell Systems & Research If 4000c" Air...Force Office of Scientific Research .~ C :AE S C.rv. Stare arma ZIP Code) 7C ADDRESS (Crty. Stare. am ZIP Code, *3660 Marshall Street NE Building 410

How to create a marketing strategy based on hospital characteristics that attract physicians.

PubMed

Nordstrom, R D; Horton, D E; Hatcher, M E

1987-03-01

Through use of multivariate statistical and research techniques, the authors analyzed 30 hospital features that contribute to a physician's image of a hospital as being a good or a poor place for patient admission and in which to practice. Use of the data obtained in this study can enable a hospital administrator to monitor changes in physicians' attitudes, plan strategies to encourage quality physicians to admit their patients, improve aspects perceived to be weak or unresponsive, and capitalize on strengths.

Optical assay for biotechnology and clinical diagnosis.

PubMed

Moczko, Ewa; Cauchi, Michael; Turner, Claire; Meglinski, Igor; Piletsky, Sergey

2011-08-01

In this paper, we present an optical diagnostic assay consisting of a mixture of environmental-sensitive fluorescent dyes combined with multivariate data analysis for quantitative and qualitative examination of biological and clinical samples. The performance of the assay is based on the analysis of spectrum of the selected fluorescent dyes with the operational principle similar to electronic nose and electronic tongue systems. This approach has been successfully applied for monitoring of growing cell cultures and identification of gastrointestinal diseases in humans.

Unraveling fabrication and calibration of wearable gas monitor for use under free-living conditions.

PubMed

Yue Deng; Cheng Chen; Tsow, Francis; Xiaojun Xian; Forzani, Erica

2016-08-01

Volatile organic compounds (VOC) are organic chemicals that have high vapor pressure at regular conditions. Some VOC could be dangerous to human health, therefore it is important to determine real-time indoor and outdoor personal exposures to VOC. To achieve this goal, our group has developed a wearable gas monitor with a complete sensor fabrication and calibration protocol for free-living conditions. Correction factors for calibrating the sensors, including sensitivity, aging effect, and temperature effect are implemented into a Quick Response Code (QR code), so that the pre-calibrated quartz tuning fork (QTF) sensor can be used with the wearable monitor under free-living conditions.

Dual computer monitors to increase efficiency of conducting systematic reviews.

PubMed

Wang, Zhen; Asi, Noor; Elraiyah, Tarig A; Abu Dabrh, Abd Moain; Undavalli, Chaitanya; Glasziou, Paul; Montori, Victor; Murad, Mohammad Hassan

2014-12-01

Systematic reviews (SRs) are the cornerstone of evidence-based medicine. In this study, we evaluated the effectiveness of using two computer screens on the efficiency of conducting SRs. A cohort of reviewers before and after using dual monitors were compared with a control group that did not use dual monitors. The outcomes were time spent for abstract screening, full-text screening and data extraction, and inter-rater agreement. We adopted multivariate difference-in-differences linear regression models. A total of 60 SRs conducted by 54 reviewers were included in this analysis. We found a significant reduction of 23.81 minutes per article in data extraction in the intervention group relative to the control group (95% confidence interval: -46.03, -1.58, P = 0.04), which was a 36.85% reduction in time. There was no significant difference in time spent on abstract screening, full-text screening, or inter-rater agreement between the two groups. Using dual monitors when conducting SRs is associated with significant reduction of time spent on data extraction. No significant difference was observed on time spent on abstract screening or full-text screening. Using dual monitors is one strategy that may improve the efficiency of conducting SRs. Copyright © 2014 Elsevier Inc. All rights reserved.

Aquatic vegetation and trophic condition of Cape Cod (Massachusetts, U.S.A.) kettle ponds

USGS Publications Warehouse

Roman, C.T.; Barrett, N.E.; Portnoy, J.W.

2001-01-01

The species composition and relative abundance of aquatic macrophytes was evaluated in five Cape Cod, Massachusetts, freshwater kettle ponds, representing a range of trophic conditions from oligotrophic to eutrophic. At each pond, aquatic vegetation and environmental variables (slope, water depth, sediment bulk density, sediment grain size, sediment organic content and porewater inorganic nutrients) were measured along five transects extending perpendicular to the shoreline from the upland border into the pond. Based on a variety of multivariate methods, including Detrended Correspondence Analysis (DCA), an indirect gradient analysis technique, and Canonical Correspondence Analysis (CCA), a direct gradient approach, it was determined that the eutrophic Herring Pond was dominated by floating aquatic vegetation (Brasenia schreberi, Nymphoides cordata, Nymphaea odorata), and the algal stonewort, Nitella. Partial CCA suggested that high porewater PO4-P concentrations and fine-grained sediments strongly influenced the vegetation of this eutrophic pond. In contrast, vegetation of the oligotrophic Duck Pond was sparse, contained no floating aquatics, and was dominated by emergent plants. Low porewater nutrients, low sediment organic content, high water clarity and low pH (4.8) best defined the environmental characteristics of this oligotrophic pond. Gull Pond, with inorganic nitrogen-enriched sediments, also exhibited a flora quite different from the oligotrophic Duck Pond. The species composition and relative abundance of aquatic macrophytes provide good indicators of the trophic status of freshwater ponds and should be incorporated into long-term monitoring programs aimed at detecting responses to anthropogenically-derived nutrient loading.

Aquatic vegetation and trophic condition of Cape Cod (Massachusetts, USA) kettle ponds

USGS Publications Warehouse

Roman, C.T.; Barrett, N.E.; Portnoy, J.W.

2001-01-01

The species composition and relative abundance of aquatic macrophytes was evaluated in five Cape Cod, Massachusetts, freshwater kettle ponds, representing a range of trophic conditions from oligotrophic to eutrophic. At each pond, aquatic vegetation and environmental variables (slope, water depth, sediment bulk density, sediment grain size, sediment organic content and porewater inorganic nutrients) were measured along five transects extending perpendicular to the shoreline from the upland border into the pond. Based on a variety of multivariate methods, including Detrended Correspondence Analysis (DCA), an indirect gradient analysis technique, and Canonical Correspondence Analysis (CCA), a direct gradient approach, it was determined that the eutrophic Herring Pond was dominated by floating aquatic vegetation (Brasenia schreberi, Nymphoides cordata, Nymphaea odorata), and the algal stonewort, Nitella. Partial CCA suggested that high porewater PO4-P concentrations and fine-grained sediments strongly influenced the vegetation of this eutrophic pond. In contrast, vegetation of the oligotrophic Duck Pond was sparse, contained no floating aquatics, and was dominated by emergent plants. Low porewater nutrients, low sediment organic content, high water clarity and low pH (4.8) best defined the environmental characteristics of this oligotrophic pond. Gull Pond, with inorganic nitrogen-enriched sediments, also exhibited a flora quite different from the oligotrophic Duck Pond. The species composition and relative abundance of aquatic macrophytes provide good indicators of the trophic status of freshwater ponds and should be incorporated into long-term monitoring programs aimed at detecting responses to anthropogenically-derived nutrient loading.

Source origin and parameters influencing levels of heavy metals in TSP, in an industrial background area of Southern Italy

NASA Astrophysics Data System (ADS)

Ragosta, Maria; Caggiano, Rosa; D'Emilio, Mariagrazia; Macchiato, Maria

In this paper, we investigate the relationships among atmospheric concentration of trace elements and some meteorological parameters. In particular, the effects of different meteorological conditions on heavy metal levels are interpreted by means of a multivariate statistical approach. The analysed variables were measured during a monitoring survey that started in 1997, and this survey was carried out in order to evaluate the atmospheric concentrations of heavy metals in the industrial area of Tito Scalo (Basilicata Region, Southern Italy). Here we present and analyse the data set collected from 1997 to 1999. The data set includes daily concentrations of total suspended particulates (TSP), daily concentrations of eight metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in TSP and daily meteoclimatic data (temperature, rainfall, speed and wind directions). Both the concentration level and the occurrence of peak concentration events are consistent with the characteristics of the study area: abundant small and medium industrial plants in a mountainous and unpolluted zone. Regarding the origin of sources of heavy metals in TSP, the statistical procedure allows us to identify three profiles: SP 1 and SP 2 related to industrial sources and SP 3 related to other sources (natural and/or anthropogenic). In particular, taking into account the effect of different meteorological conditions, we are able to distinguish the contribution of different fractions of the same metal in the detected source profiles.

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Quality by Design approach to investigate tablet dissolution shift upon accelerated stability by multivariate methods.

PubMed

Huang, Jun; Goolcharran, Chimanlall; Ghosh, Krishnendu

2011-05-01

This paper presents the use of experimental design, optimization and multivariate techniques to investigate root-cause of tablet dissolution shift (slow-down) upon stability and develop control strategies for a drug product during formulation and process development. The effectiveness and usefulness of these methodologies were demonstrated through two application examples. In both applications, dissolution slow-down was observed during a 4-week accelerated stability test under 51°C/75%RH storage condition. In Application I, an experimental design was carried out to evaluate the interactions and effects of the design factors on critical quality attribute (CQA) of dissolution upon stability. The design space was studied by design of experiment (DOE) and multivariate analysis to ensure desired dissolution profile and minimal dissolution shift upon stability. Multivariate techniques, such as multi-way principal component analysis (MPCA) of the entire dissolution profiles upon stability, were performed to reveal batch relationships and to evaluate the impact of design factors on dissolution. In Application II, an experiment was conducted to study the impact of varying tablet breaking force on dissolution upon stability utilizing MPCA. It was demonstrated that the use of multivariate methods, defined as Quality by Design (QbD) principles and tools in ICH-Q8 guidance, provides an effective means to achieve a greater understanding of tablet dissolution upon stability. Copyright © 2010 Elsevier B.V. All rights reserved.

Wireless pilot monitoring system for extreme race conditions.

PubMed

Pino, Esteban J; Arias, Diego E; Aqueveque, Pablo; Melin, Pedro; Curtis, Dorothy W

2012-01-01

This paper presents the design and implementation of an assistive device to monitor car drivers under extreme conditions. In particular, this system is designed in preparation for the 2012 Atacama Solar Challenge to be held in the Chilean desert. Actual preliminary results show the feasibility of such a project including physiological and ambient sensors, real-time processing algorithms, wireless data transmission and a remote monitoring station. Implementation details and field results are shown along with a discussion of the main problems found in real-life telemetry monitoring.

The detection of 4 vital signs of in-patients Using fuzzy database

NASA Astrophysics Data System (ADS)

Haris Rangkuti, A.; Erlisa Rasjid, Zulfany

2014-03-01

Actually in order to improve in the performance of the Hospital's administrator, by serve patients effectively and efficiently, the role of information technology become the dominant support. Especially when it comes to patient's conditions, such that it will be reported to a physician as soon as possible, including monitoring the patient's conditions regularly. For this reason it is necessary to have a Hospital Monitoring Information System, that is able to provide information about the patient's condition which is based on the four vital signs, temperature, blood pressure, pulse, and respiration. To monitor the 4 vital signs, the concept of fuzzy logic is used, where the vital signs number approaches 1 then the patient is close to recovery, and on the contrary, when the vital signs number approaches 0 then the patient still has problems. This system also helps nurses to provide answers to the relatives of patients, who wants to know the development of the patient's condition, including the recovery percentage based on the average of Fuzzy max from the 4 vital signs. Using Fuzzy-based monitoring system, the monitoring of the patient's condition becomes simpler and easier.

Intelligent Performance Analysis with a Natural Language Interface

NASA Astrophysics Data System (ADS)

Juuso, Esko K.

2017-09-01

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

PEM fuel cell monitoring system

DOEpatents

Meltser, Mark Alexander; Grot, Stephen Andreas

1998-01-01

Method and apparatus for monitoring the performance of H.sub.2 --O.sub.2 PEM fuel cells. Outputs from a cell/stack voltage monitor and a cathode exhaust gas H.sub.2 sensor are corrected for stack operating conditions, and then compared to predetermined levels of acceptability. If certain unacceptable conditions coexist, an operator is alerted and/or corrective measures are automatically undertaken.

Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

NASA Astrophysics Data System (ADS)

Lu, Siliang; Zhou, Peng; Wang, Xiaoxian; Liu, Yongbin; Liu, Fang; Zhao, Jiwen

2018-02-01

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used frequently in monitoring vital equipment. Benefiting from the development of data mining technologies, the massive data generated by sensors facilitate condition monitoring and fault diagnosis. However, too much data increase storage space, energy consumption, and computing resource, which can be considered fatal weaknesses for a WSN with limited resources. This study investigates a new method for motor bearings condition monitoring and fault diagnosis using the undersampled vibration signals acquired from a WSN. The proposed method, which is a fusion of the kurtogram, analog domain bandpass filtering, bandpass sampling, and demodulated resonance technique, can reduce the sampled data length while retaining the monitoring and diagnosis performance. A WSN prototype was designed, and simulations and experiments were conducted to evaluate the effectiveness and efficiency of the proposed method. Experimental results indicated that the sampled data length and transmission time of the proposed method result in a decrease of over 80% in comparison with that of the traditional method. Therefore, the proposed method indicates potential applications on condition monitoring and fault diagnosis of motor bearings installed in remote areas, such as wind farms and offshore platforms.

Prevalence of comorbidities and management of gout in a tropical city in Australia.

PubMed

Jeyaruban, Andrew; Soden, Muriel; Larkins, Sarah

2016-12-01

To examine the management of gout in general practice in Townsville, Australia, and to explore comorbid conditions in patients with gout. Study will also explore how closely guidelines are being followed in managing gout. Retrospective chart review was conducted from May to November 2014 in three general practices in Townsville. Registers for patients were established by searching "gout" and "gouty arthritis". Three hundred and twenty-one patients were included in the study after excluding inactive patients, patients below age of 18 and patients with cancer. Main outcome measures were prevalence of comorbidities in gout patients, gout medications and adequate serum urate control (≤0.36 mmol/l). Multivariate logistic regression was used to study the relationship between serum urate level, comorbid conditions and lifestyle factors. Hypertension was the most common comorbid condition with 60.8 % of patients followed by obesity and dyslipidaemia. In terms of medication, 46.7 % of patients were on allopurinol, 12.8 % on indomethacin and 13.4 % on diuretics. Eighty-six percentage of patients had serum urate level (sUA) recorded in the previous year. Of these, 32.2 % had a serum urate level below or equal to 0.36 mmol/l. Moreover, 17.4 % of patients had lifestyle advice documented in chart. Male gender was the most influential factor in having poor uric acid control (p < 0.01), followed by not being on allopurinol (p < 0.01) and patients older than 50 years (p = 0.02). Management of gout in this study sample was not entirely concordant with guidelines. The study also suggests a need for possible tighter monitoring and allopurinol dosing regime in older, male patients.

Effects of developmental training of basketball cadets realised in the competitive period.

PubMed

Trninić, S; Marković, G; Heimer, S

2001-12-01

The analysis of effects of a two-month developmental training cycle realised within a basketball season revealed statistically significant positive changes at the multivariate level in components of motor-functional conditioning (fitness) status of the sample of talented basketball cadets (15-16 years). The greatest correlations with discriminant function were found in variables with statistically significant changes at the univariate level, more explicitly in variables of explosive and repetitive power of the upper body and trunk, anaerobic lactic endurance, as well as in jumping type explosive leg power. The presented developmental conditioning training programme, although implemented within the competitive period, induced multiple positive fitness effects between the two control time points in this sample of basketball players. The authors suggest that, to assess power of shoulders and upper back, the test overgrip pull-up should not be applied to basketball players of this age due to its poor sensitivity. Instead, they propose the undergrip pull-up test, which is a facilitated version of the same test. The results presented in this article reinforce experienced opinion of experts that, in the training process with youth teams, the developmental conditioning training programme is effectively applicable throughout the entire competitive season. The proposed training model is a system of various training procedures, operating synergistically, aimed at enhancing integral fitness (preparedness) of basketball players. Further investigations should be focused on assessing effects of both the proposed and other developmental training cycle programmes, by means of assessing and monitoring actual quality (overall performance) of players, on the one hand, and, on the other, by following-up hormonal and biochemical changes over multiple time points.

The Active Metabolite of Warfarin (3'-Hydroxywarfarin) and Correlation with INR, Warfarin and Drug Weekly Dosage in Patients under Oral Anticoagulant Therapy: A Pharmacogenetics Study

PubMed Central

Talarico, Anna; Fabbri, Matteo; Bertocco, Cesare; Vigliano, Marco; Moratelli, Stefano; Cuneo, Antonio; Serino, Maria Luisa; Avato, Francesco Maria

2016-01-01

Objectives Warfarin oral anticoagulant therapy (OAT) requires regular and frequent drug adjustment monitored by INR. Interindividual variability, drug and diet interferences, and genetics (VKORC1 and CYP2C9) make the maintenance/reaching of stable INR a not so easy task. HPLC assessment of warfarin/enantiomers was suggested as a valid monitoring-tool along with INR, but definite results are still lacking. We evaluated possible correlations between INR, warfarin/3’-hydroxywarfarin, and drug weekly dosage aimed at searching novel alternatives to OAT monitoring. VKORC1/CYP2C9 pharmacogenetics investigation was performed to account for the known influence on warfarin homeostasis. Methods 133 OAT patients were recruited and assessed for warfarin/3’-hydroxywarfarin serum levels (HPLC), INR, and VKORC1 and CYP2C9 genotypes. A subgroup of 52 patients were monitored in detail (5 consecutive controls; c0-c4) till the target INR was reached. Correlation analyses were performed in both groups Results In the whole OAT group both warfarin and 3’-hydroxywarfarin correlate with INR at comparable degree (r2 = 0.0388 and 0.0362 respectively). Conversely, warfarin weekly dosage better correlates with warfarin than with 3’-hydroxywarfarin (r2 = 0.0975 and r2 = 0.0381 respectively), but considering together warfarin plus 3’-hydroxywarfarin the correlation strongly increased (r2 = 0.1114; p<0.0001). Interestingly, 3’-hydroxywarfarin reached a strong correlation at c4 respect to warfarin (r2 = 0.2157 and r2 = 0.0549; p = 0.0005 and p = 0.0944 respectively) seeming less affected by drug adjustments in the subgroup of 52 patients who started OAT. The multivariate analyses aimed at estimating the true contribution of 3’-hydroxywarfarin on INR value ascribed it the unique significant value (p = 0.0021) in spite of warfarin who lost association. The pharmacogenetics studies confirmed that patients carrying the VKORC1 variant-allele required lower warfarin maintenance dosage and that the combination of VKORC1 and CYP2C9 yielded a warfarin responsive index (WRI) inversely related to the number variant alleles Conclusion Our results overall suggest that 3’-hydroxywarfarin monitoring could be of great advantage in INR monitoring respect to classical warfarin assessment showing significant contribution also in multivariate analysis. Therefore, additional active metabolites should be recognized and investigated as novel useful indicators. PMID:27606428

A remote condition monitoring system for wind-turbine based DG systems

NASA Astrophysics Data System (ADS)

Ma, X.; Wang, G.; Cross, P.; Zhang, X.

2012-05-01

In this paper, a remote condition monitoring system is proposed, which fundamentally consists of real-time monitoring modules on the plant side, a remote support centre and the communications between them. The paper addresses some of the key issues related on the monitoring system, including i) the implementation and configuration of a VPN connection, ii) an effective database system to be able to handle huge amount of monitoring data, and iii) efficient data mining techniques to convert raw data into useful information for plant assessment. The preliminary results have demonstrated that the proposed system is practically feasible and can be deployed to monitor the emerging new energy generation systems.

Progress toward an advanced condition monitoring system for reusable rocket engines

NASA Technical Reports Server (NTRS)

Maram, J.; Barkhoudarian, S.

1987-01-01

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

Carter Carburetor Weekly Air Monitoring & Sampling Report - March 7, 2013 - March 13, 2016

EPA Pesticide Factsheets

Carter Carburetor Daily Weather Conditions, Dairly Work Activities, Daily Air Monitoring and Samplying Results, Air Monitoring/Samplying Results –Station 2 Linc 126, Air Monitoring/Sampling Results- Sation 3 Linc 123, Air Monitoring/Samplying Results-Stati

Wind Turbine Gearbox Oil Filtration and Condition Monitoring

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

Sheng, Shuangwen

This is an invited presentation for a pre-conference workshop, titled advances and opportunities in lubrication: wind turbine, at the 2015 Society of Tribologists and Lubrication Engineers (STLE) Tribology Frontiers Conference held in Denver, CO. It gives a brief overview of wind turbine gearbox oil filtration and condition monitoring by highlighting typical industry practices and challenges. The presentation starts with an introduction by covering recent growth of global wind industry, reliability challenges, benefits of oil filtration and condition monitoring, and financial incentives to conduct wind operation and maintenance research, which includes gearbox oil filtration and condition monitoring work presented herein. Then,more » the presentation moves on to oil filtration by stressing the benefits of filtration, discussing typical main- and offline-loop practices, highlighting important factors considered when specifying a filtration system, and illustrating real-world application challenges through a cold-start example. In the next section on oil condition monitoring, a discussion on oil sample analysis, oil debris monitoring, oil cleanliness measurements and filter analysis is given based on testing results mostly obtained by and at NREL, and by pointing out a few challenges with oil sample analysis. The presentation concludes with a brief touch on future research and development (R and D) opportunities. It is hoping that the information presented can inform the STLE community to start or redirect their R and D work to help the wind industry advance.« less

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

PubMed Central

2014-01-01

Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels. PMID:24555534

Cellphone probes as an ATMS tool

DOT National Transportation Integrated Search

2003-06-01

The foundation of traffic operations and management is the ability to monitor traffic conditions. One approach to traffic monitoring is to sample conditions by tracking a limited number of probe vehicles as they traverse a network. An emerging techno...

Reliable long-term continuous blood glucose monitoring for patients in critical care using microdialysis and infrared spectrometry

NASA Astrophysics Data System (ADS)

Heise, H. Michael; Damm, Uwe; Kondepati, Venkata R.

2006-02-01

For clinical research, in-vivo blood glucose monitoring is an ongoing important topic to improve glycemic control in patients with non-adequate blood glucose regulation. Critically ill patients received much interest, since the intensive insulin therapy treatment, as established for diabetics, reduces mortality significantly. Despite the existence of commercially available, mainly amperometric biosensors, continued interest is in infrared spectroscopic techniques for reagent-free glucose monitoring. For stable long-term operation, avoiding also sensor recalibration, a bed-side device coupled to a micro-dialysis probe was developed for quasi-continuous glucose monitoring. Multivariate calibration is required for glucose concentration prediction due to the complex composition of dialysates from interstitial body fluid. Measurements were carried out with different test persons, each experiment lasting for more than 8 hours. Owing to low dialysis recovery rates, glucose concentrations in the dialysates were between 0.83 and 4.44 mM. Standard errors of prediction (SEP) obtained with Partial Least Squares (PLS) calibration and different cross-validation strategies were mainly between 0.13 and 0.18 mM based on either full interval data or specially selected spectral variables.

Evaluating public education messages aimed at monitoring and responding to social interactive technology on smartphones among young drivers.

PubMed

Gauld, Cassandra S; Lewis, Ioni; White, Katherine M; Fleiter, Judy J; Watson, Barry

2017-07-01

Young drivers are more likely than any other age group to access social interactive technology (e.g., Facebook, E-mail) on a smartphone while driving. The current study formed part of a larger investigation and was guided by The Step Approach to Message Design and Testing (SatMDT) to evaluate the relative effectiveness of three different public education messages aimed at reducing smartphone use among young drivers. The messages were each adapted to the specific behaviours of monitoring/reading and responding to social interactive technology on smartphones. Participants (n=288; 199F, 89M) were drivers aged 17-25 years who resided in the Australian state of Queensland. Message acceptance (i.e., intention and effectiveness) and message rejection were both assessed using a self-report survey. Multivariate analyses found that, overall, the messages targeting monitoring/reading behaviour were considered more effective than those targeting responding behaviour. The message that challenged the underlying motivation that believing you are a good driver makes it easier to monitor/read social interactive technology while driving was considered particularly effective by young male drivers. Copyright © 2017 Elsevier Ltd. All rights reserved.