The job content questionnaire in various occupational contexts: applying a latent class model

PubMed Central

Santos, Kionna Oliveira Bernardes; de Araújo, Tânia Maria; Karasek, Robert

2017-01-01

Objective To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. Methods We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into account the latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Results Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. Conclusion The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers—groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. PMID:28515185

Credibility analysis of risk classes by generalized linear model

NASA Astrophysics Data System (ADS)

Erdemir, Ovgucan Karadag; Sucu, Meral

2016-06-01

In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

Reachability analysis of real-time systems using time Petri nets.

PubMed

Wang, J; Deng, Y; Xu, G

2000-01-01

Time Petri nets (TPNs) are a popular Petri net model for specification and verification of real-time systems. A fundamental and most widely applied method for analyzing Petri nets is reachability analysis. The existing technique for reachability analysis of TPNs, however, is not suitable for timing property verification because one cannot derive end-to-end delay in task execution, an important issue for time-critical systems, from the reachability tree constructed using the technique. In this paper, we present a new reachability based analysis technique for TPNs for timing property analysis and verification that effectively addresses the problem. Our technique is based on a concept called clock-stamped state class (CS-class). With the reachability tree generated based on CS-classes, we can directly compute the end-to-end time delay in task execution. Moreover, a CS-class can be uniquely mapped to a traditional state class based on which the conventional reachability tree is constructed. Therefore, our CS-class-based analysis technique is more general than the existing technique. We show how to apply this technique to timing property verification of the TPN model of a command and control (C2) system.

Markov Chain Ontology Analysis (MCOA)

PubMed Central

2012-01-01

Background Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. Results In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. Conclusion A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches. PMID:22300537

Markov Chain Ontology Analysis (MCOA).

PubMed

Frost, H Robert; McCray, Alexa T

2012-02-03

Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.

Latent Class Analysis of Peer Conformity: Who Is Yielding to Pressure and Why?

ERIC Educational Resources Information Center

Kosten, Paul A.; Scheier, Lawrence M.; Grenard, Jerry L.

2013-01-01

This study used latent class analysis to examine typologies of peer conformity in a community sample of middle school students. Students responded to 31 items assessing diverse facets of conformity dispositions. The most parsimonious model produced three qualitatively distinct classes that differed on the basis of conformity to recreational…

Detecting Math Anxiety with a Mixture Partial Credit Model

ERIC Educational Resources Information Center

Ölmez, Ibrahim Burak; Cohen, Allan S.

2017-01-01

The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…

Comparison of estimators for rolling samples using Forest Inventory and Analysis data

Treesearch

Devin S. Johnson; Michael S. Williams; Raymond L. Czaplewski

2003-01-01

The performance of three classes of weighted average estimators is studied for an annual inventory design similar to the Forest Inventory and Analysis program of the United States. The first class is based on an ARIMA(0,1,1) time series model. The equal weight, simple moving average is a member of this class. The second class is based on an ARIMA(0,2,2) time series...

Three-Level Models for Indirect Effects in School- and Class-Randomized Experiments in Education

ERIC Educational Resources Information Center

Pituch, Keenan A.; Murphy, Daniel L.; Tate, Richard L.

2009-01-01

Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class,…

MHC class I loci of the Bar-Headed goose (Anser indicus)

PubMed Central

2010-01-01

MHC class I proteins mediate functions in anti-pathogen defense. MHC diversity has already been investigated by many studies in model avian species, but here we chose the bar-headed goose, a worldwide migrant bird, as a non-model avian species. Sequences from exons encoding the peptide-binding region (PBR) of MHC class I molecules were isolated from liver genomic DNA, to investigate variation in these genes. These are the first MHC class I partial sequences of the bar-headed goose to be reported. A preliminary analysis suggests the presence of at least four MHC class I genes, which share great similarity with those of the goose and duck. A phylogenetic analysis of bar-headed goose, goose and duck MHC class I sequences using the NJ method supports the idea that they all cluster within the anseriforms clade. PMID:21637434

Using the Gravity Model to Delineate a Trade Area: A Class Project.

ERIC Educational Resources Information Center

Dzik, Anthony J.

1992-01-01

Reports that students who might be bored or intimidated by economic geographic theory become enthusiastic when they can apply it to their own experiences. Describes a class project involving fieldwork and in-class analysis on delineating the retail trade area of a small Ohio city. Includes three maps and mathematical formulae for data analysis.…

Using Latent Class Analysis to Identify Academic and Behavioral Risk Status in Elementary Students

ERIC Educational Resources Information Center

King, Kathleen R.; Lembke, Erica S.; Reinke, Wendy M.

2016-01-01

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in…

Multimethod latent class analysis

PubMed Central

Nussbeck, Fridtjof W.; Eid, Michael

2015-01-01

Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model. PMID:26441714

Biomechanical 3-Dimensional Finite Element Analysis of Obturator Protheses Retained with Zygomatic and Dental Implants in Maxillary Defects

PubMed Central

Akay, Canan; Yaluğ, Suat

2015-01-01

Background The objective of this study was to investigate the stress distribution in the bone around zygomatic and dental implants for 3 different implant-retained obturator prostheses designs in a Aramany class IV maxillary defect using 3-dimensional finite element analysis (FEA). Material\\Methods A 3-dimensional finite element model of an Aramany class IV defect was created. Three different implant-retained obturator prostheses were modeled: model 1 with 1 zygomatic implant and 1 dental implant, model 2 with 1 zygomatic implant and 2 dental implants, and model 3 with 2 zygomatic implants. Locator attachments were used as a superstructure. A 150-N load was applied 3 different ways. Qualitative analysis was based on the scale of maximum principal stress; values obtained through quantitative analysis are expressed in MPa. Results In all loading conditions, model 3 (when compared models 1 and 2) showed the lowest maximum principal stress value. Model 3 is the most appropirate reconstruction in Aramany class IV maxillary defects. Two zygomatic implants can reduce the stresses in model 3. The distribution of stresses on prostheses were more rational with the help of zygoma implants, which can distribute the stresses on each part of the maxilla. Conclusions Aramany class IV obturator prosthesis placement of 2 zygomatic implants in each side of the maxilla is more advantageous than placement of dental implants. In the non-defective side, increasing the number of dental implants is not as suitable as zygomatic implants. PMID:25714086

The job content questionnaire in various occupational contexts: applying a latent class model.

PubMed

Santos, Kionna Oliveira Bernardes; Araújo, Tânia Maria de; Carvalho, Fernando Martins; Karasek, Robert

2017-05-17

To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into accountthe latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers-groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Applying Latent Class Analysis to Risk Stratification for Perioperative Mortality in Patients Undergoing Intraabdominal General Surgery.

PubMed

Kim, Minjae; Wall, Melanie M; Li, Guohua

2016-07-01

Perioperative risk stratification is often performed using individual risk factors without consideration of the syndemic of these risk factors. We used latent class analysis (LCA) to identify the classes of comorbidities and risk factors associated with perioperative mortality in patients presenting for intraabdominal general surgery. The 2005 to 2010 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a cohort of patients undergoing intraabdominal general surgery. Risk factors and comorbidities were entered into LCA models to identify the latent classes, and individuals were assigned to a class based on the highest posterior probability of class membership. Relative risk regression was used to determine the associations between the latent classes and 30-day mortality, with adjustments for procedure. A 9-class model was fit using LCA on 466,177 observations. After combining classes with similar adjusted mortality risks, 5 risk classes were obtained. Compared with the class with average mortality risk (class 4), the risk ratios (95% confidence interval) ranged from 0.020 (0.014-0.027) in the lowest risk class (class 1) to 6.75 (6.46-7.02) in the highest risk class. After adjusting for procedure and ASA physical status, the latent classes remained significantly associated with 30-day mortality. The addition of the risk class variable to a model containing ASA physical status and surgical procedure demonstrated a significant increase in the area under the receiver operator characteristic curve (0.892 vs 0.915; P < 0.0001). Latent classes of risk factors and comorbidities in patients undergoing intraabdominal surgery are predictive of 30-day mortality independent of the ASA physical status and improve risk prediction with the ASA physical status.

Preferential attachment and growth dynamics in complex systems

NASA Astrophysics Data System (ADS)

Yamasaki, Kazuko; Matia, Kaushik; Buldyrev, Sergey V.; Fu, Dongfeng; Pammolli, Fabio; Riccaboni, Massimo; Stanley, H. Eugene

2006-09-01

Complex systems can be characterized by classes of equivalency of their elements defined according to system specific rules. We propose a generalized preferential attachment model to describe the class size distribution. The model postulates preferential growth of the existing classes and the steady influx of new classes. According to the model, the distribution changes from a pure exponential form for zero influx of new classes to a power law with an exponential cut-off form when the influx of new classes is substantial. Predictions of the model are tested through the analysis of a unique industrial database, which covers both elementary units (products) and classes (markets, firms) in a given industry (pharmaceuticals), covering the entire size distribution. The model’s predictions are in good agreement with the data. The paper sheds light on the emergence of the exponent τ≈2 observed as a universal feature of many biological, social and economic problems.

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

JAVA CLASSES FOR NONPROCEDURAL VARIOGRAM MONITORING

EPA Science Inventory

A set of Java classes was written for variogram modeling to support research for US EPA's Regional Vulnerability Assessment Program (ReVA). The modeling objectives of this research program are to use conceptual programming tools for numerical analysis for regional risk assessm...

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-04-15

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of state and transition model assumptions used in National Forest Plan revision

Treesearch

Eric B. Henderson

2008-01-01

State and transition models are being utilized in forest management analysis processes to evaluate assumptions about disturbances and succession. These models assume valid information about seral class successional pathways and timing. The Forest Vegetation Simulator (FVS) was used to evaluate seral class succession assumptions for the Hiawatha National Forest in...

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

PubMed Central

Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

2018-01-01

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues. PMID:29713300

Use of Latent Class Analysis to define groups based on validity, cognition, and emotional functioning.

PubMed

Morin, Ruth T; Axelrod, Bradley N

Latent Class Analysis (LCA) was used to classify a heterogeneous sample of neuropsychology data. In particular, we used measures of performance validity, symptom validity, cognition, and emotional functioning to assess and describe latent groups of functioning in these areas. A data-set of 680 neuropsychological evaluation protocols was analyzed using a LCA. Data were collected from evaluations performed for clinical purposes at an urban medical center. A four-class model emerged as the best fitting model of latent classes. The resulting classes were distinct based on measures of performance validity and symptom validity. Class A performed poorly on both performance and symptom validity measures. Class B had intact performance validity and heightened symptom reporting. The remaining two Classes performed adequately on both performance and symptom validity measures, differing only in cognitive and emotional functioning. In general, performance invalidity was associated with worse cognitive performance, while symptom invalidity was associated with elevated emotional distress. LCA appears useful in identifying groups within a heterogeneous sample with distinct performance patterns. Further, the orthogonal nature of performance and symptom validities is supported.

Trades Between Opposition and Conjunction Class Trajectories for Early Human Missions to Mars

NASA Technical Reports Server (NTRS)

Mattfeld, Bryan; Stromgren, Chel; Shyface, Hilary; Komar, David R.; Cirillo, William; Goodliff, Kandyce

2014-01-01

Candidate human missions to Mars, including NASA's Design Reference Architecture 5.0, have focused on conjunction-class missions with long crewed durations and minimum energy trajectories to reduce total propellant requirements and total launch mass. However, in order to progressively reduce risk and gain experience in interplanetary mission operations, it may be desirable that initial human missions to Mars, whether to the surface or to Mars orbit, have shorter total crewed durations and minimal stay times at the destination. Opposition-class missions require larger total energy requirements relative to conjunction-class missions but offer the potential for much shorter mission durations, potentially reducing risk and overall systems performance requirements. This paper will present a detailed comparison of conjunction-class and opposition-class human missions to Mars vicinity with a focus on how such missions could be integrated into the initial phases of a Mars exploration campaign. The paper will present the results of a trade study that integrates trajectory/propellant analysis, element design, logistics and sparing analysis, and risk assessment to produce a comprehensive comparison of opposition and conjunction exploration mission constructs. Included in the trade study is an assessment of the risk to the crew and the trade offs between the mission duration and element, logistics, and spares mass. The analysis of the mission trade space was conducted using four simulation and analysis tools developed by NASA. Trajectory analyses for Mars destination missions were conducted using VISITOR (Versatile ImpulSive Interplanetary Trajectory OptimizeR), an in-house tool developed by NASA Langley Research Center. Architecture elements were evaluated using EXploration Architecture Model for IN-space and Earth-to-orbit (EXAMINE), a parametric modeling tool that generates exploration architectures through an integrated systems model. Logistics analysis was conducted using NASA's Human Exploration Logistics Model (HELM), and sparing allocation predictions were generated via the Exploration Maintainability Analysis Tool (EMAT), which is a probabilistic simulation engine that evaluates trades in spacecraft reliability and sparing requirements based on spacecraft system maintainability and reparability.

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

PubMed Central

2013-01-01

Background Falls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique which could better estimate fall probability is both timely and needed. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly. Methods Using a retrospective data-set a two-step LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients’ prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons. Results A review of the model fit indices with covariates shows that a six-class solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good at 90%. Specific prescription medications were found to strongly influence group membership. Conclusions In conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data. PMID:23705639

Panic disorder and its subtypes: a comprehensive analysis of panic symptom heterogeneity using epidemiological and treatment seeking samples

PubMed Central

Roberson-Nay, R.; Kendler, K. S.

2014-01-01

Background Panic disorder (PD) is a heterogeneous syndrome that can present with a variety of symptom profiles that potentially reflect distinct etiologic pathways. The present study represents the most comprehensive examination of phenotypic variance in PD with and without agoraphobia for the purpose of identifying clinically relevant and etiologically meaningful subtypes. Method Latent class (LC) and factor mixture analysis were used to examine panic symptom data ascertained from three national epidemiologic surveys [Epidemiological Catchment Area (ECA), National Comorbidity Study (NCS), National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), Wave 1], a twin study [Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)] and a clinical trial (Cross-National Collaborative Panic Study [CNCPS]). Results Factor mixture models (versus LC) generally provided better fit to panic symptom data and suggested two panic classes for the ECA, VATSPSUD and CNCPS, with one class typified by prominent respiratory symptoms. The NCS yielded two classes, but suggested both qualitative and quantitative differences. The more contemporary NESARC sample supported a two and three class model, with the three class model suggesting two variants of respiratory panic. The NESARC’s three class model continued to provide the best fit when the model was restricted to a more severe form of PD/panic disorder with agoraphobia. Conclusions Results from epidemiologic and clinical samples suggest two panic subtypes, with one subtype characterized by a respiratory component and a second class typified by general somatic symptoms. Results are discussed in light of their relevance to the etiopathogenesis of PD. PMID:21557895

Function Model for Community Health Service Information

NASA Astrophysics Data System (ADS)

Yang, Peng; Pan, Feng; Liu, Danhong; Xu, Yongyong

In order to construct a function model of community health service (CHS) information for development of CHS information management system, Integration Definition for Function Modeling (IDEF0), an IEEE standard which is extended from Structured Analysis and Design(SADT) and now is a widely used function modeling method, was used to classifying its information from top to bottom. The contents of every level of the model were described and coded. Then function model for CHS information, which includes 4 super-classes, 15 classes and 28 sub-classed of business function, 43 business processes and 168 business activities, was established. This model can facilitate information management system development and workflow refinement.

MPTinR: analysis of multinomial processing tree models in R.

PubMed

Singmann, Henrik; Kellen, David

2013-06-01

We introduce MPTinR, a software package developed for the analysis of multinomial processing tree (MPT) models. MPT models represent a prominent class of cognitive measurement models for categorical data with applications in a wide variety of fields. MPTinR is the first software for the analysis of MPT models in the statistical programming language R, providing a modeling framework that is more flexible than standalone software packages. MPTinR also introduces important features such as (1) the ability to calculate the Fisher information approximation measure of model complexity for MPT models, (2) the ability to fit models for categorical data outside the MPT model class, such as signal detection models, (3) a function for model selection across a set of nested and nonnested candidate models (using several model selection indices), and (4) multicore fitting. MPTinR is available from the Comprehensive R Archive Network at http://cran.r-project.org/web/packages/MPTinR/ .

Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

ERIC Educational Resources Information Center

Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

2011-01-01

The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

Social space and cultural class divisions: the forms of capital and contemporary lifestyle differentiation.

PubMed

Flemmen, Magne; Jarness, Vegard; Rosenlund, Lennart

2018-03-01

In this article, we address whether and how contemporary social classes are marked by distinct lifestyles. We assess the model of the social space, a novel approach to class analysis pioneered by Bourdieu's Distinction. Although pivotal in Bourdieu's work, this model is too often overlooked in later research, making its contemporary relevance difficult to assess. We redress this by using the social space as a framework through which to study the cultural manifestation of class divisions in lifestyle differences in contemporary Norwegian society. Through a Multiple Correspondence Analysis (MCA) of unusually rich survey data, we reveal a structure strikingly similar to the model in Distinction, with a primary dimension of the volume of capital, and a secondary dimension of the composition of capital. While avoiding the substantialist fallacy of predefined notions of 'highbrow' and 'lowbrow' tastes, we explore how 168 lifestyle items map onto this social space. This reveals distinct classed lifestyles according to both dimensions of the social space. The lifestyles of the upper classes are distinctly demanding in terms of resources. Among those rich in economic capital, this manifests itself in a lifestyle which involves a quest for excitement, and which is bodily oriented and expensive. For their counterparts rich in cultural capital, a more ascetic and intellectually oriented lifestyle manifests itself, demanding of resources in the sense of requiring symbolic mastery, combining a taste for canonized, legitimate culture with more cosmopolitan and 'popular' items. In contrast to many studies' descriptions of the lower classes as 'disengaged' and 'inactive', we find evidence of distinct tastes on their part. Our analysis thus affirms the validity of Bourdieu's model of social class and the contention that classes tend to take the form of status groups. We challenge dominant positions in cultural stratification research, while questioning the aptness of the metaphor of the 'omnivore', as well as recent analyses of 'emerging cultural capital'. © London School of Economics and Political Science 2017.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

ERIC Educational Resources Information Center

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

2007-01-01

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Class Size and Student Performance at a Public Research University: A Cross-Classified Model

ERIC Educational Resources Information Center

Johnson, Iryna Y.

2010-01-01

This study addresses several methodological problems that have confronted prior research on the effect of class size on student achievement. Unlike previous studies, this analysis accounts for the hierarchical data structure of student achievement, where grades are nested within classes and students, and considers a wide range of class sizes…

Modeling Individual Differences in Unfolding Preference Data: A Restricted Latent Class Approach.

ERIC Educational Resources Information Center

Bockenholt, Ulf; Bockenholt, Ingo

1990-01-01

A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)

Classifying Patients with Chronic Pelvic Pain into Levels of Biopsychosocial Dysfunction Using Latent Class Modeling of Patient Reported Outcome Measures

PubMed Central

Fenton, Bradford W.; Grey, Scott F.; Tossone, Krystel; McCarroll, Michele; Von Gruenigen, Vivian E.

2015-01-01

Chronic pelvic pain affects multiple aspects of a patient's physical, social, and emotional functioning. Latent class analysis (LCA) of Patient Reported Outcome Measures Information System (PROMIS) domains has the potential to improve clinical insight into these patients' pain. Based on the 11 PROMIS domains applied to n=613 patients referred for evaluation in a chronic pelvic pain specialty center, exploratory factor analysis (EFA) was used to identify unidimensional superdomains. Latent profile analysis (LPA) was performed to identify the number of homogeneous classes present and to further define the pain classification system. The EFA combined the 11 PROMIS domains into four unidimensional superdomains of biopsychosocial dysfunction: Pain, Negative Affect, Fatigue, and Social Function. Based on multiple fit criteria, a latent class model revealed four distinct classes of CPP: No dysfunction (3.2%); Low Dysfunction (17.8%); Moderate Dysfunction (53.2%); and High Dysfunction (25.8%). This study is the first description of a novel approach to the complex disease process such as chronic pelvic pain and was validated by demographic, medical, and psychosocial variables. In addition to an essentially normal class, three classes of increasing biopsychosocial dysfunction were identified. The LCA approach has the potential for application to other complex multifactorial disease processes. PMID:26355825

Qualitative analysis of pure and adulterated canola oil via SIMCA

NASA Astrophysics Data System (ADS)

Basri, Katrul Nadia; Khir, Mohd Fared Abdul; Rani, Rozina Abdul; Sharif, Zaiton; Rusop, M.; Zoolfakar, Ahmad Sabirin

2018-05-01

This paper demonstrates the utilization of near infrared (NIR) spectroscopy to classify pure and adulterated sample of canola oil. Soft Independent Modeling Class Analogies (SIMCA) algorithm was implemented to discriminate the samples to its classes. Spectral data obtained was divided using Kennard Stone algorithm into training and validation dataset by a fixed ratio of 7:3. The model accuracy obtained based on the model built is 0.99 whereas the sensitivity and precision are 0.92 and 1.00. The result showed the classification model is robust to perform qualitative analysis of canola oil for future application.

Conserved directed percolation: exact quasistationary distribution of small systems and Monte Carlo simulations

NASA Astrophysics Data System (ADS)

César Mansur Filho, Júlio; Dickman, Ronald

2011-05-01

We study symmetric sleepy random walkers, a model exhibiting an absorbing-state phase transition in the conserved directed percolation (CDP) universality class. Unlike most examples of this class studied previously, this model possesses a continuously variable control parameter, facilitating analysis of critical properties. We study the model using two complementary approaches: analysis of the numerically exact quasistationary (QS) probability distribution on rings of up to 22 sites, and Monte Carlo simulation of systems of up to 32 000 sites. The resulting estimates for critical exponents β, \\beta /\

Role of delay and screening in controlling AIDS

NASA Astrophysics Data System (ADS)

Chauhan, Sudipa; Bhatia, Sumit Kaur; Gupta, Surbhi

2016-06-01

We propose a non-linear HIV/ AIDS model to analyse the spread and control of HIV/AIDS. The population is divided into three classes, susceptible, infective and AIDS patients. The model is developed under the assumptions of vertical transmission and time delay in infective class. Time delay is also included to show sexual maturity period of infected newborns. We study dynamics of the model and obtain the reproduction number. Now to control the epidemic, we study the model where aware infective class is also added, i.e., people are made aware of their medical status by way of screening. To make the model more realistic, we consider the situation where aware infective class also interacts with other people. The model is analysed qualitatively by stability theory of ODE. Stability analysis of both disease-free and endemic equilibrium is studied based on reproduction number. Also, it is proved that if (R0)1, R1 ≤ 1 then, disease free equilibrium point is locally asymptotically stable and if (R0)1, R1 > 1 then, disease free equilibrium is unstable. Also, the stability analysis of endemic equilibrium point has been done and it is shown that for (R0)1 > 1 endemic equilibrium point is stable. Global stability analysis of endemic equilibrium point has also been done. At last, it is shown numerically that the delay in sexual maturity of infected individuals result in less number of AIDS patients.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

PubMed

Xu, Lu; Shi, Peng-Tao; Ye, Zi-Hong; Yan, Si-Min; Yu, Xiao-Ping

2013-12-01

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market. Copyright © 2013 Elsevier Ltd. All rights reserved.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Clinical classes of injured workers with chronic low back pain: a latent class analysis with relationship to working status.

PubMed

Carlesso, Lisa C; Raja Rampersaud, Y; Davis, Aileen M

2018-01-01

To determine (a) clinical classes of injured workers with chronic low back pain (CLBP), (b) predictors of class membership and (c) associations of classes with baseline work status. Patients with CLBP from a tertiary care outpatient clinic in Toronto, Canada were sampled. Latent class analysis was applied to determine class structure using physical, psychological and coping indicators. Classes were interpreted by class-specific means and analyzed for predictors of membership. Lastly, association of the classes with being off work was modeled. A 3-class model was chosen based on fit criteria, theoretical and clinical knowledge of this population. The resultant 3 classes represented low, moderate and high levels of clinical severity. Predictors of being in the high severity group compared to the low severity group were < high school education [odds ratio (OR) 3.06, 95% CI (1.47, 6.37)] and comorbidity total [OR 1.28, 95% CI (1.03, 1.59)]. High severity class membership was associated with four times increased risk of being off work at baseline compared to those in the low severity group [OR 3.98, 95% CI (1.61, 6.34)]. In a cohort of injured workers with CLBP, 3 clinical classes were identified with distinct psychological and physical profiles. These profiles are useful in aiding clinicians to identify patients of high clinical severity who may be potentially at risk for problematic return to work.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

PubMed

Huang, Yangxin; Lu, Xiaosun; Chen, Jiaqing; Liang, Juan; Zangmeister, Miriam

2017-10-27

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Designing Class Methods from Dataflow Diagrams

NASA Astrophysics Data System (ADS)

Shoval, Peretz; Kabeli-Shani, Judith

A method for designing the class methods of an information system is described. The method is part of FOOM - Functional and Object-Oriented Methodology. In the analysis phase of FOOM, two models defining the users' requirements are created: a conceptual data model - an initial class diagram; and a functional model - hierarchical OO-DFDs (object-oriented dataflow diagrams). Based on these models, a well-defined process of methods design is applied. First, the OO-DFDs are converted into transactions, i.e., system processes that supports user task. The components and the process logic of each transaction are described in detail, using pseudocode. Then, each transaction is decomposed, according to well-defined rules, into class methods of various types: basic methods, application-specific methods and main transaction (control) methods. Each method is attached to a proper class; messages between methods express the process logic of each transaction. The methods are defined using pseudocode or message charts.

Using the Mixed Rasch Model to analyze data from the beliefs and attitudes about memory survey.

PubMed

Smith, Everett V; Ying, Yuping; Brown, Scott W

2012-01-01

In this study, we used the Mixed Rasch Model (MRM) to analyze data from the Beliefs and Attitudes About Memory Survey (BAMS; Brown, Garry, Silver, and Loftus, 1997). We used the original 5-point BAMS data to investigate the functioning of the "Neutral" category via threshold analysis under a 2-class MRM solution. The "Neutral" category was identified as not eliciting the model expected responses and observations in the "Neutral" category were subsequently treated as missing data. For the BAMS data without the "Neutral" category, exploratory MRM analyses specifying up to 5 latent classes were conducted to evaluate data-model fit using the consistent Akaike information criterion (CAIC). For each of three BAMS subscales, a two latent class solution was identified as fitting the mixed Rasch rating scale model the best. Results regarding threshold analysis, person parameters, and item fit based on the final models are presented and discussed as well as the implications of this study.

Multilevel Latent Class Analysis: Parametric and Nonparametric Models

ERIC Educational Resources Information Center

Finch, W. Holmes; French, Brian F.

2014-01-01

Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Snoopy--a unifying Petri net framework to investigate biomolecular networks.

PubMed

Rohr, Christian; Marwan, Wolfgang; Heiner, Monika

2010-04-01

To investigate biomolecular networks, Snoopy provides a unifying Petri net framework comprising a family of related Petri net classes. Models can be hierarchically structured, allowing for the mastering of larger networks. To move easily between the qualitative, stochastic and continuous modelling paradigms, models can be converted into each other. We get models sharing structure, but specialized by their kinetic information. The analysis and iterative reverse engineering of biomolecular networks is supported by the simultaneous use of several Petri net classes, while the graphical user interface adapts dynamically to the active one. Built-in animation and simulation are complemented by exports to various analysis tools. Snoopy facilitates the addition of new Petri net classes thanks to its generic design. Our tool with Petri net samples is available free of charge for non-commercial use at http://www-dssz.informatik.tu-cottbus.de/snoopy.html; supported operating systems: Mac OS X, Windows and Linux (selected distributions).

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis.

PubMed

Terhune, Devin Blair

2015-05-01

High hypnotic suggestibility is a heterogeneous condition and there is accumulating evidence that highly suggestible individuals may be comprised of discrete subtypes with dissimilar cognitive and phenomenological profiles. This study applied latent profile analysis to response patterns on a diverse battery of difficult hypnotic suggestions in a sample of individuals in the upper range of hypnotic suggestibility. Comparisons among models indicated that a four-class model was optimal. One class was comprised of very highly suggestible (virtuoso) participants, two classes included highly suggestible participants who were alternately more responsive to inhibitory cognitive suggestions or posthypnotic amnesia suggestions, and the fourth class consisted primarily of medium suggestible participants. These results indicate that there are discrete response profiles in high hypnotic suggestibility. They further provide a number of insights regarding the optimization of hypnotic suggestibility measurement and have implications for the instrumental use of hypnosis for the modeling of different psychological conditions. Copyright © 2015 Elsevier Inc. All rights reserved.

Race/Ethnicity and Social Capital among Middle- and Upper-Middle-Class Elementary School Families: A Structural Equation Model

ERIC Educational Resources Information Center

Caldas, Stephen J.; Cornigans, Linda

2015-01-01

This study used structural equation modeling to conduct a first and second order confirmatory factor analysis (CFA) of a scale developed by McDonald and Moberg (2002) to measure three dimensions of social capital among a diverse group of middle- and upper-middle-class elementary school parents in suburban New York. A structural path model was…

Analysis and synthesis of distributed-lumped-active networks by digital computer

NASA Technical Reports Server (NTRS)

1973-01-01

The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.

Shape analysis modeling for character recognition

NASA Astrophysics Data System (ADS)

Khan, Nadeem A. M.; Hegt, Hans A.

1998-10-01

Optimal shape modeling of character-classes is crucial for achieving high performance on recognition of mixed-font, hand-written or (and) poor quality text. A novel scheme is presented in this regard focusing on constructing such structural models that can be hierarchically examined. These models utilize a certain `well-thought' set of shape primitives. They are simplified enough to ignore the inter- class variations in font-type or writing style yet retaining enough details for discrimination between the samples of the similar classes. Thus the number of models per class required can be kept minimal without sacrificing the recognition accuracy. In this connection a flexible multi- stage matching scheme exploiting the proposed modeling is also described. This leads to a system which is robust against various distortions and degradation including those related to cases of touching and broken characters. Finally, we present some examples and test results as a proof-of- concept demonstrating the validity and the robustness of the approach.

Latent Transition Analysis of Pre-Service Teachers' Efficacy in Mathematics and Science

ERIC Educational Resources Information Center

Ward, Elizabeth Kennedy

2009-01-01

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the…

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

PubMed

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

Modeling abundance using multinomial N-mixture models

USGS Publications Warehouse

Royle, Andy

2016-01-01

Multinomial N-mixture models are a generalization of the binomial N-mixture models described in Chapter 6 to allow for more complex and informative sampling protocols beyond simple counts. Many commonly used protocols such as multiple observer sampling, removal sampling, and capture-recapture produce a multivariate count frequency that has a multinomial distribution and for which multinomial N-mixture models can be developed. Such protocols typically result in more precise estimates than binomial mixture models because they provide direct information about parameters of the observation process. We demonstrate the analysis of these models in BUGS using several distinct formulations that afford great flexibility in the types of models that can be developed, and we demonstrate likelihood analysis using the unmarked package. Spatially stratified capture-recapture models are one class of models that fall into the multinomial N-mixture framework, and we discuss analysis of stratified versions of classical models such as model Mb, Mh and other classes of models that are only possible to describe within the multinomial N-mixture framework.

Vertical Integration: Results from a Cross-Course Student Collaboration

ERIC Educational Resources Information Center

Sloan, Thomas; Lewis, David

2011-01-01

The authors report the results of a cross-class project involving sophomore-level students in an Operations Analysis (OA) class with junior-level students in an Operations Management (OM) class. The students formed virtual teams and developed a simulation model of a call center. The OM students provided the management expertise, while the OA…

A Class of Factor Analysis Estimation Procedures with Common Asymptotic Sampling Properties

ERIC Educational Resources Information Center

Swain, A. J.

1975-01-01

Considers a class of estimation procedures for the factor model. The procedures are shown to yield estimates possessing the same asymptotic sampling properties as those from estimation by maximum likelihood or generalized last squares, both special members of the class. General expressions for the derivatives needed for Newton-Raphson…

Finite element analysis of maxillary bone stress caused by Aramany Class IV obturator prostheses.

PubMed

Miyashita, Elcio Ricardo; Mattos, Beatriz Silva Câmara; Noritomi, Pedro Yoshito; Navarro, Hamilton

2012-05-01

The retention of an Aramany Class IV removable partial dental prosthesis can be compromised by a lack of support. The biomechanics of this obturator prosthesis result in an unusual stress distribution on the residual maxillary bone. This study evaluated the biomechanics of an Aramany Class IV obturator prosthesis with finite element analysis and a digital 3-dimensional (3-D) model developed from a computed tomography scan; bone stress was evaluated according to the load placed on the prosthesis. A 3-D model of an Aramany Class IV maxillary resection and prosthesis was constructed. This model was used to develop a finite element mesh. A 120 N load was applied to the occlusal and incisal platforms corresponding to the prosthetic teeth. Qualitative analysis was based on the scale of maximum principal stress; values obtained through quantitative analysis were expressed in MPa. Under posterior load, tensile and compressive stresses were observed; the tensile stress was greater than the compressive stress, regardless of the bone region, and the greatest compressive stress was observed on the anterior palate near the midline. Under an anterior load, tensile stress was observed in all of the evaluated bone regions; the tensile stress was greater than the compressive stress, regardless of the bone region. The Aramany Class IV obturator prosthesis tended to rotate toward the surgical resection when subjected to posterior or anterior loads. The amount of tensile and compressive stress caused by the Aramany Class IV obturator prosthesis did not exceed the physiological limits of the maxillary bone tissue. (J Prosthet Dent 2012;107:336-342). Copyright © 2012 The Editorial Council of the Journal of Prosthetic Dentistry. Published by Mosby, Inc. All rights reserved.

Influence of Discussion Rating in Cooperative Learning Type Numbered Head Together on Learning Results Students VII MTSN Model Padang

NASA Astrophysics Data System (ADS)

Sasmita, E.; Edriati, S.; Yunita, A.

2018-04-01

Related to the math score of the first semester in class at seventh grade of MTSN Model Padang which much the score that low (less than KKM). It because of the students who feel less involved in learning process because the teacher don't do assessment the discussions. The solution of the problem is discussion assessment in Cooperative Learning Model type Numbered Head Together. This study aims to determine whether the discussion assessment in NHT effect on student learning outcomes of class VII MTsN Model Padang. The instrument used in this study is discussion assessment and final tests. The data analysis technique used is the simple linear regression analysis. Hypothesis test results Fcount greater than the value of Ftable then the hypothesis in this study received. So it concluded that the assessment of the discussion in NHT effect on student learning outcomes of class VII MTsN Model Padang.

Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

ERIC Educational Resources Information Center

Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk

2005-01-01

Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…

The Structure of Student Satisfaction with College Services: A Latent Class Model

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

Latent Class Analysis (LCA) was used to identify distinct groups of Community college students based on their self-ratings of satisfaction with student service programs. The programs were counseling, financial aid, health center, student programs and student government. The best fitting model to describe the data was a two Discrete-Factor model…

Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams

ERIC Educational Resources Information Center

Baghaei, Nilufar; Mitrovic, Antonija; Irwin, Warwick

2007-01-01

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on…

A Latent Transition Analysis of Academic Intrinsic Motivation from Childhood through Adolescence

ERIC Educational Resources Information Center

Marcoulides, George A.; Gottfried, Adele Eskeles; Gottfried, Allen W.; Oliver, Pamella H.

2008-01-01

A longitudinal modeling approach was utilized to determine the existence of latent classes with regard to academic intrinsic motivation and the points of stability and transition of individuals between and within classes. A special type of latent Markov Chain model using "Mplus" was fit to data from the Fullerton Longitudinal Study, with…

A general methodology for maximum likelihood inference from band-recovery data

USGS Publications Warehouse

Conroy, M.J.; Williams, B.K.

1984-01-01

A numerical procedure is described for obtaining maximum likelihood estimates and associated maximum likelihood inference from band- recovery data. The method is used to illustrate previously developed one-age-class band-recovery models, and is extended to new models, including the analysis with a covariate for survival rates and variable-time-period recovery models. Extensions to R-age-class band- recovery, mark-recapture models, and twice-yearly marking are discussed. A FORTRAN program provides computations for these models.

Representing Uncertainty on Model Analysis Plots

ERIC Educational Resources Information Center

Smith, Trevor I.

2016-01-01

Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model.…

TTWand NHT in problem solving

NASA Astrophysics Data System (ADS)

Anthycamurty, R. C. C.; Mardiyana; Saputro, D. R. S.

2018-05-01

This research aims to analyze and determine effect of the model on problem solving. Subjects in this research are students of class X SMK in Purworejo. The learning model used in this research was TTW in class experimental 1 and NHT class experiment 2. This research used quasi experiment. Data analysis technique in this research used ANOVA two way. Data collection techniques in this research used tests to measure student problem solving and GEFT to measure students' cognitive style. The results of this research indicate that there are differences in problem solving between experimental classes used TTW and NHT. The impact of this research is that students are able to remind problem solving used learning model and to know cognitive style of the students.

The Sixth Annual Thermal and Fluids Analysis Workshop

NASA Technical Reports Server (NTRS)

1995-01-01

The Sixth Annual Thermal and Fluids Analysis Workshop consisted of classes, vendor demonstrations, and paper sessions. The classes and vendor demonstrations provided participants with the information on widely used tools for thermal and fluids analysis. The paper sessions provided a forum for the exchange of information and ideas among thermal and fluids analysis. Paper topics included advances an uses of established thermal and fluids computer codes (such as SINDA and TRASYS) as well as unique modeling techniques and applications.

A One-System Theory Which is Not Propositional.

PubMed

Witnauer, James E; Urcelay, Gonzalo P; Miller, Ralph R

2009-04-01

We argue that the propositional and link-based approaches to human contingency learning represent different levels of analysis because propositional reasoning requires a basis, which is plausibly provided by a link-based architecture. Moreover, in their attempt to compare two general classes of models (link-based and propositional), Mitchell et al. have referred to only two generic models and ignore the large variety of different models within each class.

A Petri Net Approach Based Elementary Siphons Supervisor for Flexible Manufacturing Systems

NASA Astrophysics Data System (ADS)

Abdul-Hussin, Mowafak Hassan

2015-05-01

This paper presents an approach to constructing a class of an S3PR net for modeling, simulation and control of processes occurring in the flexible manufacturing system (FMS) used based elementary siphons of a Petri net. Siphons are very important to the analysis and control of deadlocks of FMS that is significant objectives of siphons. Petri net models in the efficiency structure analysis, and utilization of the FMSs when different policy can be implemented lead to the deadlock prevention. We are representing an effective deadlock-free policy of a special class of Petri nets called S3PR. Simulation of Petri net structural analysis and reachability graph analysis is used for analysis and control of Petri nets. Petri nets contain been successfully as one of the most powerful tools for modelling of FMS, where Using structural analysis, we show that liveness of such systems can be attributed to the absence of under marked siphons.

Qualitative Analysis of Animation versus Reading for Pre-Class Preparation in a "Flipped" Classroom

ERIC Educational Resources Information Center

Persky, Adam M.

2015-01-01

The "flipped" classroom model, including such approaches as team-based learning (TBL), stresses pre-class preparation. For three years in a pharmacokinetics course within a pharmacy curriculum, students had the choice of using reading material or a fully animated module to prepare for class. Qualitative methods were used to analyze…

Combined Patterns of Risk for Problem and Obesogenic Behaviors in Adolescents: A Latent Class Analysis Approach

ERIC Educational Resources Information Center

Fleary, Sasha A.

2017-01-01

Background: Several studies have used latent class analyses to explore obesogenic behaviors and substance use in adolescents independently. We explored a variety of health risks jointly to identify distinct patterns of risk behaviors among adolescents. Methods: Latent class models were estimated using Youth Risk Behavior Surveillance System…

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

PubMed Central

Ann Lyons, Heidi

2017-01-01

Casual sex is common during the emerging adult life course stage, but little research has taken a person-centered approach to investigate if casual sexual behavior influences STI rates. Using a nationally representative sample and latent class analysis, results showed three distinctive latent classes. Abstainers were the least likely to have an STI, followed by the casual sex experienced, and then the casual sex risk-takers. Once other covariates were included in the model, there was no significant difference between the abstainers and casual sex experienced classes. These results highlight the need for future research to include diverse samples of emerging adults. PMID:29276549

Students concept understanding of fluid static based on the types of teaching

NASA Astrophysics Data System (ADS)

Rahmawati, I. D.; Suparmi; Sunarno, W.

2018-03-01

This research aims to know the concept understanding of student are taught by guided inquiry based learning and conventional based learning. Subjects in this study are high school students as much as 2 classes and each class consists of 32 students, both classes are homogen. The data was collected by conceptual test in the multiple choice form with the students argumentation of the answer. The data analysis used is qualitative descriptive method. The results of the study showed that the average of class that was using guided inquiry based learning is 78.44 while the class with use conventional based learning is 65.16. Based on these data, the guided inquiry model is an effective learning model used to improve students concept understanding.

Testing averaged cosmology with type Ia supernovae and BAO data

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

Santos, B.; Alcaniz, J.S.; Coley, A.A.

An important problem in precision cosmology is the determination of the effects of averaging and backreaction on observational predictions, particularly in view of the wealth of new observational data and improved statistical techniques. In this paper, we discuss the observational viability of a class of averaged cosmologies which consist of a simple parametrized phenomenological two-scale backreaction model with decoupled spatial curvature parameters. We perform a Bayesian model selection analysis and find that this class of averaged phenomenological cosmological models is favored with respect to the standard ΛCDM cosmological scenario when a joint analysis of current SNe Ia and BAO datamore » is performed. In particular, the analysis provides observational evidence for non-trivial spatial curvature.« less

Influence of Problem Based Learning on Critical Thinking Skills and Competence Class VIII SMPN 1 Gunuang Omeh, 2016/2017

NASA Astrophysics Data System (ADS)

Aswan, D. M.; Lufri, L.; Sumarmin, R.

2018-04-01

This research intends to determine the effect of Problem Based Learning models on students' critical thinking skills and competences. This study was a quasi-experimental research. The population of the study was the students of class VIII SMPN 1 Subdistrict Gunuang Omeh. Random sample selection is done by randomizing the class. Sample class that was chosen VIII3 as an experimental class given that treatment study based on problems and class VIII1 as control class that treatment usually given study. Instrument that used to consist of critical thinking test, cognitive tests, observation sheet of affective and psychomotor. Independent t-test and Mann Whitney U test was used for the analysis. Results showed that there was significant difference (sig <0.05) between control and experimental group. The conclusion of this study was Problem Based Learning models affected the students’ critical thinking skills and competences.

Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Extending a prototype knowledge and object based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Modeling and analysis of several classes of self-oscillating inverters. I - State-plane representations. II - Model extension, classification, and duality relationships

NASA Technical Reports Server (NTRS)

Lee, F. C. Y.; Wilson, T. G.

1982-01-01

The present investigation is concerned with an important class of power conditioning networks, taking into account self-oscillating dc-to-square-wave transistor inverters. The considered circuits are widely used both as the principal power converting and processing means in many systems and as low-power analog-to-discrete-time converters for controlling the switching of the output-stage semiconductors in a variety of power conditioning systems. Aspects of piecewise-linear modeling are discussed, taking into consideration component models, and an equivalent-circuit model. Questions of singular point analysis and state plane representation are also investigated, giving attention to limit cycles, starting circuits, the region of attraction, a hard oscillator, and a soft oscillator.

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

ERIC Educational Resources Information Center

Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

2016-01-01

A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

ERIC Educational Resources Information Center

Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

2013-01-01

This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

Comparative structural modeling of six old yellow enzymes (OYEs) from the necrotrophic fungus Ascochyta rabiei: insight into novel OYE classes with differences in cofactor binding, organization of active site residues and stereopreferences.

PubMed

Nizam, Shadab; Gazara, Rajesh Kumar; Verma, Sandhya; Singh, Kunal; Verma, Praveen Kumar

2014-01-01

Old Yellow Enzyme (OYE1) was the first flavin-dependent enzyme identified and characterized in detail by the entire range of physical techniques. Irrespective of this scrutiny, true physiological role of the enzyme remains a mystery. In a recent study, we systematically identified OYE proteins from various fungi and classified them into three classes viz. Class I, II and III. However, there is no information about the structural organization of Class III OYEs, eukaryotic Class II OYEs and Class I OYEs of filamentous fungi. Ascochyta rabiei, a filamentous phytopathogen which causes Ascochyta blight (AB) in chickpea possesses six OYEs (ArOYE1-6) belonging to the three OYE classes. Here we carried out comparative homology modeling of six ArOYEs representing all the three classes to get an in depth idea of structural and functional aspects of fungal OYEs. The predicted 3D structures of A. rabiei OYEs were refined and evaluated using various validation tools for their structural integrity. Analysis of FMN binding environment of Class III OYE revealed novel residues involved in interaction. The ligand para-hydroxybenzaldehyde (PHB) was docked into the active site of the enzymes and interacting residues were analyzed. We observed a unique active site organization of Class III OYE in comparison to Class I and II OYEs. Subsequently, analysis of stereopreference through structural features of ArOYEs was carried out, suggesting differences in R/S selectivity of these proteins. Therefore, our comparative modeling study provides insights into the FMN binding, active site organization and stereopreference of different classes of ArOYEs and indicates towards functional differences of these enzymes. This study provides the basis for future investigations towards the biochemical and functional characterization of these enigmatic enzymes.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 2. Simulation Analysis Using the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Northeastern FIA Tree Taper Study: Current Status and Future Work

Treesearch

James A. Westfall; Charles T. Scott

2005-01-01

The northeastern unit of the Forest Inventory and Analysis program (NE-FIA) is engaged in an ongoing project to develop regionwide tree taper equations. Sampling intensity is based on NE-FIA plot data and is stratified by species, diameter class, and height class. To date, modeling research has been aimed largely at evaluating existing model forms (and hybrids thereof...

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

A Latent Class Analysis of Weight-Related Health Behaviors among 2- and 4-year College Students, and Associated Risk of Obesity

PubMed Central

Mathur, C; Stigler, M; Lust, K; Laska, M

2016-01-01

Little is known about the complex patterning of weight-related health behaviors in 2- and 4-year college students. The objective of this study was to identify and describe unique classes of weight-related health behaviors among college youth. Latent class analysis was used to identify homogenous, mutually exclusive classes of nine health behaviors which represent multiple theoretically/clinically relevant dimensions of obesity risk among 2- versus 4-year college students using cross-sectional statewide surveillance data (n= 17,584). Additionally, differences in class membership on selected sociodemographic characteristics were examined using a model-based approach. Analysis was conducted separately for both college groups, and 5 and 4 classes were identified for 2-and 4-year college students, respectively. Four classes were similar across 2-and 4-year college groups and were characterized as “mostly healthy dietary habits, active”, “moderately high screen time, active”, “moderately healthy dietary habits, inactive”, and “moderately high screen time, inactive”. “Moderately healthy dietary habits, high screen time” was the additional class unique to 2-year college students. These classes differed on a number of sociodemographic characteristics, including the proportion in each class who were classified as obese. Implications for prevention scientists and future intervention programs are considered. PMID:24990599

A latent class analysis of weight-related health behaviors among 2- and 4-year college students and associated risk of obesity.

PubMed

Mathur, Charu; Stigler, Melissa; Lust, Katherine; Laska, Melissa

2014-12-01

Little is known about the complex patterning of weight-related health behaviors in 2- and 4-year college students. The objective of this study was to identify and describe unique classes of weight-related health behaviors among college students. Latent class analysis was used to identify homogenous, mutually exclusive classes of nine health behaviors that represent multiple theoretically/clinically relevant dimensions of obesity risk among 2- versus 4-year college students using cross-sectional statewide surveillance data (N = 17,584). Additionally, differences in class membership on selected sociodemographic characteristics were examined using a model-based approach. Analysis was conducted separately for both college groups, and five and four classes were identified for 2- and 4-year college students, respectively. Four classes were similar across 2- and 4-year college groups and were characterized as "mostly healthy dietary habits, active"; "moderately high screen time, active"; "moderately healthy dietary habits, inactive"; and "moderately high screen time, inactive." "Moderately healthy dietary habits, high screen time" was the additional class unique to 2-year college students. These classes differed on a number of sociodemographic characteristics, including the proportion in each class who were classified as obese. Implications for prevention scientists and future intervention programs are considered. © 2014 Society for Public Health Education.

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics.

PubMed

da Silva Oliveira, Vanessa; Honorato, Ricardo Saldanha; Honorato, Fernanda Araújo; Pereira, Claudete Fernandes

2018-05-01

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models.

PubMed

Blagus, Rok; Lusa, Lara

2015-11-04

Prediction models are used in clinical research to develop rules that can be used to accurately predict the outcome of the patients based on some of their characteristics. They represent a valuable tool in the decision making process of clinicians and health policy makers, as they enable them to estimate the probability that patients have or will develop a disease, will respond to a treatment, or that their disease will recur. The interest devoted to prediction models in the biomedical community has been growing in the last few years. Often the data used to develop the prediction models are class-imbalanced as only few patients experience the event (and therefore belong to minority class). Prediction models developed using class-imbalanced data tend to achieve sub-optimal predictive accuracy in the minority class. This problem can be diminished by using sampling techniques aimed at balancing the class distribution. These techniques include under- and oversampling, where a fraction of the majority class samples are retained in the analysis or new samples from the minority class are generated. The correct assessment of how the prediction model is likely to perform on independent data is of crucial importance; in the absence of an independent data set, cross-validation is normally used. While the importance of correct cross-validation is well documented in the biomedical literature, the challenges posed by the joint use of sampling techniques and cross-validation have not been addressed. We show that care must be taken to ensure that cross-validation is performed correctly on sampled data, and that the risk of overestimating the predictive accuracy is greater when oversampling techniques are used. Examples based on the re-analysis of real datasets and simulation studies are provided. We identify some results from the biomedical literature where the incorrect cross-validation was performed, where we expect that the performance of oversampling techniques was heavily overestimated.

An investigation of difficulties experienced by students developing unified modelling language (UML) class and sequence diagrams

NASA Astrophysics Data System (ADS)

Sien, Ven Yu

2011-12-01

Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they essentially do not know "what" to model. This article investigates the difficulties and misconceptions undergraduate students have with analysing systems using unified modelling language analysis class and sequence diagrams. These models were chosen because they represent important static and dynamic aspects of the software system under development. The results of this study will help students produce effective OO models, and facilitate software engineering lecturers design learning materials and approaches for introductory OOAD courses.

Experimental analysis and constitutive modelling of steel of A-IIIN strength class

NASA Astrophysics Data System (ADS)

Kruszka, Leopold; Janiszewski, Jacek

2015-09-01

Fundamentally important is the better understanding of behaviour of new building steels under impact loadings, including plastic deformations. Results of the experimental analysis in wide range of strain rates in compression at room temperature, as well as constitutive modelling for and B500SP structural steels of new A-IIIN Polish strength class, examined dynamically by split Hopkinson pressure bar technique at high strain rates, are presented in table and graphic forms. Dynamic mechanical characteristics of compressive strength for tested building structural steel are determined as well as dynamic mechanical properties of this material are compared with 18G2-b steel of A-II strength class, including effects of the shape of tested specimens, i.e. their slenderness. The paper focuses the attention on those experimental tests, their interpretation, and constitutive semi-empirical modelling of the behaviour of tested steels based on Johnson-Cook's model. Obtained results of analyses presented here are used for designing and numerical simulations of reinforced concrete protective structures.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2017-12-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2018-06-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Evaluation of POE and instructor-led problem-solving approaches integrated into force and motion lecture classes using a model analysis technique

NASA Astrophysics Data System (ADS)

Rakkapao, S.; Pengpan, T.; Srikeaw, S.; Prasitpong, S.

2014-01-01

This study aims to investigate the use of the predict-observe-explain (POE) approach integrated into large lecture classes on forces and motion. It is compared to the instructor-led problem-solving method using model analysis. The samples are science (SC, N = 420) and engineering (EN, N = 434) freshmen, from Prince of Songkla University, Thailand. Research findings from the force and motion conceptual evaluation indicate that the multimedia-supported POE method promotes students’ learning better than the problem-solving method, in particular for the velocity and acceleration concepts. There is a small shift of the students’ model states after the problem-solving instruction. Moreover, by using model analysis instructors are able to investigate students’ misconceptions and evaluate teaching methods. It benefits instructors in organizing subsequent instructional materials.

Striving for Discussion: An Analysis of a Teacher Educator's Comments in Whole-Class Conversation

ERIC Educational Resources Information Center

Reynolds, Todd

2016-01-01

During my English Language Arts methods class, I noticed that my discussion patterns were teacher-focused and in an Initiation-Response-Evaluation format. Because I wanted to model dialogic methods of whole-class discussions for my preservice teachers, I recoiled from this finding and began a self-study using an action research method to examine…

Revised Chapman-Enskog analysis for a class of forcing schemes in the lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Li, Q.; Zhou, P.; Yan, H. J.

2016-10-01

In the lattice Boltzmann (LB) method, the forcing scheme, which is used to incorporate an external or internal force into the LB equation, plays an important role. It determines whether the force of the system is correctly implemented in an LB model and affects the numerical accuracy. In this paper we aim to clarify a critical issue about the Chapman-Enskog analysis for a class of forcing schemes in the LB method in which the velocity in the equilibrium density distribution function is given by u =∑αeαfα / ρ , while the actual fluid velocity is defined as u ̂=u +δtF / (2 ρ ) . It is shown that the usual Chapman-Enskog analysis for this class of forcing schemes should be revised so as to derive the actual macroscopic equations recovered from these forcing schemes. Three forcing schemes belonging to the above class are analyzed, among which Wagner's forcing scheme [A. J. Wagner, Phys. Rev. E 74, 056703 (2006), 10.1103/PhysRevE.74.056703] is shown to be capable of reproducing the correct macroscopic equations. The theoretical analyses are examined and demonstrated with two numerical tests, including the simulation of Womersley flow and the modeling of flat and circular interfaces by the pseudopotential multiphase LB model.

A new prognostic model for chemotherapy-induced febrile neutropenia.

PubMed

Ahn, Shin; Lee, Yoon-Seon; Lee, Jae-Lyun; Lim, Kyung Soo; Yoon, Sung-Cheol

2016-02-01

The objective of this study was to develop and validate a new prognostic model for febrile neutropenia (FN). This study comprised 1001 episodes of FN: 718 for the derivation set and 283 for the validation set. Multivariate logistic regression analysis was performed with unfavorable outcome as the primary endpoint and bacteremia as the secondary endpoint. In the derivation set, risk factors for adverse outcomes comprised age ≥ 60 years (2 points), procalcitonin ≥ 0.5 ng/mL (5 points), ECOG performance score ≥ 2 (2 points), oral mucositis grade ≥ 3 (3 points), systolic blood pressure <90 mmHg (3 points), and respiratory rate ≥ 24 breaths/min (3 points). The model stratified patients into three severity classes, with adverse event rates of 6.0 % in class I (score ≤ 2), 27.3 % in class II (score 3-8), and 67.9 % in class III (score ≥ 9). Bacteremia was present in 1.1, 11.5, and 29.8 % of patients in class I, II, and III, respectively. The outcomes of the validation set were similar in each risk class. When the derivation and validation sets were integrated, unfavorable outcomes occurred in 5.9 % of the low-risk group classified by the new prognostic model and in 12.2 % classified by the Multinational Association for Supportive Care in Cancer (MASCC) risk index. With the new prognostic model, we can classify patients with FN into three classes of increasing adverse outcomes and bacteremia. Early discharge would be possible for class I patients, short-term observation could safely manage class II patients, and inpatient admission is warranted for class III patients.

PTSD symptom severity and psychiatric comorbidity in recent motor vehicle accident victims: a latent class analysis.

PubMed

Hruska, Bryce; Irish, Leah A; Pacella, Maria L; Sledjeski, Eve M; Delahanty, Douglas L

2014-10-01

We conducted a latent class analysis (LCA) on 249 recent motor vehicle accident (MVA) victims to examine subgroups that differed in posttraumatic stress disorder (PTSD) symptom severity, current major depressive disorder and alcohol/other drug use disorders (MDD/AoDs), gender, and interpersonal trauma history 6-weeks post-MVA. A 4-class model best fit the data with a resilient class displaying asymptomatic PTSD symptom levels/low levels of comorbid disorders; a mild psychopathology class displaying mild PTSD symptom severity and current MDD; a moderate psychopathology class displaying severe PTSD symptom severity and current MDD/AoDs; and a severe psychopathology class displaying extreme PTSD symptom severity and current MDD. Classes also differed with respect to gender composition and history of interpersonal trauma experience. These findings may aid in the development of targeted interventions for recent MVA victims through the identification of subgroups distinguished by different patterns of psychiatric problems experienced 6-weeks post-MVA. Copyright © 2014 Elsevier Ltd. All rights reserved.

PTSD Symptom Severity and Psychiatric Comorbidity in Recent Motor Vehicle Accident Victims: A Latent Class Analysis

PubMed Central

Hruska, Bryce; Irish, Leah A.; Pacella, Maria L.; Sledjeski, Eve M.; Delahanty, Douglas L.

2014-01-01

We conducted a latent class analysis (LCA) on 249 recent motor vehicle accident (MVA) victims to examine subgroups that differed in posttraumatic stress disorder (PTSD) symptom severity, current major depressive disorder and alcohol/other drug use disorders (MDD/AoDs), gender, and interpersonal trauma history 6-weeks post-MVA. A 4-class model best fit the data with a resilient class displaying asymptomatic PTSD symptom levels/low levels of comorbid disorders; a mild psychopathology class displaying mild PTSD symptom severity and current MDD; a moderate psychopathology class displaying severe PTSD symptom severity and current MDD/AoDs; and a severe psychopathology class displaying extreme PTSD symptom severity and current MDD. Classes also differed with respect to gender composition and history of interpersonal trauma experience. These findings may aid in the development of targeted interventions for recent MVA victims through the identification of subgroups distinguished by different patterns of psychiatric problems experienced 6-weeks post-MVA. PMID:25124501

Predicting the decision to pursue mediation in civil disputes: a hierarchical classes analysis.

PubMed

Reich, Warren A; Kressel, Kenneth; Scanlon, Kathleen M; Weiner, Gary A

2007-11-01

Clients (N = 185) involved in civil court cases completed the CPR Institute's Mediation Screen, which is designed to assist in making a decision about pursuing mediation. The authors modeled data using hierarchical classes analysis (HICLAS), a clustering algorithm that places clients into 1 set of classes and CPRMS items into another set of classes. HICLAS then links the sets of classes so that any class of clients can be identified in terms of the classes of items they endorsed. HICLAS-derived item classes reflected 2 underlying themes: (a) suitability of the dispute for a problem-solving process and (b) potential benefits of mediation. All clients who perceived that mediation would be beneficial also believed that the context of their conflict was favorable to mediation; however, not all clients who saw a favorable context believed they would benefit from mediation. The majority of clients who agreed to pursue mediation endorsed items reflecting both contextual suitability and perceived benefits of mediation.

Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach

NASA Astrophysics Data System (ADS)

Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel

2016-01-01

Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Teaching Tip: Using Activity Diagrams to Model Systems Analysis Techniques: Teaching What We Preach

ERIC Educational Resources Information Center

Lending, Diane; May, Jeffrey

2013-01-01

Activity diagrams are used in Systems Analysis and Design classes as a visual tool to model the business processes of "as-is" and "to-be" systems. This paper presents the idea of using these same activity diagrams in the classroom to model the actual processes (practices and techniques) of Systems Analysis and Design. This tip…

Bianchi class B spacetimes with electromagnetic fields

NASA Astrophysics Data System (ADS)

Yamamoto, Kei

2012-02-01

We carry out a thorough analysis on a class of cosmological space-times which admit three spacelike Killing vectors of Bianchi class B and contain electromagnetic fields. Using dynamical system analysis, we show that a family of electro-vacuum plane-wave solutions of the Einstein-Maxwell equations is the stable attractor for expanding universes. Phase dynamics are investigated in detail for particular symmetric models. We integrate the system exactly for some special cases to confirm the qualitative features. Some of the obtained solutions have not been presented previously to the best of our knowledge. Finally, based on those analyses, we discuss the relation between those homogeneous models and perturbations of open Friedmann-Lemaitre-Robertson-Walker universes. We argue that the electro-vacuum plane-wave modes correspond to a certain long-wavelength limit of electromagnetic perturbations.

Empowering Muslim Girls? Post-Feminism, Multiculturalism and the Production of the 'Model' Muslim Female Student in British Schools

ERIC Educational Resources Information Center

Mirza, Heidi Safia; Meetoo, Veena

2018-01-01

This article draws on an analysis of the narratives of teachers, policy-makers and young Muslim working-class women to explore how schools worked towards producing the model neoliberal middle-class female student. In two urban case-study schools, teaching staff encouraged the girls to actively challenge their culture through discourses grounded in…

Cost prediction model for various payloads and instruments for the Space Shuttle Orbiter

NASA Technical Reports Server (NTRS)

Hoffman, F. E.

1984-01-01

The following cost parameters of the space shuttle were undertaken: (1) to develop a cost prediction model for various payload classes of instruments and experiments for the Space Shuttle Orbiter; and (2) to show the implications of various payload classes on the cost of: reliability analysis, quality assurance, environmental design requirements, documentation, parts selection, and other reliability enhancing activities.

Persistence of discrimination: Revisiting Axtell, Epstein and Young

NASA Astrophysics Data System (ADS)

Weisbuch, Gérard

2018-02-01

We reformulate an earlier model of the "Emergence of classes..." proposed by Axtell et al. (2001) using more elaborate cognitive processes allowing a statistical physics approach. The thorough analysis of the phase space and of the basins of attraction leads to a reconsideration of the previous social interpretations: our model predicts the reinforcement of discrimination biases and their long term stability rather than the emergence of classes.

Managerial performance and cost efficiency of Japanese local public hospitals: a latent class stochastic frontier model.

PubMed

Besstremyannaya, Galina

2011-09-01

The paper explores the link between managerial performance and cost efficiency of 617 Japanese general local public hospitals in 1999-2007. Treating managerial performance as unobservable heterogeneity, the paper employs a panel data stochastic cost frontier model with latent classes. Financial parameters associated with better managerial performance are found to be positively significant in explaining the probability of belonging to the more efficient latent class. The analysis of latent class membership was consistent with the conjecture that unobservable technological heterogeneity reflected in the existence of the latent classes is related to managerial performance. The findings may support the cause for raising efficiency of Japanese local public hospitals by enhancing the quality of management. Copyright © 2011 John Wiley & Sons, Ltd.

An examination of generalized anxiety disorder and dysthymic disorder by latent class analysis.

PubMed

Rhebergen, D; van der Steenstraten, I M; Sunderland, M; de Graaf, R; Ten Have, M; Lamers, F; Penninx, B W J H; Andrews, G

2014-06-01

The nosological status of generalized anxiety disorder (GAD) versus dysthymic disorder (DD) has been questioned. The aim of this study was to examine qualitative differences within (co-morbid) GAD and DD symptomatology. Latent class analysis was applied to anxious and depressive symptomatology of respondents from three population-based studies (2007 Australian National Survey of Mental Health and Wellbeing; National Comorbidity Survey Replication; and Netherlands Mental Health Survey and Incidence Study-2; together known as the Triple study) and respondents from a multi-site naturalistic cohort [Netherlands Study of Depression and Anxiety (NESDA)]. Sociodemographics and clinical characteristics of each class were examined. A three-class (Triple study) and two-class (NESDA) model best fitted the data, reflecting mainly different levels of severity of symptoms. In the Triple study, no division into a predominantly GAD or DD co-morbidity subtype emerged. Likewise, in spite of the presence of pure GAD and DD cases in the NESDA sample, latent class analysis did not identify specific anxiety or depressive profiles in the NESDA study. Next, sociodemographics and clinical characteristics of each class were examined. Classes only differed in levels of severity. The absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

PubMed Central

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods Sibs-A (n=204) were assessed with the Mullen Scales of Early Learning from age 6–36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (n=52); non-ASD social/communication delay (broader autism phenotype; BAP) (n=31); and unaffected (n=121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. Results A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Conclusions Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. PMID:22574686

Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

PubMed

Prat, Chantal; Besalú, Emili; Bañeras, Lluís; Anticó, Enriqueta

2011-06-15

The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses. Copyright © 2010 Elsevier Ltd. All rights reserved.

Severity of mental illness as a result of multiple childhood adversities: US National Epidemiologic Survey.

PubMed

Curran, Emma; Adamson, Gary; Stringer, Maurice; Rosato, Michael; Leavey, Gerard

2016-05-01

To examine patterns of childhood adversity, their long-term consequences and the combined effect of different childhood adversity patterns as predictors of subsequent psychopathology. Secondary analysis of data from the US National Epidemiologic Survey on alcohol and related conditions. Using latent class analysis to identify childhood adversity profiles; and using multinomial logistic regression to validate and further explore these profiles with a range of associated demographic and household characteristics. Finally, confirmatory factor analysis substantiated initial latent class analysis findings by investigating a range of mental health diagnoses. Latent class analysis generated a three-class model of childhood adversity in which 60 % of participants were allocated to a low adversity class; 14 % to a global adversities class (reporting exposures for all the derived latent classes); and 26 % to a domestic emotional and physical abuse class (exposed to a range of childhood adversities). Confirmatory Factor analysis defined an internalising-externalising spectrum to represent lifetime reporting patterns of mental health disorders. Using logistic regression, both adversity groups showed specific gender and race/ethnicity differences, related family discord and increased psychopathology. We identified underlying patterns in the exposure to childhood adversity and associated mental health. These findings are informative in their description of the configuration of adversities, rather than focusing solely on the cumulative aspect of experience. Amelioration of longer-term negative consequences requires early identification of psychopathology risk factors that can inform protective and preventive interventions. This study highlights the utility of screening for childhood adversities when individuals present with symptoms of psychiatric disorders.

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

PubMed

Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

2015-05-01

We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Characterizing Health Disparities in the Age of Autism Diagnosis in a Study of 8-Year-Old Children

ERIC Educational Resources Information Center

Parikh, Chandni; Kurzius-Spencer, Margaret; Mastergeorge, Ann M.; Pettygrove, Sydney

2018-01-01

The diagnosis of autism spectrum disorder (ASD) is often delayed from the time of noted concerns to the actual diagnosis. The current study used child- and family-level factors to identify homogeneous classes in a surveillance-based sample (n = 2303) of 8-year-old children with ASD. Using latent class analysis, a 5-class model emerged and the…

A latent transition model of the effects of a teen dating violence prevention initiative.

PubMed

Williams, Jason; Miller, Shari; Cutbush, Stacey; Gibbs, Deborah; Clinton-Sherrod, Monique; Jones, Sarah

2015-02-01

Patterns of physical and psychological teen dating violence (TDV) perpetration, victimization, and related behaviors were examined with data from the evaluation of the Start Strong: Building Healthy Teen Relationships initiative, a dating violence primary prevention program targeting middle school students. Latent class and latent transition models were used to estimate distinct patterns of TDV and related behaviors of bullying and sexual harassment in seventh grade students at baseline and to estimate transition probabilities from one pattern of behavior to another at the 1-year follow-up. Intervention effects were estimated by conditioning transitions on exposure to Start Strong. Latent class analyses suggested four classes best captured patterns of these interrelated behaviors. Classes were characterized by elevated perpetration and victimization on most behaviors (the multiproblem class), bullying perpetration/victimization and sexual harassment victimization (the bully-harassment victimization class), bullying perpetration/victimization and psychological TDV victimization (bully-psychological victimization), and experience of bully victimization (bully victimization). Latent transition models indicated greater stability of class membership in the comparison group. Intervention students were less likely to transition to the most problematic pattern and more likely to transition to the least problem class. Although Start Strong has not been found to significantly change TDV, alternative evaluation models may find important differences. Latent transition analysis models suggest positive intervention impact, especially for the transitions at the most and the least positive end of the spectrum. Copyright © 2015. Published by Elsevier Inc.

Search for Type Ia supernova NUV-optical subclasses

NASA Astrophysics Data System (ADS)

Cinabro, David; Scolnic, Daniel; Kessler, Richard; Li, Ashley; Miller, Jake

2017-04-01

In response to a recently reported observation of evidence for two classes of Type Ia supernovae (SNe Ia) distinguished by their brightness in the rest-frame near-ultraviolet (NUV), we search for the phenomenon in publicly available light-curve data. We use the SNANA supernova analysis package to simulate SN Ia light curves in the Sloan Digital Sky Survey (SDSS) Supernova Search and the Supernova Legacy Survey (SNLS) with a model of two distinct ultraviolet classes of SNe Ia and a conventional model with a single broad distribution of SN-Ia ultraviolet brightnesses. We compare simulated distributions of rest-frame colours with these two models to those observed in 158 SNe Ia in the SDSS and SNLS data. The SNLS sample of 99 SNe Ia is in clearly better agreement with a model with one class of SN Ia light curves and shows no evidence for distinct NUV sub-classes. The SDSS sample of 59 SNe Ia with poorer colour resolution does not distinguish between the two models.

Verification of relationship model between Korean new elderly class's recovery resilience and productive aging.

PubMed

Cho, Gun-Sang; Kim, Dae-Sung; Yi, Eun-Surk

2015-12-01

The purpose of this study is to verification of relationship model between Korean new elderly class's recovery resilience and productive aging. As of 2013, this study sampled preliminary elderly people in Gyeonggi-do and other provinces nationwide. Data from a total of effective 484 subjects was analyzed. The collected data was processed using the IBM SPSS 20.0 and AMOS 20.0, and underwent descriptive statistical analysis, confirmatory factor analysis, and structure model verification. The path coefficient associated with model fitness was examined. The standardization path coefficient between recovery resilience and productive aging is β=0.975 (t=14.790), revealing a statistically significant positive effect. Thus, it was found that the proposed basic model on the direct path of recovery resilience and productive aging was fit for the model.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 1. The Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

77 FR 21896 - Approval and Promulgation of Air Quality Implementation Plans; State of Nevada; Regional Haze...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-12

... proposed approval, including specific comments on NDEP's modeling and cost analysis of the RGGS BART Determination for NO X . See Modeling for the Reid Gardner Generating Station: Visibility Impacts in Class I... independent modeling analysis to evaluate the incremental visibility improvement attributable to the NO X...

School climate and bullying victimization: a latent class growth model analysis.

PubMed

Gage, Nicholas A; Prykanowski, Debra A; Larson, Alvin

2014-09-01

Researchers investigating school-level approaches for bullying prevention are beginning to discuss and target school climate as a construct that (a) may predict prevalence and (b) be an avenue for school-wide intervention efforts (i.e., increasing positive school climate). Although promising, research has not fully examined and established the social-ecological link between school climate factors and bullying/peer aggression. To address this gap, we examined the association between school climate factors and bullying victimization for 4,742 students in Grades 3-12 across 3 school years in a large, very diverse urban school district using latent class growth modeling. Across 3 different models (elementary, secondary, and transition to middle school), a 3-class model was identified, which included students at high-risk for bullying victimization. Results indicated that, for all students, respect for diversity and student differences (e.g., racial diversity) predicted within-class decreases in reports of bullying. High-risk elementary students reported that adult support in school was a significant predictor of within-class reduction of bullying, and high-risk secondary students report peer support as a significant predictor of within-class reduction of bullying. PsycINFO Database Record (c) 2014 APA, all rights reserved.

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

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

Cai, Hao; Burnham, Andrew; Wang, Michael

2015-05-01

Heavy-duty vehicles (HDVs) account for a significant portion of the U.S. transportation sector’s fuel consumption, greenhouse gas (GHG) emissions, and air pollutant emissions. In our most recent efforts, we expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model to include life-cycle analysis of HDVs. In particular, the GREET expansion includes the fuel consumption, GHG emissions, and air pollutant emissions of a variety of conventional (i.e., diesel and/or gasoline) HDV types, including Class 8b combination long-haul freight trucks, Class 8b combination short-haul freight trucks, Class 8b dump trucks, Class 8a refuse trucks, Class 8a transit buses, Classmore » 8a intercity buses, Class 6 school buses, Class 6 single-unit delivery trucks, Class 4 single-unit delivery trucks, and Class 2b heavy-duty pickup trucks and vans. These vehicle types were selected to represent the diversity in the U.S. HDV market, and specific weight classes and body types were chosen on the basis of their fuel consumption using the 2002 Vehicle Inventory and Use Survey (VIUS) database. VIUS was also used to estimate the fuel consumption and payload carried for most of the HDV types. In addition, fuel economy projections from the U.S. Energy Information Administration, transit databases, and the literature were examined. The U.S. Environmental Protection Agency’s latest Motor Vehicle Emission Simulator was employed to generate tailpipe air pollutant emissions of diesel and gasoline HDV types.« less

A blended learning approach for teaching computer programming: design for large classes in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Bayu Bati, Tesfaye; Gelderblom, Helene; van Biljon, Judy

2014-01-01

The challenge of teaching programming in higher education is complicated by problems associated with large class teaching, a prevalent situation in many developing countries. This paper reports on an investigation into the use of a blended learning approach to teaching and learning of programming in a class of more than 200 students. A course and learning environment was designed by integrating constructivist learning models of Constructive Alignment, Conversational Framework and the Three-Stage Learning Model. Design science research is used for the course redesign and development of the learning environment, and action research is integrated to undertake participatory evaluation of the intervention. The action research involved the Students' Approach to Learning survey, a comparative analysis of students' performance, and qualitative data analysis of data gathered from various sources. The paper makes a theoretical contribution in presenting a design of a blended learning solution for large class teaching of programming grounded in constructivist learning theory and use of free and open source technologies.

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Damage modeling and statistical analysis of optics damage performance in MJ-class laser systems.

PubMed

Liao, Zhi M; Raymond, B; Gaylord, J; Fallejo, R; Bude, J; Wegner, P

2014-11-17

Modeling the lifetime of a fused silica optic is described for a multiple beam, MJ-class laser system. This entails combining optic processing data along with laser shot data to account for complete history of optic processing and shot exposure. Integrating with online inspection data allows for the construction of a performance metric to describe how an optic performs with respect to the model. This methodology helps to validate the damage model as well as allows strategic planning and identifying potential hidden parameters that are affecting the optic's performance.

Entry, Descent and Landing Systems Analysis: Exploration Class Simulation Overview and Results

NASA Technical Reports Server (NTRS)

DwyerCianciolo, Alicia M.; Davis, Jody L.; Shidner, Jeremy D.; Powell, Richard W.

2010-01-01

NASA senior management commissioned the Entry, Descent and Landing Systems Analysis (EDL-SA) Study in 2008 to identify and roadmap the Entry, Descent and Landing (EDL) technology investments that the agency needed to make in order to successfully land large payloads at Mars for both robotic and exploration or human-scale missions. The year one exploration class mission activity considered technologies capable of delivering a 40-mt payload. This paper provides an overview of the exploration class mission study, including technologies considered, models developed and initial simulation results from the EDL-SA year one effort.

Longitudinal analysis of latent classes of psychopathology and patterns of class migration in survivors of severe injury.

PubMed

Forbes, David; Nickerson, Angela; Alkemade, Nathan; Bryant, Richard A; Creamer, Mark; Silove, Derrick; McFarlane, Alexander C; Van Hooff, Miranda; Fletcher, Susan L; O'Donnell, Meaghan

2015-09-01

Little research to date has explored the typologies of psychopathology following trauma, beyond development of particular diagnoses such as posttraumatic stress disorder (PTSD). The objective of this study was to determine the longitudinal patterns of these typologies, especially the movement of persons across clusters of psychopathology. In this 6-year longitudinal study, 1,167 hospitalized severe injury patients who were recruited between April 2004-February 2006 were analyzed, with repeated measures at baseline, 3 months, 12 months, and 72 months after injury. All patients met the DSM-IV criterion A1 for PTSD. Structured clinical interviews were used to assess psychiatric disorders at each follow-up point. Latent class analysis and latent transition analysis were applied to assess clusters of individuals determined by psychopathology. The Mini International Neuropsychiatric Interview (MINI) and Clinician-Administered PTSD Scale (CAPS) were employed to complete diagnoses. Four latent classes were identified at each time point: (1) Alcohol/Depression class (3 months, 2.1%; 12 months, 1.3%; and 72 months, 1.1%), (2) Alcohol class (3 months, 3.3%; 12 months, 3.7%; and 72 months, 5.4%), (3) PTSD/Depression class (3 months, 10.3%; 12 months, 11.5%; and 72 months, 6.4%), and (4) No Disorder class (3 months, 84.2%; 12 months, 83.5%; and 72 months, 87.1%). Latent transition analyses conducted across the 2 transition points (12 months and 72 months) found consistently high levels of stability in the No Disorder class (90.9%, 93.0%, respectively) but lower and reducing levels of consistency in the PTSD/Depression class (81.3%, 46.6%), the Alcohol/Depression class (59.7%, 21.5%), and the Alcohol class (61.0%, 36.5%), demonstrating high levels of between-class migration. Despite the array of psychiatric disorders that may develop following severe injury, a 4-class model best described the data with excellent classification certainty. The high levels of migration across classes indicate a complex pattern of psychopathology expression over time. The findings have considerable implications for tailoring multifocused interventions to class type, as well as flexible stepped care models, and for the potential development and delivery of transdiagnostic interventions targeting underlying mechanisms. © Copyright 2015 Physicians Postgraduate Press, Inc.

CMB constraints on β-exponential inflationary models

NASA Astrophysics Data System (ADS)

Santos, M. A.; Benetti, M.; Alcaniz, J. S.; Brito, F. A.; Silva, R.

2018-03-01

We analyze a class of generalized inflationary models proposed in ref. [1], known as β-exponential inflation. We show that this kind of potential can arise in the context of brane cosmology, where the field describing the size of the extra-dimension is interpreted as the inflaton. We discuss the observational viability of this class of model in light of the latest Cosmic Microwave Background (CMB) data from the Planck Collaboration through a Bayesian analysis, and impose tight constraints on the model parameters. We find that the CMB data alone prefer weakly the minimal standard model (ΛCDM) over the β-exponential inflation. However, when current local measurements of the Hubble parameter, H0, are considered, the β-inflation model is moderately preferred over the ΛCDM cosmology, making the study of this class of inflationary models interesting in the context of the current H0 tension.

Developmental trajectories of paediatric headache - sex-specific analyses and predictors.

PubMed

Isensee, Corinna; Fernandez Castelao, Carolin; Kröner-Herwig, Birgit

2016-01-01

Headache is the most common pain disorder in children and adolescents and is associated with diverse dysfunctions and psychological symptoms. Several studies evidenced sex-specific differences in headache frequency. Until now no study exists that examined sex-specific patterns of change in paediatric headache across time and included pain-related somatic and (socio-)psychological predictors. Latent Class Growth Analysis (LCGA) was used in order to identify different trajectory classes of headache across four annual time points in a population-based sample (n = 3 227; mean age 11.34 years; 51.2 % girls). In multinomial logistic regression analyses the influence of several predictors on the class membership was examined. For girls, a four-class model was identified as the best fitting model. While the majority of girls reported no (30.5 %) or moderate headache frequencies (32.5 %) across time, one class with a high level of headache days (20.8 %) and a class with an increasing headache frequency across time (16.2 %) were identified. For boys a two class model with a 'no headache class' (48.6 %) and 'moderate headache class' (51.4 %) showed the best model fit. Regarding logistic regression analyses, migraine and parental headache proved to be stable predictors across sexes. Depression/anxiety was a significant predictor for all pain classes in girls. Life events, dysfunctional stress coping and school burden were also able to differentiate at least between some classes in both sexes. The identified trajectories reflect sex-specific differences in paediatric headache, as seen in the number and type of classes extracted. The documented risk factors can deliver ideas for preventive actions and considerations for treatment programmes.

Regional impacts of oil and gas development on ozone formation in the western United States.

PubMed

Rodriguez, Marco A; Barna, Michael G; Moore, Tom

2009-09-01

The Intermountain West is currently experiencing increased growth in oil and gas production, which has the potential to affect the visibility and air quality of various Class I areas in the region. The following work presents an analysis of these impacts using the Comprehensive Air Quality Model with extensions (CAMx). CAMx is a state-of-the-science, "one-atmosphere" Eulerian photochemical dispersion model that has been widely used in the assessment of gaseous and particulate air pollution (ozone, fine [PM2.5], and coarse [PM10] particulate matter). Meteorology and emissions inventories developed by the Western Regional Air Partnership Regional Modeling Center for regional haze analysis and planning are used to establish an ozone baseline simulation for the year 2002. The predicted range of values for ozone in the national parks and other Class I areas in the western United States is then evaluated with available observations from the Clean Air Status and Trends Network (CASTNET). This evaluation demonstrates the model's suitability for subsequent planning, sensitivity, and emissions control strategy modeling. Once the ozone baseline simulation has been established, an analysis of the model results is performed to investigate the regional impacts of oil and gas development on the ozone concentrations that affect the air quality of Class I areas. Results indicate that the maximum 8-hr ozone enhancement from oil and gas (9.6 parts per billion [ppb]) could affect southwestern Colorado and northwestern New Mexico. Class I areas in this region that are likely to be impacted by increased ozone include Mesa Verde National Park and Weminuche Wilderness Area in Colorado and San Pedro Parks Wilderness Area, Bandelier Wilderness Area, Pecos Wilderness Area, and Wheeler Peak Wilderness Area in New Mexico.

Longitudinal burnout-collaboration patterns in Japanese medical care workers at special needs schools: a latent class growth analysis

PubMed Central

Kanayama, Mieko; Suzuki, Machiko; Yuma, Yoshikazu

2016-01-01

The present study aimed to identify and characterize potential burnout types and the relationship between burnout and collaboration over time. Latent class growth analysis and the growth mixture model were used to identify and characterize heterogeneous patterns of longitudinal stability and change in burnout, and the relationship between burnout and collaboration. We collected longitudinal data at three time points based on Japanese academic terms. The 396 study participants included academic teachers, yogo teachers, and registered nurses in Japanese special needs schools. The best model included four types of both burnout and collaboration in latent class growth analysis with intercept, slope, and quadratic terms. The four types of burnout were as follows: low stable, moderate unstable, high unstable, and high decreasing. They were identified as involving inverse collaboration function. The results indicated that there could be dynamic burnout types, namely moderate unstable, high unstable, and high decreasing, when focusing on growth trajectories in latent class analyses. The finding that collaboration was dynamic for dynamic burnout types and stable for stable burnout types is of great interest. This was probably related to the inverse relationship between the two constructs. PMID:27366107

Machine learning for predicting soil classes in three semi-arid landscapes

USGS Publications Warehouse

Brungard, Colby W.; Boettinger, Janis L.; Duniway, Michael C.; Wills, Skye A.; Edwards, Thomas C.

2015-01-01

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

Creating opportunities to influence self-efficacy through modeling instruction

NASA Astrophysics Data System (ADS)

Sawtelle, Vashti; Brewe, Eric; Goertzen, Renee Michelle; Kramer, Laird H.

2012-02-01

In this paper we present an initial analysis connecting key elements of Modeling Instruction (MI) to self-efficacy experience opportunities. Previously, we demonstrated that MI has positive effects on self-efficacy when compared with traditional Lecture instruction [1]. We also found a particularly strong positive effect on the social persuasion source of self-efficacy for women in the MI class. Our current study seeks to understand through what mechanisms MI influences self-efficacy. We demonstrate this connection through an in-depth analysis of video chosen to exemplify Modeling techniques used in a problem-solving episode by three female participants enrolled in a MI introductory physics class. We provide a rich and descriptive analysis of the self-efficacy experiences opportunities within this context and discuss how these opportunities provide a potential explanation of how MI influences self-efficacy.

An Educational Model for Hands-On Hydrology Education

NASA Astrophysics Data System (ADS)

AghaKouchak, A.; Nakhjiri, N.; Habib, E. H.

2014-12-01

This presentation provides an overview of a hands-on modeling tool developed for students in civil engineering and earth science disciplines to help them learn the fundamentals of hydrologic processes, model calibration, sensitivity analysis, uncertainty assessment, and practice conceptual thinking in solving engineering problems. The toolbox includes two simplified hydrologic models, namely HBV-EDU and HBV-Ensemble, designed as a complement to theoretical hydrology lectures. The models provide an interdisciplinary application-oriented learning environment that introduces the hydrologic phenomena through the use of a simplified conceptual hydrologic model. The toolbox can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation) are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI) and an ensemble simulation scheme that can be used for teaching more advanced topics including uncertainty analysis, and ensemble simulation. Both models have been administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of hydrology.

Dynamic analysis of a hepatitis B model with three-age-classes

NASA Astrophysics Data System (ADS)

Zhang, Suxia; Zhou, Yicang

2014-07-01

Based on the fact that the likelihood of becoming chronically infected is dependent on age at primary infection Kane (1995) [2], Edmunds et al. (1993) [3], Medley et al. (2001) [4], and Ganem and Prince (2004) [6], we formulate a hepatitis B transmission model with three age classes. The reproduction number, R0 is defined and the dynamical behavior of the model is analyzed. It is proved that the disease-free equilibrium is globally stable if R0<1, and there exists at least one endemic equilibrium and that the disease is uniformly persistent if R0>1. The unique endemic equilibrium and its global stability is obtained in a special case. Simulations are also conducted to compare the dynamical behavior of the model with and without age classes.

Bayesian Analysis of Hmi Images and Comparison to Tsi Variations and MWO Image Observables

NASA Astrophysics Data System (ADS)

Parker, D. G.; Ulrich, R. K.; Beck, J.; Tran, T. V.

2015-12-01

We have previously applied the Bayesian automatic classification system AutoClass to solar magnetogram and intensity images from the 150 Foot Solar Tower at Mount Wilson to identify classes of solar surface features associated with variations in total solar irradiance (TSI) and, using those identifications, modeled TSI time series with improved accuracy (r > 0.96). (Ulrich, et al, 2010) AutoClass identifies classes by a two-step process in which it: (1) finds, without human supervision, a set of class definitions based on specified attributes of a sample of the image data pixels, such as magnetic field and intensity in the case of MWO images, and (2) applies the class definitions thus found to new data sets to identify automatically in them the classes found in the sample set. HMI high resolution images capture four observables-magnetic field, continuum intensity, line depth and line width-in contrast to MWO's two observables-magnetic field and intensity. In this study, we apply AutoClass to the HMI observables for images from June, 2010 to December, 2014 to identify solar surface feature classes. We use contemporaneous TSI measurements to determine whether and how variations in the HMI classes are related to TSI variations and compare the characteristic statistics of the HMI classes to those found from MWO images. We also attempt to derive scale factors between the HMI and MWO magnetic and intensity observables.The ability to categorize automatically surface features in the HMI images holds out the promise of consistent, relatively quick and manageable analysis of the large quantity of data available in these images. Given that the classes found in MWO images using AutoClass have been found to improve modeling of TSI, application of AutoClass to the more complex HMI images should enhance understanding of the physical processes at work in solar surface features and their implications for the solar-terrestrial environment.Ulrich, R.K., Parker, D, Bertello, L. and Boyden, J. 2010, Solar Phys. , 261 , 11.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 3. Appendices to the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Latent classes of emotional and behavioural problems in epidemiological and referred samples and their relations to DSM-IV diagnoses.

PubMed

Bianchi, Valentina; Brambilla, Paolo; Garzitto, Marco; Colombo, Paola; Fornasari, Livia; Bellina, Monica; Bonivento, Carolina; Tesei, Alessandra; Piccin, Sara; Conte, Stefania; Perna, Giampaolo; Frigerio, Alessandra; Castiglioni, Isabella; Fabbro, Franco; Molteni, Massimo; Nobile, Maria

2017-05-01

Researchers' interest have recently moved toward the identification of recurrent psychopathological profiles characterized by concurrent elevations on different behavioural and emotional traits. This new strategy turned to be useful in terms of diagnosis and outcome prediction. We used a person-centred statistical approach to examine whether different groups could be identified in a referred sample and in a general-population sample of children and adolescents, and we investigated their relation to DSM-IV diagnoses. A latent class analysis (LCA) was performed on the Child Behaviour Checklist (CBCL) syndrome scales of the referred sample (N = 1225), of the general-population sample (N = 3418), and of the total sample. Models estimating 1-class through 5-class solutions were compared and agreement in the classification of subjects was evaluated. Chi square analyses, a logistic regression, and a multinomial logistic regression analysis were used to investigate the relations between classes and diagnoses. In the two samples and in the total sample, the best-fitting models were 4-class solutions. The identified classes were Internalizing Problems (15.68%), Severe Dysregulated (7.82%), Attention/Hyperactivity (10.19%), and Low Problems (66.32%). Subsequent analyses indicated a significant relationship between diagnoses and classes as well as a main association between the severe dysregulated class and comorbidity. Our data suggested the presence of four different psychopathological profiles related to different outcomes in terms of psychopathological diagnoses. In particular, our results underline the presence of a profile characterized by severe emotional and behavioural dysregulation that is mostly associated with the presence of multiple diagnosis.

Microarrays for Undergraduate Classes

ERIC Educational Resources Information Center

Hancock, Dale; Nguyen, Lisa L.; Denyer, Gareth S.; Johnston, Jill M.

2006-01-01

A microarray experiment is presented that, in six laboratory sessions, takes undergraduate students from the tissue sample right through to data analysis. The model chosen, the murine erythroleukemia cell line, can be easily cultured in sufficient quantities for class use. Large changes in gene expression can be induced in these cells by…

Modeling Statistical Insensitivity: Sources of Suboptimal Behavior

ERIC Educational Resources Information Center

Gagliardi, Annie; Feldman, Naomi H.; Lidz, Jeffrey

2017-01-01

Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the…

Confirmatory factors analysis of science teacher leadership in the Thailand world-class standard schools

NASA Astrophysics Data System (ADS)

Thawinkarn, Dawruwan

2018-01-01

This research aims to analyze factors of science teacher leadership in the Thailand World-Class Standard Schools. The research instrument was a five scale rating questionnaire with reliability 0.986. The sample group included 500 science teachers from World-Class Standard Schools who had been selected by using the stratified random sampling technique. Factor analysis of science teacher leadership in the Thailand World-Class Standard Schools was conducted by using M plus for Windows. The results are as follows: The results of confirmatory factor analysis on science teacher leadership in the Thailand World-Class Standard Schools revealed that the model significantly correlated with the empirical data. The consistency index value was x2 = 105.655, df = 88, P-Value = 0.086, TLI = 0.997, CFI = 0.999, RMSEA = 0.022, and SRMR = 0.019. The value of factor loading of science teacher leadership was positive, with statistical significance at the level of 0.01. The value of six factors was between 0.880-0.996. The highest factor loading was the professional learning community, followed by child-centered instruction, participation in development, the role model in teaching, transformational leaders, and self-development with factor loading at 0.996, 0.928, 0.911, 0.907, 0.901, and 0.871, respectively. The reliability of each factor was 99.1%, 86.0%, 83.0%, 82.2%, 81.0%, and 75.8%, respectively.

Tailoring community-based wellness initiatives with latent class analysis--Massachusetts Community Transformation Grant projects.

PubMed

Arcaya, Mariana; Reardon, Timothy; Vogel, Joshua; Andrews, Bonnie K; Li, Wenjun; Land, Thomas

2014-02-13

Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts' municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach. Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains. Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges. Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.

Latino cigarette smoking patterns by gender in a US national sample

PubMed Central

Kristman-Valente, Allison; Flaherty, Brian P.

2015-01-01

Background Latino smokers are a rising public health concern who experience elevated tobacco related health disparities. Purpose Additional information on Latino smoking is needed to inform screening and treatment. Analysis Latent class analysis using smoking frequency, cigarette preferences, onset, smoking duration, cigarettes per day and minutes to first cigarette were used to create multivariate latent smoking profiles for Latino men and women. Results Final models found seven classes for Latinas and nine classes for Latinos. Despite a common finding in the literature that Latino smokers are more likely to be low-risk, intermittent smokers, the majority of classes, for both males and females, described patterns of high-risk, daily smoking. Gender variations in smoking classes were noted. Conclusions Several markers of smoking risk were identified among both male and female Latino smokers including long durations of smoking, daily smoking and preference for specialty cigarettes, all factors associated with long-term health consequences. PMID:26304857

A latent class approach to understanding patterns of peer victimization in four low-resource settings.

PubMed

Nguyen, Amanda J; Bradshaw, Catherine; Townsend, Lisa; Gross, Alden L; Bass, Judith

2016-08-17

Peer victimization is a common form of aggression among school-aged youth, but research is sparse regarding victimization dynamics in low- and middle-income countries (LMIC). Person-centered approaches have demonstrated utility in understanding patterns of victimization in the USA. We aimed to empirically identify classes of youth with unique victimization patterns in four LMIC settings using latent class analysis (LCA). We used data on past-year exposure to nine forms of victimization reported by 3536 youth (aged 15 years) from the Young Lives (YL) study in Ethiopia, India (Andhra Pradesh and Telangana states), Peru, and Vietnam. Sex and rural/urban context were examined as predictors of class membership. LCA supported a 2-class model in Peru, a 3-class model in Ethiopia and Vietnam, and a 4-class model in India. Classes were predominantly ordered by severity, suggesting that youth who experienced one form of victimization were likely to experience other forms as well. In India, two unordered classes were also observed, characterized by direct and indirect victimization. Boys were more likely than girls to be in the highly victimized (HV) class in Ethiopia and India. Urban contexts, compared with rural, conferred higher risk of victimization in Ethiopia and Peru, and lower risk in India and Vietnam. The identified patterns of multiple forms of victimization highlight a limitation of common researcher-driven classifications and suggest avenues for future person-centered research to improve intervention development in LMIC settings.

The Fourth Annual Thermal and Fluids Analysis Workshop

NASA Technical Reports Server (NTRS)

1992-01-01

The Fourth Annual Thermal and Fluids Analysis Workshop was held from August 17-21, 1992, at NASA Lewis Research Center. The workshop consisted of classes, vendor demonstrations, and paper sessions. The classes and vendor demonstrations provided participants with the information on widely used tools for thermal and fluids analysis. The paper sessions provided a forum for the exchange of information and ideas among thermal and fluids analysts. Paper topics included advances and uses of established thermal and fluids computer codes (such as SINDA and TRASYS) as well as unique modeling techniques and applications.

A person-centered analysis of posttraumatic stress disorder symptoms following a natural disaster: predictors of latent class membership.

PubMed

Rosellini, Anthony J; Coffey, Scott F; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Person-Centered Analysis of Posttraumatic Stress Disorder Symptoms Following a Natural Disaster: Predictors of Latent Class Membership

PubMed Central

Rosellini, Anthony J.; Coffey, Scott F.; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. PMID:24334161

Standard representation and unified stability analysis for dynamic artificial neural network models.

PubMed

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae; Song, Tae-Min

2017-07-24

Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression. The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. ©Hyesil Jung, Hyeoun-Ae Park, Tae-Min Song. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.07.2017.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

PubMed Central

Jung, Hyesil; Song, Tae-Min

2017-01-01

Background Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression. Conclusions The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. PMID:28739560

Colloquium: Statistical mechanics of money, wealth, and income

NASA Astrophysics Data System (ADS)

Yakovenko, Victor M.; Rosser, J. Barkley, Jr.

2009-10-01

This Colloquium reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models with interacting economic agents. Alternative scenarios are also reviewed. Data analysis of the empirical distributions of wealth and income reveals a two-class distribution. The majority of the population belongs to the lower class, characterized by the exponential (“thermal”) distribution, whereas a small fraction of the population in the upper class is characterized by the power-law (“superthermal”) distribution. The lower part is very stable, stationary in time, whereas the upper part is highly dynamical and out of equilibrium.

Opioid withdrawal, craving, and use during and after outpatient buprenorphine stabilization and taper: A discrete survival and growth mixture model

PubMed Central

Stotts, Angela L.; Green, Charles; Potter, Jennifer S.; Marino, Elise N.; Walker, Robrina; Weiss, Roger D.; Trivedi, Madhukar

2014-01-01

Most patients relapse to opioids within one month of opioid agonist detoxification, making the antecedents and parallel processes of first use critical for investigation. Craving and withdrawal are often studied in relationship to opioid outcomes, and a novel analytic strategy applied to these two phenomena may indicate targeted intervention strategies. Specifically, this secondary data analysis of the Prescription Opioid Addiction Treatment Study used a discrete-time mixture analysis with time-to-first opioid use (survival) simultaneously predicted by craving and withdrawal growth trajectories. This analysis characterized heterogeneity among prescription opioid-dependent individuals (N=653) into latent classes (i.e., latent class analysis [LCA]) during and after buprenorphine/naloxone stabilization and taper. A 4-latent class solution was selected for overall model fit and clinical parsimony. In order of shortest to longest time-to-first use, the 4 classes were characterized as 1) high craving and withdrawal 2) intermediate craving and withdrawal 3) high initial craving with low craving and withdrawal trajectories and 4) a low initial craving with low craving and withdrawal trajectories. Odds ratio calculations showed statistically significant differences in time-to-first use across classes. Generally, participants with lower baseline levels and greater decreases in craving and withdrawal during stabilization combined with slower craving and withdrawal rebound during buprenorphine taper remained opioid-free longer. This exploratory work expanded on the importance of monitoring craving and withdrawal during buprenorphine induction, stabilization, and taper. Future research may allow individually tailored and timely interventions to be developed to extend time-to-first opioid use. PMID:25282598

Discriminative components of data.

PubMed

Peltonen, Jaakko; Kaski, Samuel

2005-01-01

A simple probabilistic model is introduced to generalize classical linear discriminant analysis (LDA) in finding components that are informative of or relevant for data classes. The components maximize the predictability of the class distribution which is asymptotically equivalent to 1) maximizing mutual information with the classes, and 2) finding principal components in the so-called learning or Fisher metrics. The Fisher metric measures only distances that are relevant to the classes, that is, distances that cause changes in the class distribution. The components have applications in data exploration, visualization, and dimensionality reduction. In empirical experiments, the method outperformed, in addition to more classical methods, a Renyi entropy-based alternative while having essentially equivalent computational cost.

A Latent Class Analysis of Maternal Responsiveness and Autonomy-Granting in Early Adolescence: Prediction to Later Adolescent Sexual Risk-Taking

PubMed Central

Lanza, H. Isabella; Huang, David Y. C.; Murphy, Debra A.; Hser, Yih-Ing

2013-01-01

The present study sought to extend empirical inquiry related to the role of parenting on adolescent sexual risk-taking by using latent class analysis (LCA) to identify patterns of adolescent-reported mother responsiveness and autonomy-granting in early adolescence and examine associations with sexual risk-taking in mid- and late-adolescence. Utilizing a sample of 12- to 14-year-old adolescents (N = 4,743) from the 1997 National Longitudinal Survey of Youth (NLSY97), results identified a four-class model of maternal responsiveness and autonomy-granting: low responsiveness/high autonomy-granting, moderate responsiveness/moderate autonomy-granting, high responsiveness/low autonomy-granting, high responsiveness/moderate autonomy-granting. Membership in the low responsiveness/high autonomy-granting class predicted greater sexual risk-taking in mid- and late-adolescence compared to all other classes, and membership in the high responsiveness/ moderate autonomy-granting class predicted lower sexual risk-taking. Gender and ethnic differences in responsiveness and autonomy-granting class membership were also found, potentially informing gender and ethnic disparities of adolescent sexual risk-taking. PMID:23828712

Retrenched Welfare Regimes Still Lessen Social Class Inequalities in Health: A Longitudinal Analysis of the 2003-2010 EU-SILC in 23 European Countries.

PubMed

Muntaner, C; Davis, O; McIsaack, K; Kokkinen, L; Shankardass, K; O'Campo, P

2017-07-01

This article builds on recent work that has explored how welfare regimes moderate social class inequalities in health. It extends research to date by using longitudinal data from the EU-SILC (2003-2010) and examines how the relationship between social class and self-reported health and chronic conditions varies across 23 countries, which are split into five welfare regimes (Nordic, Anglo-Saxon, Eastern, Southern, and Continental). Our analysis finds that health across all classes was only worse in Eastern Europe (compared with the Nordic countries). In contrast, we find evidence that the social class gradient in both measures of health was significantly wider in the Anglo-Saxon and Southern regimes. We suggest that this evidence supports the notion that welfare regimes continue to explain differences in health according to social class location. We therefore argue that although downward pressures from globalization and neoliberalism have blurred welfare regime typologies, the Nordic model may continue to have an important mediating effect on class-based inequalities in health.

Critical exponents of the 3D antiferromagnetic three-state Potts model using the coherent-anomaly method

NASA Astrophysics Data System (ADS)

Kolesik, Miroslav; Suzuki, Masuo

1995-02-01

The antiferromagnetic three-state Potts model on the simple-cubic lattice is studied using the coherent-anomaly method (CAM). The CAM analysis provides the estimates for the critical exponents which indicate the XY universality class, namely α = -0.011, β = 0.351, γ = 1.309 and δ = 4.73. This observation corroborates the results of the recent Monte Carlo simulations, and disagrees with the proposal of a new universality class.

Confirming the Structural Validity of the My Class Inventory -- Short Form Revised

ERIC Educational Resources Information Center

Mariani, Melissa; Villares, Elizabeth; Sink, Christopher A.; Colvin, Kimberly; Kuba, Summer Perhay

2015-01-01

Researchers analyzed data collected from elementary school students (N = 893) to further establish the psychometric soundness of the My Class Inventory--Short Form Revised (MCI-SFR). A confirmatory factor analysis was conducted resulting in a good fit for a four-factor model, which corresponds to the instrument's four scales (Cohesion,…

Differential gene expression detection and sample classification using penalized linear regression models.

PubMed

Wu, Baolin

2006-02-15

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Clustering of Genetically Defined Allele Classes in the Caenorhabditis elegans DAF-2 Insulin/IGF-1 Receptor

PubMed Central

Patel, Dhaval S.; Garza-Garcia, Acely; Nanji, Manoj; McElwee, Joshua J.; Ackerman, Daniel; Driscoll, Paul C.; Gems, David

2008-01-01

The DAF-2 insulin/IGF-1 receptor regulates development, metabolism, and aging in the nematode Caenorhabditis elegans. However, complex differences among daf-2 alleles complicate analysis of this gene. We have employed epistasis analysis, transcript profile analysis, mutant sequence analysis, and homology modeling of mutant receptors to understand this complexity. We define an allelic series of nonconditional daf-2 mutants, including nonsense and deletion alleles, and a putative null allele, m65. The most severe daf-2 alleles show incomplete suppression by daf-18(0) and daf-16(0) and have a range of effects on early development. Among weaker daf-2 alleles there exist distinct mutant classes that differ in epistatic interactions with mutations in other genes. Mutant sequence analysis (including 11 newly sequenced alleles) reveals that class 1 mutant lesions lie only in certain extracellular regions of the receptor, while class 2 (pleiotropic) and nonconditional missense mutants have lesions only in the ligand-binding pocket of the receptor ectodomain or the tyrosine kinase domain. Effects of equivalent mutations on the human insulin receptor suggest an altered balance of intracellular signaling in class 2 alleles. These studies consolidate and extend our understanding of the complex genetics of daf-2 and its underlying molecular biology. PMID:18245374

Latent class analysis of early developmental trajectory in baby siblings of children with autism.

PubMed

Landa, Rebecca J; Gross, Alden L; Stuart, Elizabeth A; Bauman, Margaret

2012-09-01

Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Sibs-A (N = 204) were assessed with the Mullen Scales of Early Learning from age 6 to 36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (N = 52); non-ASD social/communication delay (broader autism phenotype; BAP; N = 31); and unaffected (N = 121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.

Development of two socioeconomic indices for Saudi Arabia.

PubMed

AlOmar, Reem S; Parslow, Roger C; Law, Graham R

2018-06-26

Health and socioeconomic status (SES) are linked in studies worldwide. Measures of SES exist for many countries, however not for Saudi Arabia (SA). We describe two indices of area-based SES for SA. Routine census data has been used to construct two indices of SES at the geographically-delimited administrative region of Governorates in SA (n = 118). The data used included indicators of educational status, employment status, car and material ownership. A continuous measure of SES was constructed using exploratory factor analysis (EFA) and a categorical measure of SES using latent class analysis (LCA). Both indices were mapped by Governorates. The EFA identified three factors: The first explained 51.58% of the common variance within the interrelated factors, the second 15.14%, and the third 14.26%. These proportions were used in the formulation of the standard index. The scores were fixed to range from 100 for the affluent Governorate and 0 for the deprived. The LCA found a 4 class model as the best model fit. Class 1 was termed "affluent" and included 11.01% of Governorates, class 2 "upper middle class" (44.91%), class 3 "lower middle class" (33.05%) and class 4 "deprived" (11.01%). The populated urbanised Governorates were found to be the most affluent whereas the smaller rural Governorates were the most deprived. This is the first description of measures of SES in SA at a geographical level. Two measures have been successfully constructed and mapped. The maps show similar patterns suggesting validity. Both indices support the common perception of SES in SA.

Anxiety, depression, and the suicidal spectrum: a latent class analysis of overlapping and distinctive features.

PubMed

Podlogar, Matthew C; Rogers, Megan L; Stanley, Ian H; Hom, Melanie A; Chiurliza, Bruno; Joiner, Thomas E

2017-03-20

Anxiety and depression diagnoses are associated with suicidal thoughts and behaviours. However, a categorical understanding of these associations limits insight into identifying dimensional mechanisms of suicide risk. This study investigated anxious and depressive features through a lens of suicide risk, independent of diagnosis. Latent class analysis of 97 depression, anxiety, and suicidality-related items among 616 psychiatric outpatients indicated a 3-class solution, specifically: (1) a higher suicide-risk class uniquely differentiated from both other classes by high reported levels of depression and anxious arousal; (2) a lower suicide-risk class that reported levels of anxiety sensitivity and generalised worry comparable to Class 1, but lower levels of depression and anxious arousal; and (3) a low to non-suicidal class that reported relatively low levels across all depression and anxiety measures. Discriminants of the higher suicide-risk class included borderline personality disorder; report of worthlessness, crying, and sadness; higher levels of anxious arousal and negative affect; and lower levels of positive affect. Depression and anxiety diagnoses were not discriminant between higher and lower suicide risk classes. This transdiagnostic and dimensional approach to understanding the suicidal spectrum contrasts with treating it as a depressive symptom, and illustrates the advantages of a tripartite model for conceptualising suicide risk.

The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

PubMed

Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

2015-02-15

Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.

Ranges of Applicability for the Continuum-beam Model in the Constitutive Analysis of Carbon Nanotubes: Nanotubes or Nano-beams?

NASA Technical Reports Server (NTRS)

Harik, Vasyl Michael; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

Ranges of validity for the continuum-beam model, the length-scale effects and continuum assumptions are analyzed in the framework of scaling analysis of NT structure. Two coupled criteria for the applicability of the continuum model are presented. Scaling analysis of NT buckling and geometric parameters (e.g., diameter and length) is carried out to determine the key non-dimensional parameters that control the buckling strains and modes of NT buckling. A model applicability map, which represents two classes of NTs, is constructed in the space of non-dimensional parameters. In an analogy with continuum mechanics, a mechanical law of geometric similitude is presented for two classes of beam-like NTs having different geometries. Expressions for the critical buckling loads and strains are tailored for the distinct groups of NTs and compared with the data provided by the molecular dynamics simulations. Implications for molecular dynamics simulations and the NT-based scanning probes are discussed.

State-space reduction and equivalence class sampling for a molecular self-assembly model.

PubMed

Packwood, Daniel M; Han, Patrick; Hitosugi, Taro

2016-07-01

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

AVO Analysis of a Shallow Gas Accumulation in the Marmara Sea

NASA Astrophysics Data System (ADS)

Er, M.; Dondurur, D.; Çifçi, G.

2012-04-01

In recent years, Amplitude versus Offset-AVO analysis is widely used in determination and classification of gas anomalies from wide-offset seismic data. Bright spots which are among the significant factors in determining the hydrocarbon accumulations, can also be determined sucessfully using AVO analysis. A bright spot anomaly were identified on the multi-channel seismic data collected by R/V K. Piri Reis research vessel in the Marmara Sea in 2008. On prestack seismic data, the associated AVO anomalies are clearly identified on the supergathers. Near- and far-offset stack sections are plotted to show the amplitudes changes at different offsets and the bright amplitudes were observed on the far-offset stack. AVO analysis was applied to the observed bright spot anomaly following the standart data processing steps. The analysis includes the preparation of Intercept, Gradient and Fluid Factor sections of AVO attribues. Top and base boundaries of gas bearing sediment were shown by intercept - gradient crossplot method. 1D modelling was also performed to show AVO classes and models were compared with the analysis results. It is interpreted that the bright spot anomaly arises from a shallow gas accumulation. In addition, the gas saturation from P-wave velocity was also estimated by the analysis. AVO analysis indicated Class 3 and Class 4 AVO anomalies observed on the bright spot anomaly.

Homology modeling of a Class A GPCR in the inactive conformation: A quantitative analysis of the correlation between model/template sequence identity and model accuracy.

PubMed

Costanzi, Stefano; Skorski, Matthew; Deplano, Alessandro; Habermehl, Brett; Mendoza, Mary; Wang, Keyun; Biederman, Michelle; Dawson, Jessica; Gao, Jia

2016-11-01

With the present work we quantitatively studied the modellability of the inactive state of Class A G protein-coupled receptors (GPCRs). Specifically, we constructed models of one of the Class A GPCRs for which structures solved in the inactive state are available, namely the β 2 AR, using as templates each of the other class members for which structures solved in the inactive state are also available. Our results showed a detectable linear correlation between model accuracy and model/template sequence identity. This suggests that the likely accuracy of the homology models that can be built for a given receptor can be generally forecasted on the basis of the available templates. We also probed whether sequence alignments that allow for the presence of gaps within the transmembrane domains to account for structural irregularities afford better models than the classical alignment procedures that do not allow for the presence of gaps within such domains. As our results indicated, although the overall differences are very subtle, the inclusion of internal gaps within the transmembrane domains has a noticeable a beneficial effect on the local structural accuracy of the domain in question. Copyright © 2016 Elsevier Inc. All rights reserved.

Building gene expression profile classifiers with a simple and efficient rejection option in R.

PubMed

Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco; Savino, Alessandro; Hafeezurrehman, Hafeez

2011-01-01

The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be available.

Self-tuning regulators for multicyclic control of helicopter vibration

NASA Technical Reports Server (NTRS)

Johnson, W.

1982-01-01

A class of algorithms for the multicyclic control of helicopter vibration and loads is derived and discussed. This class is characterized by a linear, quasi-static, frequency-domain model of the helicopter response to control; identification of the helicopter model by least-squared-error or Kalman filter methods; and a minimum variance or quadratic performance function controller. Previous research on such controllers is reviewed. The derivations and discussions cover the helicopter model; the identification problem, including both off-line and on-line (recursive) algorithms; the control problem, including both open-loop and closed-loop feedback; and the various regulator configurations possible within this class. Conclusions from analysis and numerical simulations of the regulators provide guidance in the design and selection of algorithms for further development, including wind tunnel and flight tests.

Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.

PubMed

Wilson, Machelle D; Sethi, Sunjay; Lein, Pamela J; Keil, Kimberly P

2017-03-01

The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines. A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference. Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely. Copyright © 2017 Elsevier B.V. All rights reserved.

Sensitivity analysis as an aid in modelling and control of (poorly-defined) ecological systems. [closed ecological systems

NASA Technical Reports Server (NTRS)

Hornberger, G. M.; Rastetter, E. B.

1982-01-01

A literature review of the use of sensitivity analyses in modelling nonlinear, ill-defined systems, such as ecological interactions is presented. Discussions of previous work, and a proposed scheme for generalized sensitivity analysis applicable to ill-defined systems are included. This scheme considers classes of mathematical models, problem-defining behavior, analysis procedures (especially the use of Monte-Carlo methods), sensitivity ranking of parameters, and extension to control system design.

Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms.

PubMed

Chen, Lei; Liu, Tao; Zhao, Xian

2018-06-01

The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. Copyright © 2017 Elsevier B.V. All rights reserved.

Spectrum of classes of point emitters of electromagnetic wave fields.

PubMed

Castañeda, Román

2016-09-01

The spectrum of classes of point emitters has been introduced as a numerical tool suitable for the design, analysis, and synthesis of non-paraxial optical fields in arbitrary states of spatial coherence. In this paper, the polarization state of planar electromagnetic wave fields is included in the spectrum of classes, thus increasing its modeling capabilities. In this context, optical processing is realized as a filtering on the spectrum of classes of point emitters, performed by the complex degree of spatial coherence and the two-point correlation of polarization, which could be implemented dynamically by using programmable optical devices.

Trail impacts in Sagarmatha (Mt. Everest) National Park, Nepal: a logistic regression analysis.

PubMed

Nepal, S K

2003-09-01

A trail study was conducted in the Sagarmatha (Mt. Everest) National Park, Nepal, during 1997-1998. Based on that study, this paper examines the spatial variability of trail conditions and analyzes factors that influence trail conditions. Logistic regression (multinomial logit model) is applied to examine the influence of use and environmental factors on trail conditions. The assessment of trail conditions is based on a four-class rating system: (class I, very little damaged; class II, moderately damaged, class III, heavily damaged; and class IV, severely damaged). Wald statistics and a model classification table have been used for data interpretation. Results indicate that altitude, trail gradient, hazard potential, and vegetation type are positively associated with trail condition. Trails are more degraded at higher altitude, on steep gradients, in areas with natural hazard potential, and within shrub/grassland zones. Strong correlations between high levels of trail degradation and higher frequencies of visitors and lodges were found. A detailed analysis of environmental and use factors could provide valuable information to park managers in their decisions about trail design, layout and maintenance, and efficient and effective visitor management strategies. Comparable studies on high alpine environments are needed to predict precisely the effects of topographic and climatic extremes. More refined approaches and experimental methods are necessary to control the effects of environmental factors.

A latent class analysis of substance use and culture among gay, bisexual and other men who have sex with men.

PubMed

Card, Kiffer G; Armstrong, Heather L; Carter, Allison; Cui, Zishan; Wang, Lu; Zhu, Julia; Lachowsky, Nathan J; Moore, David M; Hogg, Robert S; Roth, Eric A

2018-03-28

Assessments of gay and bisexual men's substance use often obscures salient sociocultural and identity-related experiences related to how they use drugs. Latent class analysis was used to examine how patterns of substance use represent the social, economic and identity-related experiences of this population. Participants were sexually active gay and bisexual men (including other men who have sex with men), aged ≥ 16 years, living in Metro Vancouver (n = 774). LCA indicators included all substances used in the past six months self-reported by more than 30 men. Model selection was made with consideration to model parsimony, interpretability and optimisation of statistical criteria. Multinomial regression identified factors associated with class membership. A six-class solution was identified representing: 'assorted drug use' (4.5%); 'club drug use' (9.5%); 'street drug use' (12.1%); 'sex drug use' (11.4%); 'conventional drug use' (i.e. tobacco, alcohol, marijuana; 25.9%); and 'limited drug use' (36.7%). Factors associated with class membership included age, sexual orientation, annual income, occupation, income from drug sales, housing stability, group sex event participation, gay bars/clubs attendance, sensation seeking and escape motivation. These results highlight the need for programmes and policies that seek to lessen social disparities and account for social distinctions among this population.

Using latent class analysis to identify academic and behavioral risk status in elementary students.

PubMed

King, Kathleen R; Lembke, Erica S; Reinke, Wendy M

2016-03-01

Identifying classes of children on the basis of academic and behavior risk may have important implications for the allocation of intervention resources within Response to Intervention (RTI) and Multi-Tiered System of Support (MTSS) models. Latent class analysis (LCA) was conducted with a sample of 517 third grade students. Fall screening scores in the areas of reading, mathematics, and behavior were used as indicators of success on an end of year statewide achievement test. Results identified 3 subclasses of children, including a class with minimal academic and behavioral concerns (Tier 1; 32% of the sample), a class at-risk for academic problems and somewhat at-risk for behavior problems (Tier 2; 37% of the sample), and a class with significant academic and behavior problems (Tier 3; 31%). Each class was predictive of end of year performance on the statewide achievement test, with the Tier 1 class performing significantly higher on the test than the Tier 2 class, which in turn scored significantly higher than the Tier 3 class. The results of this study indicated that distinct classes of children can be determined through brief screening measures and are predictive of later academic success. Further implications are discussed for prevention and intervention for students at risk for academic failure and behavior problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The role of charge and multiple faces of the CD8 alpha/alpha homodimer in binding to major histocompatibility complex class I molecules: support for a bivalent model.

PubMed

Giblin, P A; Leahy, D J; Mennone, J; Kavathas, P B

1994-03-01

The CD8 dimer interacts with the alpha 3 domain of major histocompatibility complex class I molecules through two immunoglobulin variable-like domains. In this study a crystal structure-informed mutational analysis has been performed to identify amino acids in the CD8 alpha/alpha homodimer that are likely to be involved in binding to class I. Several key residues are situated on the top face of the dimer within loops analogous to the complementarity-determining regions (CDRs) of immunoglobulin. In addition, other important amino acids are located in the A and B beta-strands on the sides of the dimer. The potential involvement of amino acids on both the top and the side faces of the molecule is consistent with a bivalent model for the interaction between a single CD8 alpha/alpha homodimer and two class I molecules and may have important implications for signal transduction in class I-expressing cells. This study also demonstrates a role for the positive surface potential of CD8 in class I binding and complements previous work demonstrating the importance of a negatively charged loop on the alpha 3 domain of class I for CD8 alpha/alpha-class I interaction. We propose a model whereby residues located on the CDR-like loops of the CD8 homodimer interact with the alpha 3 domain of MHC class I while amino acids on the side of the molecule containing the A and B beta-strands contact the alpha 2 domain of class I.

The role of population inertia in predicting the outcome of stage-structured biological invasions.

PubMed

Guiver, Chris; Dreiwi, Hanan; Filannino, Donna-Maria; Hodgson, Dave; Lloyd, Stephanie; Townley, Stuart

2015-07-01

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour-phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. Copyright © 2015. Published by Elsevier Inc.

American Guild of Musical Artists: A Case for System Development, Data Modeling, and Analytics

ERIC Educational Resources Information Center

Harris, Ranida; Wedel, Thomas

2017-01-01

This article presents a case scenario that may be used in system analysis and design, database management, and business analytics classes. The case document includes realistic, detailed information on the operations at the American Guild of Musical Artists (AGMA). Examples of assignments for each class and suggested reading are presented. In each…

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

ERIC Educational Resources Information Center

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background: Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods: Sibs-A (N = 204) were assessed…

How Students Learn from Multiple Contexts and Definitions: Proper Time as a Coordination Class

ERIC Educational Resources Information Center

Levrini, Olivia; diSessa, Andrea A.

2008-01-01

This article provides an empirical analysis of a single classroom episode in which students reveal difficulties with the concept of proper time in special relativity but slowly make progress in improving their understanding. The theoretical framework used is "coordination class theory," which is an evolving model of concepts and conceptual change.…

Adversary modeling: an analysis of criminal activities analogous to potential threats to nuclear safeguard systems

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

Heineke, J.M.

1978-12-20

This study examines and analyzes several classes of incidents in which decision makers are confronted with adversaries. The classes are analogous to adversaries in a material control system in a nuclear facility. Both internal threats (bank frauds and embezzlements) and external threats (aircraft hijackings and hostage-type terrorist events were analyzed. (DLC)

Family Success and Sexual Equality: The Limits of the Dual-Career Family Model.

ERIC Educational Resources Information Center

Benenson, Harold

Dual-career analysis is misleading as a guide to actual developments in wives' employment and family economic patterns at different levels of the class system. Weaknesses can be examined through seven empirical propositions concerning the determinants of family employment patterns at various class levels. (1) Despite recent gains, married women in…

A benefit-cost analysis of ten tree species in Modesto, California, U.S.A

Treesearch

E.G. McPherson

2003-01-01

Tree work records for ten species were analyzed to estimate average annual management costs by dbh class for six activity areas. Average annual benefits were calculated by dbh class for each species with computer modeling. Average annual net benefits per tree were greatest for London plane (Platanus acerifolia) ($178.57), hackberry (...

Dietary and exercise change following acute cardiac syndrome onset: A latent class growth modelling analysis.

PubMed

Bennett, Paul; Gruszczynska, Ewa; Marke, Victoria

2016-10-01

The present study aim determine sub-group trajectories of change on measures of diet and exercise following acute coronary syndrome. 150 participants were assessed in hospital, 1 month and 6 months subsequently on measures including physical activity, diet, illness beliefs, coping and mood. Change trajectories were measured using latent class growth modelling. Multinomial logistic regression was used to predict class membership. These analyses revealed changes in exercise were confined to a sub-group of participants already reporting relatively high exercise levels; those eating less healthily evidenced modest dietary improvements. Coping, gender, depression and perceived control predicted group membership to a modest degree. © The Author(s) 2015.

Bayesian Analysis Of HMI Solar Image Observables And Comparison To TSI Variations And MWO Image Observables

NASA Astrophysics Data System (ADS)

Parker, D. G.; Ulrich, R. K.; Beck, J.

2014-12-01

We have previously applied the Bayesian automatic classification system AutoClass to solar magnetogram and intensity images from the 150 Foot Solar Tower at Mount Wilson to identify classes of solar surface features associated with variations in total solar irradiance (TSI) and, using those identifications, modeled TSI time series with improved accuracy (r > 0.96). (Ulrich, et al, 2010) AutoClass identifies classes by a two-step process in which it: (1) finds, without human supervision, a set of class definitions based on specified attributes of a sample of the image data pixels, such as magnetic field and intensity in the case of MWO images, and (2) applies the class definitions thus found to new data sets to identify automatically in them the classes found in the sample set. HMI high resolution images capture four observables-magnetic field, continuum intensity, line depth and line width-in contrast to MWO's two observables-magnetic field and intensity. In this study, we apply AutoClass to the HMI observables for images from May, 2010 to June, 2014 to identify solar surface feature classes. We use contemporaneous TSI measurements to determine whether and how variations in the HMI classes are related to TSI variations and compare the characteristic statistics of the HMI classes to those found from MWO images. We also attempt to derive scale factors between the HMI and MWO magnetic and intensity observables. The ability to categorize automatically surface features in the HMI images holds out the promise of consistent, relatively quick and manageable analysis of the large quantity of data available in these images. Given that the classes found in MWO images using AutoClass have been found to improve modeling of TSI, application of AutoClass to the more complex HMI images should enhance understanding of the physical processes at work in solar surface features and their implications for the solar-terrestrial environment. Ulrich, R.K., Parker, D, Bertello, L. and Boyden, J. 2010, Solar Phys. , 261 , 11.

Network-based stochastic semisupervised learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Comparing Factor, Class, and Mixture Models of Cannabis Initiation and DSM Cannabis Use Disorder Criteria, Including Craving, in the Brisbane Longitudinal Twin Study

PubMed Central

Kubarych, Thomas S.; Kendler, Kenneth S.; Aggen, Steven H.; Estabrook, Ryne; Edwards, Alexis C.; Clark, Shaunna L.; Martin, Nicholas G.; Hickie, Ian B.; Neale, Michael C.; Gillespie, Nathan A.

2014-01-01

Accumulating evidence suggests that the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying factor. However, it remains possible that models with additional factors, or latent class models or hybrid models, may better explain the data. Using structured interviews, 626 adult male and female twins provided complete data on symptoms of cannabis abuse and dependence, plus a craving criterion. We compared latent factor analysis, latent class analysis, and factor mixture modeling using normal theory marginal maximum likelihood for ordinal data. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-IV criteria and determine whether DSM-5 craving loads onto a general factor. When compared with latent class and mixture models, factor models provided a better fit to the data. When conditioned on initiation and cannabis use, the association between criteria for abuse, dependence, withdrawal, and craving were best explained by two correlated latent factors for males and females: a general risk factor to CUD and a factor capturing the symptoms of social and occupational impairment as a consequence of frequent use. Secondary analyses revealed a modest increase in the prevalence of DSM-5 CUD compared with DSM-IV cannabis abuse or dependence. It is concluded that, in addition to a general factor with loadings on cannabis use and symptoms of abuse, dependence, withdrawal, and craving, a second clinically relevant factor defined by features of social and occupational impairment was also found for frequent cannabis use. PMID:24588857

Complex Networks/Foundations of Information Systems

DTIC Science & Technology

2013-03-06

the benefit of feedback or dynamic correlations in coding and protocol. Using Renyi correlation analysis and entropy to model this wider class of...dynamic heterogeneous conditions. Lizhong Zheng, MIT Renyi Channel Correlation Analysis (connected to geometric curvature) Network Channel

40 CFR 93.152 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... official charged with direct responsibility for management of an area designated as Class I under the Act.... Areawide air quality modeling analysis means an assessment on a scale that includes the entire nonattainment or maintenance area using an air quality dispersion model or photochemical grid model to determine...

HIV-related sexual risk behavior among African American adolescent girls.

PubMed

Danielson, Carla Kmett; Walsh, Kate; McCauley, Jenna; Ruggiero, Kenneth J; Brown, Jennifer L; Sales, Jessica M; Rose, Eve; Wingood, Gina M; Diclemente, Ralph J

2014-05-01

Latent class analysis (LCA) is a useful statistical tool that can be used to enhance understanding of how various patterns of combined sexual behavior risk factors may confer differential levels of HIV infection risk and to identify subtypes among African American adolescent girls. Data for this analysis is derived from baseline assessments completed prior to randomization in an HIV prevention trial. Participants were African American girls (n=701) aged 14-20 years presenting to sexual health clinics. Girls completed an audio computer-assisted self-interview, which assessed a range of variables regarding sexual history and current and past sexual behavior. Two latent classes were identified with the probability statistics for the two groups in this model being 0.89 and 0.88, respectively. In the final multivariate model, class 1 (the "higher risk" group; n=331) was distinguished by a higher likelihood of >5 lifetime sexual partners, having sex while high on alcohol/drugs, less frequent condom use, and history of sexually transmitted diseases (STDs), when compared with class 2 (the "lower risk" group; n=370). The derived model correctly classified 85.3% of participants into the two groups and accounted for 71% of the variance in the latent HIV-related sexual behavior risk variable. The higher risk class also had worse scores on all hypothesized correlates (e.g., self-esteem, history of sexual assault or physical abuse) relative to the lower risk class. Sexual health clinics represent a unique point of access for HIV-related sexual risk behavior intervention delivery by capitalizing on contact with adolescent girls when they present for services. Four empirically supported risk factors differentiated higher versus lower HIV risk. Replication of these findings is warranted and may offer an empirical basis for parsimonious screening recommendations for girls presenting for sexual healthcare services.

Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

PubMed

Stumpe, B; Engel, T; Steinweg, B; Marschner, B

2012-04-03

In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

Opioid withdrawal, craving, and use during and after outpatient buprenorphine stabilization and taper: a discrete survival and growth mixture model.

PubMed

Northrup, Thomas F; Stotts, Angela L; Green, Charles; Potter, Jennifer S; Marino, Elise N; Walker, Robrina; Weiss, Roger D; Trivedi, Madhukar

2015-02-01

Most patients relapse to opioids within one month of opioid agonist detoxification, making the antecedents and parallel processes of first use critical for investigation. Craving and withdrawal are often studied in relationship to opioid outcomes, and a novel analytic strategy applied to these two phenomena may indicate targeted intervention strategies. Specifically, this secondary data analysis of the Prescription Opioid Addiction Treatment Study used a discrete-time mixture analysis with time-to-first opioid use (survival) simultaneously predicted by craving and withdrawal growth trajectories. This analysis characterized heterogeneity among prescription opioid-dependent individuals (N=653) into latent classes (i.e., latent class analysis [LCA]) during and after buprenorphine/naloxone stabilization and taper. A 4-latent class solution was selected for overall model fit and clinical parsimony. In order of shortest to longest time-to-first use, the 4 classes were characterized as 1) high craving and withdrawal, 2) intermediate craving and withdrawal, 3) high initial craving with low craving and withdrawal trajectories and 4) a low initial craving with low craving and withdrawal trajectories. Odds ratio calculations showed statistically significant differences in time-to-first use across classes. Generally, participants with lower baseline levels and greater decreases in craving and withdrawal during stabilization combined with slower craving and withdrawal rebound during buprenorphine taper remained opioid-free longer. This exploratory work expanded on the importance of monitoring craving and withdrawal during buprenorphine induction, stabilization, and taper. Future research may allow individually tailored and timely interventions to be developed to extend time-to-first opioid use. Copyright © 2014 Elsevier Ltd. All rights reserved.

Analysis of a novel class of predictive microbial growth models and application to coculture growth.

PubMed

Poschet, F; Vereecken, K M; Geeraerd, A H; Nicolaï, B M; Van Impe, J F

2005-04-15

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.

The alcohol policy environment, enforcement and consumption in the United States.

PubMed

Erickson, Darin J; Lenk, Kathleen M; Toomey, Traci L; Nelson, Toben F; Jones-Webb, Rhonda

2016-01-01

Many studies of alcohol policies examine the presence or absence of a single policy without considering policy strength or enforcement. We developed measures for the strength of 18 policies (from Alcohol Policy Information System) and levels of enforcement of those policies for the 50 US states, and examined their associations with alcohol consumption. We grouped policies into four domains (underage alcohol use, provision of alcohol to underage, alcohol serving, general availability) and used latent class analysis to assign states to one of four classes based on the configuration of policies-weak except serving policies (6 states), average (29 states), strong for underage use (11 states) and strong policies overall (4 states). We surveyed 1082 local enforcement agencies regarding alcohol enforcement across five domains. We used multilevel latent class analysis to assign states to classes in each domain and assigned each state to an overall low (15 states), moderate (19 states) or high (16 states) enforcement group. Consumption outcomes (past month, binge and heavy) came from the Behavioral Risk Factor Surveillance System. Regression models show inverse associations between alcohol consumption and policy class, with past month alcohol consumption at 54% in the weakest policy class and 34% in the strongest. In adjusted models, the strong underage use policy class was consistently associated with lower consumption. Enforcement group did not affect the policy class and consumption associations. Results suggest strong alcohol policies, particularly underage use policies, may help to reduce alcohol consumption and related consequences. [Erickson DJ, Lenk KM, Toomey TL, Nelson TF, Jones-Webb R. The alcohol policy environment, enforcement, and consumption in the United States. Drug Alcohol Rev 2015;●●:●●-●●]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis.

PubMed

Eppig, Joel S; Edmonds, Emily C; Campbell, Laura; Sanderson-Cimino, Mark; Delano-Wood, Lisa; Bondi, Mark W

2017-08-01

Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes. A total of 806 participants diagnosed by means of the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on "robust" normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer's disease CSF biomarkers than LPA-derived normal subjects. Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent "false-positive" diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564-576).

Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis.

PubMed

Lopatka, Martin; Sigman, Michael E; Sjerps, Marjan J; Williams, Mary R; Vivó-Truyols, Gabriel

2015-07-01

Forensic chemical analysis of fire debris addresses the question of whether ignitable liquid residue is present in a sample and, if so, what type. Evidence evaluation regarding this question is complicated by interference from pyrolysis products of the substrate materials present in a fire. A method is developed to derive a set of class-conditional features for the evaluation of such complex samples. The use of a forensic reference collection allows characterization of the variation in complex mixtures of substrate materials and ignitable liquids even when the dominant feature is not specific to an ignitable liquid. Making use of a novel method for data imputation under complex mixing conditions, a distribution is modeled for the variation between pairs of samples containing similar ignitable liquid residues. Examining the covariance of variables within the different classes allows different weights to be placed on features more important in discerning the presence of a particular ignitable liquid residue. Performance of the method is evaluated using a database of total ion spectrum (TIS) measurements of ignitable liquid and fire debris samples. These measurements include 119 nominal masses measured by GC-MS and averaged across a chromatographic profile. Ignitable liquids are labeled using the American Society for Testing and Materials (ASTM) E1618 standard class definitions. Statistical analysis is performed in the class-conditional feature space wherein new forensic traces are represented based on their likeness to known samples contained in a forensic reference collection. The demonstrated method uses forensic reference data as the basis of probabilistic statements concerning the likelihood of the obtained analytical results given the presence of ignitable liquid residue of each of the ASTM classes (including a substrate only class). When prior probabilities of these classes can be assumed, these likelihoods can be connected to class probabilities. In order to compare the performance of this method to previous work, a uniform prior was assumed, resulting in an 81% accuracy for an independent test of 129 real burn samples. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Studying Psychosocial Barriers to Drug Treatment Among Chinese Methamphetamine Users Using A 3-Step Latent Class Analysis.

PubMed

Wang, Jichuan; Kelly, Brian C; Liu, Tieqiao; Hao, Wei

2016-03-01

Given the growth in methamphetamine use in China during the 21st century, we assessed perceived psychosocial barriers to drug treatment among this population. Using a sample of 303 methamphetamine users recruited via Respondent Driven Sampling, we use Latent Class Analysis (LCA) to identify possible distinct latent groups among Chinese methamphetamine users on the basis of their perceptions of psychosocial barriers to drug treatment. After covariates were included to predict latent class membership, the 3-step modeling approach was applied. Our findings indicate that the Chinese methamphetamine using population was heterogeneous on perceptions of drug treatment barriers; four distinct latent classes (subpopulations) were identified--Unsupported Deniers, Deniers, Privacy Anxious, and Low Barriers--and individual characteristics shaped the probability of class membership. Efforts to link Chinese methamphetamine users to treatment may require a multi-faceted approach that attends to differing perceptions about impediments to drug treatment. Copyright © 2015. Published by Elsevier Inc.

The Effect of STEM Learning through the Project of Designing Boat Model toward Student STEM Literacy

NASA Astrophysics Data System (ADS)

Tati, T.; Firman, H.; Riandi, R.

2017-09-01

STEM Learning focusses on development of STEM-literate society, the research about implementation of STEM learning to develope students’ STEM literacy is still limited. This study is aimed to examine the effect of implementation STEM learning through the project of designing boat model on students STEM literacy in energy topic. The method of this study was a quasi-experiment with non-randomized pretest-posttest control group design. There were two classes involved, the experiment class used Project Based Learning with STEM approach and control class used Project-Based Learning without STEM approach. A STEM Literacy test instrument was developed to measure students STEM literacy which consists of science literacy, mathematics literacy, and technology-engineering literacy. The analysis showed that there were significant differences on improvement science literacy, mathematics technology-engineering between experiment class and control class with effect size more than 0.8 (large effect). The difference of improvement of STEM literacy between experiment class and control class is caused by the existence of design engineering activity which required students to apply the knowledge from every field of STEM. The challenge that was faced in STEM learning through design engineering activity was how to give the students practice to integrate STEM field in solving the problems. In additional, most of the students gave positive response toward implementation of STEM learning through design boat model project.

A Systems Analysis and Design Case Study for a Business Modeling Learning Experience for a Capstone CIS/IS Systems Development Class

ERIC Educational Resources Information Center

Russell, Jack; Russell, Barbara

2015-01-01

The goal is to provide a robust and challenging problem statement for a capstone, advanced systems analysis and design course for CIS/MIS/CS majors. In addition to the problem narrative, a representative solution for much of the business modeling deliverables is presented using the UML paradigm. A structured analysis deliverable will be the topic…

Customizing G Protein-coupled receptor models for structure-based virtual screening.

PubMed

de Graaf, Chris; Rognan, Didier

2009-01-01

This review will focus on the construction, refinement, and validation of G Protein-coupled receptor models for the purpose of structure-based virtual screening. Practical tips and tricks derived from concrete modeling and virtual screening exercises to overcome the problems and pitfalls associated with the different steps of the receptor modeling workflow will be presented. These examples will not only include rhodopsin-like (class A), but also secretine-like (class B), and glutamate-like (class C) receptors. In addition, the review will present a careful comparative analysis of current crystal structures and their implication on homology modeling. The following themes will be discussed: i) the use of experimental anchors in guiding the modeling procedure; ii) amino acid sequence alignments; iii) ligand binding mode accommodation and binding cavity expansion; iv) proline-induced kinks in transmembrane helices; v) binding mode prediction and virtual screening by receptor-ligand interaction fingerprint scoring; vi) extracellular loop modeling; vii) virtual filtering schemes. Finally, an overview of several successful structure-based screening shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands.

Assets as a Socioeconomic Status Index: Categorical Principal Components Analysis vs. Latent Class Analysis.

PubMed

Sartipi, Majid; Nedjat, Saharnaz; Mansournia, Mohammad Ali; Baigi, Vali; Fotouhi, Akbar

2016-11-01

Some variables like Socioeconomic Status (SES) cannot be directly measured, instead, so-called 'latent variables' are measured indirectly through calculating tangible items. There are different methods for measuring latent variables such as data reduction methods e.g. Principal Components Analysis (PCA) and Latent Class Analysis (LCA). The purpose of our study was to measure assets index- as a representative of SES- through two methods of Non-Linear PCA (NLPCA) and LCA, and to compare them for choosing the most appropriate model. This was a cross sectional study in which 1995 respondents filled the questionnaires about their assets in Tehran. The data were analyzed by SPSS 19 (CATPCA command) and SAS 9.2 (PROC LCA command) to estimate their socioeconomic status. The results were compared based on the Intra-class Correlation Coefficient (ICC). The 6 derived classes from LCA based on BIC, were highly consistent with the 6 classes from CATPCA (Categorical PCA) (ICC = 0.87, 95%CI: 0.86 - 0.88). There is no gold standard to measure SES. Therefore, it is not possible to definitely say that a specific method is better than another one. LCA is a complicated method that presents detailed information about latent variables and required one assumption (local independency), while NLPCA is a simple method, which requires more assumptions. Generally, NLPCA seems to be an acceptable method of analysis because of its simplicity and high agreement with LCA.

The association between school exclusion, delinquency and subtypes of cyber- and F2F-victimizations: identifying and predicting risk profiles and subtypes using latent class analysis.

PubMed

Barboza, Gia Elise

2015-01-01

This purpose of this paper is to identify risk profiles of youth who are victimized by on- and offline harassment and to explore the consequences of victimization on school outcomes. Latent class analysis is used to explore the overlap and co-occurrence of different clusters of victims and to examine the relationship between class membership and school exclusion and delinquency. Participants were a random sample of youth between the ages of 12 and 18 selected for inclusion to participate in the 2011 National Crime Victimization Survey: School Supplement. The latent class analysis resulted in four categories of victims: approximately 3.1% of students were highly victimized by both bullying and cyberbullying behaviors; 11.6% of youth were classified as being victims of relational bullying, verbal bullying and cyberbullying; a third class of students were victims of relational bullying, verbal bullying and physical bullying but were not cyberbullied (8%); the fourth and final class, characteristic of the majority of students (77.3%), was comprised of non-victims. The inclusion of covariates to the latent class model indicated that gender, grade and race were significant predictors of at least one of the four victim classes. School delinquency measures were included as distal outcomes to test for both overall and pairwise associations between classes. With one exception, the results were indicative of a significant relationship between school delinquency and the victim subtypes. Implications for these findings are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Do Plants Contain G Protein-Coupled Receptors?1[C][W][OPEN

PubMed Central

Taddese, Bruck; Upton, Graham J.G.; Bailey, Gregory R.; Jordan, Siân R.D.; Abdulla, Nuradin Y.; Reeves, Philip J.; Reynolds, Christopher A.

2014-01-01

Whether G protein-coupled receptors (GPCRs) exist in plants is a fundamental biological question. Interest in deorphanizing new GPCRs arises because of their importance in signaling. Within plants, this is controversial, as genome analysis has identified 56 putative GPCRs, including G protein-coupled receptor1 (GCR1), which is reportedly a remote homolog to class A, B, and E GPCRs. Of these, GCR2 is not a GPCR; more recently, it has been proposed that none are, not even GCR1. We have addressed this disparity between genome analysis and biological evidence through a structural bioinformatics study, involving fold recognition methods, from which only GCR1 emerges as a strong candidate. To further probe GCR1, we have developed a novel helix-alignment method, which has been benchmarked against the class A-class B-class F GPCR alignments. In addition, we have presented a mutually consistent set of alignments of GCR1 homologs to class A, class B, and class F GPCRs and shown that GCR1 is closer to class A and/or class B GPCRs than class A, class B, or class F GPCRs are to each other. To further probe GCR1, we have aligned transmembrane helix 3 of GCR1 to each of the six GPCR classes. Variability comparisons provide additional evidence that GCR1 homologs have the GPCR fold. From the alignments and a GCR1 comparative model, we have identified motifs that are common to GCR1, class A, B, and E GPCRs. We discuss the possibilities that emerge from this controversial evidence that GCR1 has a GPCR fold. PMID:24246381

Common Mental Disorders among Occupational Groups: Contributions of the Latent Class Model

PubMed Central

Martins Carvalho, Fernando; de Araújo, Tânia Maria

2016-01-01

Background. The Self-Reporting Questionnaire (SRQ-20) is widely used for evaluating common mental disorders. However, few studies have evaluated the SRQ-20 measurements performance in occupational groups. This study aimed to describe manifestation patterns of common mental disorders symptoms among workers populations, by using latent class analysis. Methods. Data derived from 9,959 Brazilian workers, obtained from four cross-sectional studies that used similar methodology, among groups of informal workers, teachers, healthcare workers, and urban workers. Common mental disorders were measured by using SRQ-20. Latent class analysis was performed on each database separately. Results. Three classes of symptoms were confirmed in the occupational categories investigated. In all studies, class I met better criteria for suspicion of common mental disorders. Class II discriminated workers with intermediate probability of answers to the items belonging to anxiety, sadness, and energy decrease that configure common mental disorders. Class III was composed of subgroups of workers with low probability to respond positively to questions for screening common mental disorders. Conclusions. Three patterns of symptoms of common mental disorders were identified in the occupational groups investigated, ranging from distinctive features to low probabilities of occurrence. The SRQ-20 measurements showed stability in capturing nonpsychotic symptoms. PMID:27630999

Harvesting, predation and competition effects on a red coral population

NASA Astrophysics Data System (ADS)

Abbiati, M.; Buffoni, G.; Caforio, G.; Di Cola, G.; Santangelo, G.

A Corallium rubrum population, dwelling in the Ligurian Sea, has been under observation since 1987. Biometric descriptors of colonies (base diameter, weight, number of polyps, number of growth rings) have been recorded and correlated. The population size structure was obtained by distributing the colonies into diameter classes, each size class representing the average annual increment of diameter growth. The population was divided into ten classes, including a recruitment class. This size structure showed a fairly regular trend in the first four classes. The irregularity of survival in the older classes agreed with field observations on harvesting and predation. Demographic parameters such as survival, growth plasticity and natality coefficients were estimated from the experimental data. On this basis a discrete nonlinear model was implemented. The model is based on a kind of density-dependent Leslie matrix, where the feedback term only occurs in survival of the first class; the recruitment function is assumed to be dependent on the total biomass and related to inhibiting effects due to competitive interactions. Stability analysis was applied to steady-state solutions. Numerical simulations of population evolution were carried out under different conditions. The dynamics of settlement and the effects of disturbances such as harvesting, predation and environmental variability were studied.

Curve of Spee and its relationship to vertical eruption of teeth among different malocclusion groups.

PubMed

Veli, Ilknur; Ozturk, Mehmet Ali; Uysal, Tancan

2015-03-01

Our objectives were to assess the depth of the curve of Spee (COS) in different malocclusion groups, to relate this to the eruption of anterior or posterior teeth quantitatively, and to determine whether the depth of the COS is affected by the vertical eruption of anterior or posterior teeth. Two hundred conventional lateral cephalograms and 3-dimensional models of untreated patients (70 boys, mean age: 16.4 ± 1.4 years; 130 young women, mean age: 18.1 ± 1.8 years) were included and assigned to 4 malocclusion groups as Class I, Class II Division 1, Class II Division 2, and Class III. The depth of the COS, overjet, and overbite were measured on 3-dimensional models. The perpendicular distance between the incisal tip of the mandibular central incisor (L1-MP), the deepest point of the COS (S-MP), and the distobuccal cusp tip of the mandibular second molar (L7-MP) to the mandibular plane were calculated and proportioned with each other. The Pearson correlation coefficient was calculated, and multiple linear regression analysis was carried out. Also, multivariate analysis of variance was performed at the P <0.05 level. The mesiobuccal cusp of the first molar was the deepest part of the COS in all groups, with a maximum depth of 2.44 ± 0.73 mm in the Class II Division 1 subjects and a minimum depth of 1.76 ± 0.94 in the Class III subjects. The depth of the COS changed as follows: Class II Division 1 > Class II Division 2 > Class I > Class III malocclusion groups. Statistically significant positive correlations were found between the depth of the COS and L1-MP/S-MP (r = 0.541) and L7-MP/S-MP (r = 0.269) in the Class I and Class III subjects, and between the depth of the COS and overjet (r = 0.483) and L7-MP/S-MP (r = 0.289) in the Class II Division 1 subjects. All variables except overjet had positive correlations with the depth of the COS in Class II Division 2 subjects. The multivariate analysis of variance showed statistically significant differences in overjet, overbite, L1-MP/S-MP, L7-MP/S-MP, and the depth of the COS (P <0.001) among the groups. Although the overjet differed, vertical eruption of the anterior teeth did not differ among the different malocclusion groups and had a significant contribution to the depth of the COS in subjects with Class I and Class III malocclusions. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Posttraumatic Stress Disorder: Diagnostic Data Analysis by Data Mining Methodology

PubMed Central

Marinić, Igor; Supek, Fran; Kovačić, Zrnka; Rukavina, Lea; Jendričko, Tihana; Kozarić-Kovačić, Dragica

2007-01-01

Aim To use data mining methods in assessing diagnostic symptoms in posttraumatic stress disorder (PTSD) Methods The study included 102 inpatients: 51 with a diagnosis of PTSD and 51 with psychiatric diagnoses other than PTSD. Several models for predicting diagnosis were built using the random forest classifier, one of the intelligent data analysis methods. The first prediction model was based on a structured psychiatric interview, the second on psychiatric scales (Clinician-administered PTSD Scale – CAPS, Positive and Negative Syndrome Scale – PANSS, Hamilton Anxiety Scale – HAMA, and Hamilton Depression Scale – HAMD), and the third on combined data from both sources. Additional models placing more weight on one of the classes (PTSD or non-PTSD) were trained, and prototypes representing subgroups in the classes constructed. Results The first model was the most relevant for distinguishing PTSD diagnosis from comorbid diagnoses such as neurotic, stress-related, and somatoform disorders. The second model pointed out the scores obtained on the Clinician-administered PTSD Scale (CAPS) and additional Positive and Negative Syndrome Scale (PANSS) scales, together with comorbid diagnoses of neurotic, stress-related, and somatoform disorders as most relevant. In the third model, psychiatric scales and the same group of comorbid diagnoses were found to be most relevant. Specialized models placing more weight on either the PTSD or non-PTSD class were able to better predict their targeted diagnoses at some expense of overall accuracy. Class subgroup prototypes mainly differed in values achieved on psychiatric scales and frequency of comorbid diagnoses. Conclusion Our work demonstrated the applicability of data mining methods for the analysis of structured psychiatric data for PTSD. In all models, the group of comorbid diagnoses, including neurotic, stress-related, and somatoform disorders, surfaced as important. The important attributes of the data, based on the structured psychiatric interview, were the current symptoms and conditions such as presence and degree of disability, hospitalizations, and duration of military service during the war, while CAPS total scores, symptoms of increased arousal, and PANSS additional criteria scores were indicated as relevant from the psychiatric symptom scales. PMID:17436383

Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study.

PubMed

Zammit, Andrea R; Hall, Charles B; Lipton, Richard B; Katz, Mindy J; Muniz-Terrera, Graciela

2018-05-01

The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup's characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer's disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511-523).

Econometric analysis of the factors influencing forest acreage trends in the southeast.

Treesearch

Ralph J. Alig

1986-01-01

Econometric models of changes in land use acreages in the Southeast by physiographic region have been developed by pooling cross-section and time series data. Separate acreage equations have been estimated for the three major private forestland owner classes and the three major classes of nonforest land use. Observations were drawn at three or four different points in...

Predicting Homework Time Management at the Secondary School Level: A Multilevel Analysis

ERIC Educational Resources Information Center

Xu, Jianzhong

2010-01-01

The purpose of this study is to test empirical models of variables posited to predict homework time management at the secondary school level. Student- and class-level predictors of homework time management were analyzed in a survey of 1895 students from 111 classes. Most of the variance in homework time management occurred at the student level,…

Aligning Coordination Class Theory with a New Context: Applying a Theory of Individual Learning to Group Learning

ERIC Educational Resources Information Center

Barth-Cohen, Lauren A.; Wittmann, Michael C.

2017-01-01

This article presents an empirical analysis of conceptual difficulties encountered and ways students made progress in learning at both individual and group levels in a classroom environment in which the students used an embodied modeling activity to make sense of a specific scientific scenario. The theoretical framework, coordination class theory,…

Science Learning: A Path Analysis of Its Links with Reading Comprehension, Question-Asking in Class and Science Achievement

ERIC Educational Resources Information Center

Cano, Francisco; García, Ángela; Berbén, A. B. G.; Justicia, Fernando

2014-01-01

The purpose of this research was to build and test a conceptual model of the complex interrelationships between students' learning in science (learning approaches and self-regulation), their reading comprehension, question-asking in class and science achievement. These variables were measured by means of a test and a series of questionnaires…

Integrating end-to-end threads of control into object-oriented analysis and design

NASA Technical Reports Server (NTRS)

Mccandlish, Janet E.; Macdonald, James R.; Graves, Sara J.

1993-01-01

Current object-oriented analysis and design methodologies fall short in their use of mechanisms for identifying threads of control for the system being developed. The scenarios which typically describe a system are more global than looking at the individual objects and representing their behavior. Unlike conventional methodologies that use data flow and process-dependency diagrams, object-oriented methodologies do not provide a model for representing these global threads end-to-end. Tracing through threads of control is key to ensuring that a system is complete and timing constraints are addressed. The existence of multiple threads of control in a system necessitates a partitioning of the system into processes. This paper describes the application and representation of end-to-end threads of control to the object-oriented analysis and design process using object-oriented constructs. The issue of representation is viewed as a grouping problem, that is, how to group classes/objects at a higher level of abstraction so that the system may be viewed as a whole with both classes/objects and their associated dynamic behavior. Existing object-oriented development methodology techniques are extended by adding design-level constructs termed logical composite classes and process composite classes. Logical composite classes are design-level classes which group classes/objects both logically and by thread of control information. Process composite classes further refine the logical composite class groupings by using process partitioning criteria to produce optimum concurrent execution results. The goal of these design-level constructs is to ultimately provide the basis for a mechanism that can support the creation of process composite classes in an automated way. Using an automated mechanism makes it easier to partition a system into concurrently executing elements that can be run in parallel on multiple processors.

Stability of ARDS subphenotypes over time in two randomised controlled trials.

PubMed

Delucchi, Kevin; Famous, Katie R; Ware, Lorraine B; Parsons, Polly E; Thompson, B Taylor; Calfee, Carolyn S

2018-05-01

Two distinct acute respiratory distress syndrome (ARDS) subphenotypes have been identified using data obtained at time of enrolment in clinical trials; it remains unknown if these subphenotypes are durable over time. To determine the stability of ARDS subphenotypes over time. Secondary analysis of data from two randomised controlled trials in ARDS, the ARMA trial of lung protective ventilation (n=473; patients randomised to low tidal volumes only) and the ALVEOLI trial of low versus high positive end-expiratory pressure (n=549). Latent class analysis (LCA) and latent transition analysis (LTA) were applied to data from day 0 and day 3, independent of clinical outcomes. In ALVEOLI, LCA indicated strong evidence of two ARDS latent classes at days 0 and 3; in ARMA, evidence of two classes was stronger at day 0 than at day 3. The clinical and biological features of these two classes were similar to those in our prior work and were largely stable over time, though class 2 demonstrated evidence of progressive organ failures by day 3, compared with class 1. In both LCA and LTA models, the majority of patients (>94%) stayed in the same class from day 0 to day 3. Clinical outcomes were statistically significantly worse in class 2 than class 1 and were more strongly associated with day 3 class assignment. ARDS subphenotypes are largely stable over the first 3 days of enrolment in two ARDS Network trials, suggesting that subphenotype identification may be feasible in the context of clinical trials. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The effects of rurality on substance use disorder diagnosis: A multiple-groups latent class analysis.

PubMed

Brooks, Billy; McBee, Matthew; Pack, Robert; Alamian, Arsham

2017-05-01

Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities. Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership. Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P value<0.0001). Overall the rural class structure exhibited less diversity in class structure and lower prevalence of SUD in multiple drug categories (e.g. cocaine, hallucinogens, and stimulants). This result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of Three Cognitive Diagnosis Models to ESL Reading and Listening Assessments

ERIC Educational Resources Information Center

Lee, Yong-Won; Sawaki, Yasuyo

2009-01-01

The present study investigated the functioning of three psychometric models for cognitive diagnosis--the general diagnostic model, the fusion model, and latent class analysis--when applied to large-scale English as a second language listening and reading comprehension assessments. Data used in this study were scored item responses and incidence…

The Potential of Growth Mixture Modelling

ERIC Educational Resources Information Center

Muthen, Bengt

2006-01-01

The authors of the paper on growth mixture modelling (GMM) give a description of GMM and related techniques as applied to antisocial behaviour. They bring up the important issue of choice of model within the general framework of mixture modelling, especially the choice between latent class growth analysis (LCGA) techniques developed by Nagin and…

Improving students’ mathematical representational ability through RME-based progressive mathematization

NASA Astrophysics Data System (ADS)

Warsito; Darhim; Herman, T.

2018-01-01

This study aims to determine the differences in the improving of mathematical representation ability based on progressive mathematization with realistic mathematics education (PMR-MP) with conventional learning approach (PB). The method of research is quasi-experiments with non-equivalent control group designs. The study population is all students of class VIII SMPN 2 Tangerang consisting of 6 classes, while the sample was taken two classes with purposive sampling technique. The experimental class is treated with PMR-MP while the control class is treated with PB. The instruments used are test of mathematical representation ability. Data analysis was done by t-test, ANOVA test, post hoc test, and descriptive analysis. The result of analysis can be concluded that: 1) there are differences of mathematical representation ability improvement between students treated by PMR-MP and PB, 2) no interaction between learning approach (PMR-MP, PB) and prior mathematics knowledge (PAM) to improve students’ mathematical representation; 3) Students’ mathematical representation improvement in the level of higher PAM is better than medium, and low PAM students. Thus, based on the process of mathematization, it is very important when the learning direction of PMR-MP emphasizes on the process of building mathematics through a mathematical model.

Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy.

PubMed

Dias, Rafael Carlos Eloy; Valderrama, Patrícia; Março, Paulo Henrique; Dos Santos Scholz, Maria Brigida; Edelmann, Michael; Yeretzian, Chahan

2018-07-30

Chemical analyses and sensory evaluation are the most applied methods for quality control of roasted and ground coffee (RG). However, faster alternatives would be highly valuable. Here, we applied infrared-photoacoustic spectroscopy (FTIR-PAS) on RG powder. Mixtures of specific defective beans were blended with healthy (defect-free) Coffea arabica and Coffea canephora bases in specific ratios, forming different classes of blends. Principal Component Analysis allowed predicting the amount/fraction and nature of the defects in blends while partial Least Squares Discriminant Analysis revealed similarities between blends (=samples). A successful predictive model was obtained using six classes of blends. The model could classify 100% of the samples into four classes. The specificities were higher than 0.9. Application of FTIR-PAS on RG coffee to characterize and classify blends has shown to be an accurate, easy, quick and "green" alternative to current methods. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

PubMed

Schlattmann, Peter; Verba, Maryna; Dewey, Marc; Walther, Mario

2015-01-01

Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present. Copyright © 2015 Elsevier Inc. All rights reserved.

Utilization Elementary Siphons of Petri Net to Solved Deadlocks in Flexible Manufacturing Systems

NASA Astrophysics Data System (ADS)

Abdul-Hussin, Mowafak Hassan

2015-07-01

This article presents an approach to the constructing a class structural analysis of Petri nets, where elementary siphons are mainly used in the development of a deadlock control policy of flexible manufacturing systems (FMSs), that has been exploited successfully for the design of supervisors of some supervisory control problems. Deadlock-free operation of FMSs is significant objectives of siphons in the Petri net. The structure analysis of Petri net models has efficiency in control of FMSs, however different policy can be implemented for the deadlock prevention. Petri nets models based deadlock prevention for FMS's has gained considerable interest in the development of control theory and methods for design, controlling, operation, and performance evaluation depending of the special class of Petri nets called S3PR. Both structural analysis and reachability tree analysis is used for the purposes analysis, simulation and control of Petri nets. In our ex-perimental approach based to siphon is able to resolve the problem of deadlock occurred to Petri nets that are illustrated with an FMS.

[Poverty profile regarding households participating in a food assistance program].

PubMed

Álvarez-Uribe, Martha C; Aguirre-Acevedo, Daniel C

2012-06-01

This study was aimed at establishing subgroups having specific socioeconomic characteristics by using latent class analysis as a method for segmenting target population members of the MANA-ICBF supplementary food program in the Antioquia department of Colombia and determine their differences regarding poverty and health conditions in efficiently addressing pertinent resources, programs and policies. The target population consisted of 200,000 children and their households involved in the MANA food assistance program; a representative sample by region was used. Latent class analysis was used, as were the expectation-maximization and Newton Raphson algorithms for identifying the appropriate number of classes. The final model classified the households into four clusters or classes, differing according to well-defined socio-demographic conditions affecting children's health. Some homes had a greater depth of poverty, therefore lowering the families' quality of life and affecting the health of the children in this age group.

Latent class analysis of accident risks in usage-based insurance: Evidence from Beijing.

PubMed

Jin, Wen; Deng, Yinglu; Jiang, Hai; Xie, Qianyan; Shen, Wei; Han, Weijian

2018-06-01

Car insurance is quickly becoming a big data industry, with usage-based insurance (UBI) poised to potentially change the business of insurance. Telematics data, which are transmitted from wireless devices in car, are widely used in UBI to obtain individual-level travel and driving characteristics. While most existing studies have introduced telematics data into car insurance pricing, the telematics-related characteristics are directly obtained from the raw data. In this study, we propose to quantify drivers' familiarity with their driving routes and develop models to quantify drivers' accident risks using the telematics data. In addition, we build a latent class model to study the heterogeneity in travel and driving styles based on the telematics data, which has not been investigated in literature. Our main results include: (1) the improvement to the model fit is statistically significant by adding telematics-related characteristics; (2) drivers' familiarity with their driving trips is critical to identify high risk drivers, and the relationship between drivers' familiarity and accident risks is non-linear; (3) the drivers can be classified into two classes, where the first class is the low risk class with 0.54% of its drivers reporting accidents, and the second class is the high risk class with 20.66% of its drivers reporting accidents; and (4) for the low risk class, drivers with high probability of reporting accidents can be identified by travel-behavior-related characteristics, while for the high risk class, they can be identified by driving-behavior-related characteristics. The driver's familiarity will affect the probability of reporting accidents for both classes. Copyright © 2018 Elsevier Ltd. All rights reserved.

LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome

PubMed Central

Neumann, Steffen; Schmitt-Kopplin, Philippe

2017-01-01

Lipid identification is a major bottleneck in high-throughput lipidomics studies. However, tools for the analysis of lipid tandem MS spectra are rather limited. While the comparison against spectra in reference libraries is one of the preferred methods, these libraries are far from being complete. In order to improve identification rates, the in silico fragmentation tool MetFrag was combined with Lipid Maps and lipid-class specific classifiers which calculate probabilities for lipid class assignments. The resulting LipidFrag workflow was trained and evaluated on different commercially available lipid standard materials, measured with data dependent UPLC-Q-ToF-MS/MS acquisition. The automatic analysis was compared against manual MS/MS spectra interpretation. With the lipid class specific models, identification of the true positives was improved especially for cases where candidate lipids from different lipid classes had similar MetFrag scores by removing up to 56% of false positive results. This LipidFrag approach was then applied to MS/MS spectra of lipid extracts of the nematode Caenorhabditis elegans. Fragments explained by LipidFrag match known fragmentation pathways, e.g., neutral losses of lipid headgroups and fatty acid side chain fragments. Based on prediction models trained on standard lipid materials, high probabilities for correct annotations were achieved, which makes LipidFrag a good choice for automated lipid data analysis and reliability testing of lipid identifications. PMID:28278196

Modelling the social dynamics of a sex industry: its implications for spread of HIV/AIDS.

PubMed

Hsieh, Ying Hen; Hsun Chen, Chien

2004-01-01

A theoretical model is proposed for a community which has the structure of two classes (direct and indirect) of commercial sex workers (CSW), and two classes of sexually active male customers with different levels of sexual activity. The direct CSW's work in brothels while the indirect CSW's are based in commercial establishments such as bars, cafes, and massage parlours where sex can be bought on request and conducted elsewhere. Behaviour change and the resulting change of activity class occurs between the two classes of CSW's and two classes of males under the setting of the proliferation of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome epidemic and the subsequent intervention programmes. In recently years, this phenomenon has been observed in several countries in Asia. Given the lower levels of condom use and higher HIV prevalence of the indirect CSW's, ascertaining the impact of this change in the structure of the sex industry on the spread of HIV is the main focus of this paper. The complete analysis of the disease-free model is given. For the full model, local analysis will be performed for the case of preferred mixing without activity class change, as well as the case with activity class change and restricted mixing. The basic reproduction number for the spread of epidemic will be derived for each case. Results of biological significance include: (i). the change of behaviour by the CSW's has a more direct effect on the spread of HIV than that of the male customers; (ii). the basic reproduction number is obtained by considering all possible infection cycles of the heterosexual transmission of HIV which indicates the importance of understanding the sexual networking in heterosexual transmission of HIV; (iii). the social dynamics of the sex industry is not just a simple 'supply and demand' mechanism driven by the demand of the customers, hence highlighting the need for further understanding of the changing structure of the sex industry. The main points of this work will be illustrated with numerical examples using the HIV data of Thailand.

An empirical analysis of ontology reuse in BioPortal.

PubMed

Ochs, Christopher; Perl, Yehoshua; Geller, James; Arabandi, Sivaram; Tudorache, Tania; Musen, Mark A

2017-07-01

Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies. Content reuse enables a consistent representation of a domain and reusing content can save an ontology author significant time and effort. Prior studies have investigated the existence of reused terms among the ontologies in the NCBO BioPortal, but as of yet there has not been a study investigating how the ontologies in BioPortal utilize reused content in the modeling of their own content. In this study we investigate how 355 ontologies hosted in the NCBO BioPortal reuse content from other ontologies for the purposes of creating new ontology content. We identified 197 ontologies that reuse content. Among these ontologies, 108 utilize reused classes in the modeling of their own classes and 116 utilize reused properties in class restrictions. Current utilization of reuse and quality issues related to reuse are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Satellite services system analysis study. Volume 1, part 2: Executive summary

NASA Technical Reports Server (NTRS)

1981-01-01

The early mission model was developed through a survey of the potential user market. Service functions were defined and a group of design reference missions were selected which represented needs for each of the service functions. Servicing concepts were developed through mission analysis and STS timeline constraint analysis. The hardware needs for accomplishing the service functions were identified with emphasis being placed on applying equipment in the current NASA inventory and that in advanced stages of planning. A more comprehensive service model was developed based on the NASA and DoD mission models segregated by mission class. The number of service events of each class were estimated based on average revisit and service assumptions. Service Kits were defined as collections of equipment applicable to performing one or more service functions. Preliminary design was carrid out on a selected set of hardware needed for early service missions. The organization and costing of the satellie service systems were addressed.

Empirical redefinition of comprehensive health and well-being in the older adults of the United States.

PubMed

McClintock, Martha K; Dale, William; Laumann, Edward O; Waite, Linda

2016-05-31

The World Health Organization (WHO) defines health as a "state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." Despite general acceptance of this comprehensive definition, there has been little rigorous scientific attempt to use it to measure and assess population health. Instead, the dominant model of health is a disease-centered Medical Model (MM), which actively ignores many relevant domains. In contrast to the MM, we approach this issue through a Comprehensive Model (CM) of health consistent with the WHO definition, giving statistically equal consideration to multiple health domains, including medical, physical, psychological, functional, and sensory measures. We apply a data-driven latent class analysis (LCA) to model 54 specific health variables from the National Social Life, Health, and Aging Project (NSHAP), a nationally representative sample of US community-dwelling older adults. We first apply the LCA to the MM, identifying five health classes differentiated primarily by having diabetes and hypertension. The CM identifies a broader range of six health classes, including two "emergent" classes completely obscured by the MM. We find that specific medical diagnoses (cancer and hypertension) and health behaviors (smoking) are far less important than mental health (loneliness), sensory function (hearing), mobility, and bone fractures in defining vulnerable health classes. Although the MM places two-thirds of the US population into "robust health" classes, the CM reveals that one-half belong to less healthy classes, independently associated with higher mortality. This reconceptualization has important implications for medical care delivery, preventive health practices, and resource allocation.

The intersectionality of discrimination attributes and bullying among youth: an applied latent class analysis.

PubMed

Garnett, Bernice Raveche; Masyn, Katherine E; Austin, S Bryn; Miller, Matthew; Williams, David R; Viswanath, Kasisomayajula

2014-08-01

Discrimination is commonly experienced among adolescents. However, little is known about the intersection of multiple attributes of discrimination and bullying. We used a latent class analysis (LCA) to illustrate the intersections of discrimination attributes and bullying, and to assess the associations of LCA membership to depressive symptoms, deliberate self harm and suicidal ideation among a sample of ethnically diverse adolescents. The data come from the 2006 Boston Youth Survey where students were asked whether they had experienced discrimination based on four attributes: race/ethnicity, immigration status, perceived sexual orientation and weight. They were also asked whether they had been bullied or assaulted for these attributes. A total of 965 (78%) students contributed to the LCA analytic sample (45% Non-Hispanic Black, 29% Hispanic, 58% Female). The LCA revealed that a 4-class solution had adequate relative and absolute fit. The 4-classes were characterized as: low discrimination (51%); racial discrimination (33%); sexual orientation discrimination (7%); racial and weight discrimination with high bullying (intersectional class) (7%). In multivariate models, compared to the low discrimination class, individuals in the sexual orientation discrimination class and the intersectional class had higher odds of engaging in deliberate self-harm. Students in the intersectional class also had higher odds of suicidal ideation. All three discrimination latent classes had significantly higher depressive symptoms compared to the low discrimination class. Multiple attributes of discrimination and bullying co-occur among adolescents. Research should consider the co-occurrence of bullying and discrimination.

Patterns of HIV Risks and Related Factors among People Who Inject Drugs in Kermanshah, Iran: A Latent Class Analysis.

PubMed

Sharifi, Hamid; Mirzazadeh, Ali; Noroozi, Alireza; Marshall, Brandon D L; Farhoudian, Ali; Higgs, Peter; Vameghi, Meroe; Mohhamadi Shahboulaghi, Farahnaz; Qorbani, Mostafa; Massah, Omid; Armoon, Bahram; Noroozi, Mehdi

2017-01-01

The objective of this study was to explore patterns of drug use and sexual risk behaviors among people who inject drugs (PWID) in Iran. We surveyed 500 PWID in Kermanshah concerning demographic characteristics, sexual risk behaviors, and drug-related risk behaviors in the month prior to study. We used latent class analysis (LCA) to establish a baseline model of risk profiles and to identify the optimal number of latent classes, and we used ordinal regression to identify factors associated with class membership. Three classes of multiple HIV risk were identified. The probability of membership in the high-risk class was 0.33, compared to 0.26 and 0.40 for the low- and moderate-risk classes, respectively. Compared to members in the lowest-risk class (reference group), the highest-risk class members had higher odds of being homeless (OR = 4.5, CI: 1.44-8.22; p = 0.001) in the past 12 months. Members of the high-risk class had lower odds of regularly visiting a needle and syringe exchange program as compared to the lowest-risk class members (AOR = 0.42, CI: 0.2-0.81; p = 0.01). Findings show the sexual and drug-related HIV risk clusters among PWID in Iran, and emphasize the importance of developing targeted prevention and harm reduction programs for all domains of risk behaviors, both sexual and drug use related.

PAL: an object-oriented programming library for molecular evolution and phylogenetics.

PubMed

Drummond, A; Strimmer, K

2001-07-01

Phylogenetic Analysis Library (PAL) is a collection of Java classes for use in molecular evolution and phylogenetics. PAL provides a modular environment for the rapid construction of both special-purpose and general analysis programs. PAL version 1.1 consists of 145 public classes or interfaces in 13 packages, including classes for models of character evolution, maximum-likelihood estimation, and the coalescent, with a total of more than 27000 lines of code. The PAL project is set up as a collaborative project to facilitate contributions from other researchers. AVAILIABILTY: The program is free and is available at http://www.pal-project.org. It requires Java 1.1 or later. PAL is licensed under the GNU General Public License.

The GRASP 3: Graphical Reliability Analysis Simulation Program. Version 3: A users' manual and modelling guide

NASA Technical Reports Server (NTRS)

Phillips, D. T.; Manseur, B.; Foster, J. W.

1982-01-01

Alternate definitions of system failure create complex analysis for which analytic solutions are available only for simple, special cases. The GRASP methodology is a computer simulation approach for solving all classes of problems in which both failure and repair events are modeled according to the probability laws of the individual components of the system.

Associations Between Latent Classes of Interpersonal Polyvictimization and Polyperpetration and Sexual Risk Behaviors Among Young Pregnant Couples: A Dyadic Analysis.

PubMed

Willie, Tiara; Kershaw, Trace S

2018-05-24

Interpersonal violence victimization and perpetration have been associated with sexual risk behaviors among adolescents and young adults, but research is lacking on: (1) how patterns of interpersonal polyvictimization and polyperpetration are associated with sexual risk among young pregnant couples, and (2) how individual and partner experiences of violence differentially impact sexual risk. The current analyses used baseline data from a longitudinal study that followed 296 pregnant young couples from pregnancy to 12 months postpartum. Couples were recruited at obstetrics and gynecology clinics, and an ultrasound clinic in the U.S. Latent class analysis identified subgroups based on polyvictimization and polyperpetration. Using the Actor-Partner Interdependence Model, path analyses assessed actor-partner effects of class membership on sexual risk. Three latent classes were used for women: Class 1: Polyvictim-Polyperpetrator; Class 2: Nonvictim-Nonperpetrator; and Class 3: Community and Prior IPV Victim. Four latent classes were used for men: Class 1: Community and Prior IPV Victim; Class 2: Polyvictim-Nonpartner Perpetrator; Class 3: Prior IPV and Peer Victim; and Class 4: Nonvictim-Nonperpetrator. Path analyses revealed that females in Class 2 and their male partners had higher condom use than females in Class 3. Males in Class 2 had more sexual partners than males in Class 1. Among nonmonogamous couples, males in Class 2 were less likely to be involved with a female partner reporting unprotected sex than males in Class 1. Among nonmonogamous couples, females in Class 2 had more acts of unprotected sex than females in Class 1. Males in Class 4 were less likely to have concurrent sexual partners compared to males in Class 1. Risk reduction interventions should address both victimization and perpetration. Additional research is needed to understand how mechanisms driving differential sexual risk by patterns of interpersonal polyvictimization and polyperpetration.

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

NASA Astrophysics Data System (ADS)

Lin, Bin; An, Jubai; Brown, Carl E.; Chen, Weiwei

2003-05-01

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

Schizophrenia with prominent catatonic features ('catatonic schizophrenia') III. Latent class analysis of the catatonic syndrome.

PubMed

Ungvari, Gabor S; Goggins, William; Leung, Siu-Kau; Lee, Edwin; Gerevich, Jozsef

2009-02-01

No reports have yet been published on catatonia using latent class analysis (LCA). This study applied LCA to a large, diagnostically homogenous sample of patients with chronic schizophrenia who also presented with catatonic symptoms. A random sample of 225 Chinese inpatients with DSM-IV schizophrenia was selected from the long-stay wards of a psychiatric hospital. Their psychopathology, extrapyramidal motor status and level of functioning were evaluated with standardized rating scales. Catatonia was rated using a modified version of the Bush-Francis Catatonia Rating Scale. LCA was then applied to the 178 patients who presented with at least one catatonic sign. In LCA a four-class solution was found to fit best the statistical model. Classes 1, 2, 3 and 4 constituted 18%, 39.4%, 20.1% and 22.5% of the whole catatonic sample, respectively. Class 1 included patients with symptoms of 'automatic' phenomena (automatic obedience, Mitgehen, waxy flexibility). Class 2 comprised patients with 'repetitive/echo' phenomena (perseveration, stereotypy, verbigeration, mannerisms and grimacing). Class 3 contained patients with symptoms of 'withdrawal' (immobility, mutism, posturing, staring and withdrawal). Class 4 consisted of 'agitated/resistive' patients, who displayed symptoms of excitement, impulsivity, negativism and combativeness. The symptom composition of these 4 classes was nearly identical with that of the four factors identified by factor analysis in the same cohort of subjects in an earlier study. In multivariate regression analysis, the 'withdrawn' class was associated with higher scores on the Scale of Assessment of Negative Symptoms and lower and higher scores for negative and positive items respectively on the Nurses' Observation Scale for Inpatient Evaluation's (NOSIE). The 'automatic' class was associated with lower values on the Simpson-Angus Extrapyramidal Side Effects Scale, and the 'repetitive/echo' class with higher scores on the NOSIE positive items. These results provide preliminary support for the notion that chronic schizophrenia patients with catatonic features can be classified into 4 distinct syndromal groups on the basis of their motor symptoms. Identifying distinct catatonic syndromes would help to find their biological substrates and to develop specific therapeutic measures.

Comparing physically-based and statistical landslide susceptibility model outputs - a case study from Lower Austria

NASA Astrophysics Data System (ADS)

Canli, Ekrem; Thiebes, Benni; Petschko, Helene; Glade, Thomas

2015-04-01

By now there is a broad consensus that due to human-induced global change the frequency and magnitude of heavy precipitation events is expected to increase in certain parts of the world. Given the fact, that rainfall serves as the most common triggering agent for landslide initiation, also an increased landside activity can be expected there. Landslide occurrence is a globally spread phenomenon that clearly needs to be handled. The present and well known problems in modelling landslide susceptibility and hazard give uncertain results in the prediction. This includes the lack of a universal applicable modelling solution for adequately assessing landslide susceptibility (which can be seen as the relative indication of the spatial probability of landslide initiation). Generally speaking, there are three major approaches for performing landslide susceptibility analysis: heuristic, statistical and deterministic models, all with different assumptions, its distinctive data requirements and differently interpretable outcomes. Still, detailed comparison of resulting landslide susceptibility maps are rare. In this presentation, the susceptibility modelling outputs of a deterministic model (Stability INdex MAPping - SINMAP) and a statistical modelling approach (generalized additive model - GAM) are compared. SINMAP is an infinite slope stability model which requires parameterization of soil mechanical parameters. Modelling with the generalized additive model, which represents a non-linear extension of a generalized linear model, requires a high quality landslide inventory that serves as the dependent variable in the statistical approach. Both methods rely on topographical data derived from the DTM. The comparison has been carried out in a study area located in the district of Waidhofen/Ybbs in Lower Austria. For the whole district (ca. 132 km²), 1063 landslides have been mapped and partially used within the analysis and the validation of the model outputs. The respective susceptibility maps have been reclassified to contain three susceptibility classes each. The comparison of the susceptibility maps was performed on a grid cell basis. A match of the maps was observed for grid cells located in the same susceptibility class. In contrast, a mismatch or deviation was observed for locations with different assigned susceptibility classes (up to two classes' difference). Although the modelling approaches differ significantly, more than 70% of the pixels reveal a match in the same susceptibility class. A mismatch by two classes' difference occurred in less than 2% of all pixels. Although the result looks promising and strengthens the confidence in the susceptibility zonation for this area, some of the general drawbacks related to the respective approaches still have to be addressed in further detail. Future work is heading towards an integration of probabilistic aspects into deterministic modelling.

Tree Height and DBH Growth Model Establishment of Main Tree Species in Wuling Mountain Small Watershed

NASA Astrophysics Data System (ADS)

Luo, Jia; Zhang, Min; Zhou, Xiaoling; Chen, Jianhua; Tian, Yuxin

2018-01-01

Taken 4 main tree species in the Wuling mountain small watershed as research objects, 57 typical sample plots were set up according to the stand type, site conditions and community structure. 311 goal diameter-class sample trees were selected according to diameter-class groups of different tree-height grades, and the optimal fitting models of tree height and DBH growth of main tree species were obtained by stem analysis using Richard, Logistic, Korf, Mitscherlich, Schumacher, Weibull theoretical growth equations, and the correlation coefficient of all optimal fitting models reached above 0.9. Through the evaluation and test, the optimal fitting models possessed rather good fitting precision and forecast dependability.

A statistical model for radar images of agricultural scenes

NASA Technical Reports Server (NTRS)

Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.

1982-01-01

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

Examining geological controls on baseflow index (BFI) using regression analysis: An illustration from the Thames Basin, UK

NASA Astrophysics Data System (ADS)

Bloomfield, J. P.; Allen, D. J.; Griffiths, K. J.

2009-06-01

SummaryLinear regression methods can be used to quantify geological controls on baseflow index (BFI). This is illustrated using an example from the Thames Basin, UK. Two approaches have been adopted. The areal extents of geological classes based on lithostratigraphic and hydrogeological classification schemes have been correlated with BFI for 44 'natural' catchments from the Thames Basin. When regression models are built using lithostratigraphic classes that include a constant term then the model is shown to have some physical meaning and the relative influence of the different geological classes on BFI can be quantified. For example, the regression constants for two such models, 0.64 and 0.69, are consistent with the mean observed BFI (0.65) for the Thames Basin, and the signs and relative magnitudes of the regression coefficients for each of the lithostratigraphic classes are consistent with the hydrogeology of the Basin. In addition, regression coefficients for the lithostratigraphic classes scale linearly with estimates of log 10 hydraulic conductivity for each lithological class. When a regression is built using a hydrogeological classification scheme with no constant term, the model does not have any physical meaning, but it has a relatively high adjusted R2 value and because of the continuous coverage of the hydrogeological classification scheme, the model can be used for predictive purposes. A model calibrated on the 44 'natural' catchments and using four hydrogeological classes (low-permeability surficial deposits, consolidated aquitards, fractured aquifers and intergranular aquifers) is shown to perform as well as a model based on a hydrology of soil types (BFIHOST) scheme in predicting BFI in the Thames Basin. Validation of this model using 110 other 'variably impacted' catchments in the Basin shows that there is a correlation between modelled and observed BFI. Where the observed BFI is significantly higher than modelled BFI the deviations can be explained by an exogenous factor, catchment urban area. It is inferred that this is may be due influences from sewage discharge, mains leakage, and leakage from septic tanks.

Tri-city study of Ecstasy use problems: a latent class analysis.

PubMed

Scheier, Lawrence M; Ben Abdallah, Arbi; Inciardi, James A; Copeland, Jan; Cottler, Linda B

2008-12-01

This study used latent class analysis to examine distinctive subtypes of Ecstasy users based on 24 abuse and dependence symptoms underlying standard DSM-IV criteria. Data came from a three site, population-based, epidemiological study to examine diagnostic nosology for Ecstasy use. Subject inclusion criteria included lifetime Ecstasy use exceeding five times and once in the past year, with participants ranging in age between 16 and 47 years of age from St. Louis, Miami, U.S. and Sydney, Australia. A satisfactory model typified four latent classes representing clearly differentiated diagnostic clusters including: (1) a group of sub-threshold users endorsing few abuse and dependence symptoms (negatives), (2) a group of 'diagnostic orphans' who had characteristic features of dependence for a select group of symptoms (mild dependent), (3) a 'transitional group' mimicking the orphans with regard to their profile of dependence also but reporting some abuse symptoms (moderate dependent), and (4) a 'severe dependent' group with a distinct profile of abuse and dependence symptoms. A multinomial logistic regression model indicated that certain latent classes showed unique associations with external non-diagnostic markers. Controlling for demographic characteristics and lifetime quantity of Ecstasy pill use, criminal behavior and motivational cues for Ecstasy use were the most efficient predictors of cluster membership. This study reinforces the heuristic utility of DSM-IV criteria applied to Ecstasy but with a different collage of symptoms that produced four distinct classes of Ecstasy users.

The Fifth Annual Thermal and Fluids Analysis Workshop

NASA Technical Reports Server (NTRS)

1993-01-01

The Fifth Annual Thermal and Fluids Analysis Workshop was held at the Ohio Aerospace Institute, Brook Park, Ohio, cosponsored by NASA Lewis Research Center and the Ohio Aerospace Institute, 16-20 Aug. 1993. The workshop consisted of classes, vendor demonstrations, and paper sessions. The classes and vendor demonstrations provided participants with the information on widely used tools for thermal and fluid analysis. The paper sessions provided a forum for the exchange of information and ideas among thermal and fluids analysts. Paper topics included advances and uses of established thermal and fluids computer codes (such as SINDA and TRASYS) as well as unique modeling techniques and applications.

Heterotopic expression of class B floral homeotic genes supports a modified ABC model for tulip (Tulipa gesneriana).

PubMed

Kanno, Akira; Saeki, Hiroshi; Kameya, Toshiaki; Saedler, Heinz; Theissen, Günter

2003-07-01

In higher eudicotyledonous angiosperms the floral organs are typically arranged in four different whorls, containing sepals, petals, stamens and carpels. According to the ABC model, the identity of these organs is specified by floral homeotic genes of class A, A+B, B+C and C, respectively. In contrast to the sepal and petal whorls of eudicots, the perianths of many plants from the Liliaceae family have two outer whorls of almost identical petaloid organs, called tepals. To explain the Liliaceae flower morphology, van Tunen et al. (1993) proposed a modified ABC model, exemplified with tulip. According to this model, class B genes are not only expressed in whorls 2 and 3, but also in whorl 1. Thus the organs of both whorls 1 and 2 express class A plus class B genes and, therefore, get the same petaloid identity. To test this modified ABC model we have cloned and characterized putative class B genes from tulip. Two DEF- and one GLO-like gene were identified, named TGDEFA, TGDEFB and TGGLO. Northern hybridization analysis showed that all of these genes are expressed in whorls 1, 2 and 3 (outer and inner tepals and stamens), thus corroborating the modified ABC model. In addition, these experiments demonstrated that TGGLO is also weakly expressed in carpels, leaves, stems and bracts. Gel retardation assays revealed that TGGLO alone binds to DNA as a homodimer. In contrast, TGDEFA and TGDEFB cannot homodimerize, but make heterodimers with PI. Homodimerization of GLO-like protein has also been reported for lily, suggesting that this phenomenon is conserved within Liliaceae plants or even monocot species.

CCSDS Advanced Orbiting Systems Virtual Channel Access Service for QoS MACHETE Model

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John S.

2011-01-01

To support various communications requirements imposed by different missions, interplanetary communication protocols need to be designed, validated, and evaluated carefully. Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in "Simulator of Space Communication Networks" (NPO-41373), NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. By building abstract behavioral models of network protocols, one can validate performance after identifying the appropriate metrics of interest. The innovators have extended the MACHETE model library to include a generic link-layer Virtual Channel (VC) model supporting quality-of-service (QoS) controls based on IP streams. The main purpose of this generic Virtual Channel model addition was to interface fine-grain flow-based QoS (quality of service) between the network and MAC layers of the QualNet simulator, a commercial component of MACHETE. This software model adds the capability of mapping IP streams, based on header fields, to virtual channel numbers, allowing extended QoS handling at link layer. This feature further refines the QoS v existing at the network layer. QoS at the network layer (e.g. diffserv) supports few QoS classes, so data from one class will be aggregated together; differentiating between flows internal to a class/priority is not supported. By adding QoS classification capability between network and MAC layers through VC, one maps multiple VCs onto the same physical link. Users then specify different VC weights, and different queuing and scheduling policies at the link layer. This VC model supports system performance analysis of various virtual channel link-layer QoS queuing schemes independent of the network-layer QoS systems.

The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation.

PubMed

Hogan, William R; Wagner, Michael M; Brochhausen, Mathias; Levander, John; Brown, Shawn T; Millett, Nicholas; DePasse, Jay; Hanna, Josh

2016-08-18

We developed the Apollo Structured Vocabulary (Apollo-SV)-an OWL2 ontology of phenomena in infectious disease epidemiology and population biology-as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators. Apollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures. In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound. Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology. Our ontological analysis as expressed in Apollo-SV was instrumental in developing a simulator-independent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/apollo_sv.owl .

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)

NASA Astrophysics Data System (ADS)

Badawy, Bakr; Polavarapu, Saroja; Jones, Dylan B. A.; Deng, Feng; Neish, Michael; Melton, Joe R.; Nassar, Ray; Arora, Vivek K.

2018-02-01

The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth system models (CanESMs). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere-land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASS-CTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin-up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing from the Climate Research Unit (CRU) combined with reanalysis fields from the National Centers for Environmental Prediction (NCEP) to form CRU-NCEP dataset. This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However, notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are consistent to some extent with other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data-poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent. Coupling CLASS-CTEM into the Environment Canada Carbon Assimilation System (EC-CAS) is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.

Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data.

PubMed

Bellatorre, Anna; Jackson, Sharon H; Choi, Kelvin

2017-01-01

To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies. Population-based survey. Individuals participated in 2003-2004, 2005-2006, or 2009-2010 the National Health and Nutrition Examination Survey (NHANES), and 2010 Coronary Artery Risk Development in Young Adults (CARDIA) survey (research materials obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center). 3084, 3040 and 3318 US adults from the 2003-2004, 2005-2006 and 2009-2010 NHANES samples respectively, and 5,115 US adults in the CARDIA cohort. We proposed the Diabetes Typology Model (DTM) through the use of six composite measures based on the Homeostatic Model Assessment (HOMA-IR, HOMA-%β, high HOMA-%S), insulin and glucose levels, and body mass index and conducted latent class analyses to empirically classify individuals into different classes. Three empirical latent classes consistently emerged across studies (entropy = 0.81-0.998). These three classes were likely Type 1 DM, likely Type 2 DM, and atypical DM. The classification has high sensitivity (75.5%), specificity (83.3%), and positive predictive value (97.4%) when validated against C-peptide level. Correlates of Type 2 DM were significantly associated with model-identified Type 2 DM. Compared to regression analysis on known correlates of Type 2 DM using all diabetes cases as outcomes, using DTM to remove likely Type 1 DM and atypical DM cases results in a 2.5-5.3% r-square improvement in the regression analysis, as well as model fits as indicated by significant improvement in -2 log likelihood (p<0.01). Lastly, model-defined likely Type 2 DM was significantly associated with known correlates of Type 2 DM (e.g., age, waist circumference), which provide additional validation of the DTM-defined classes. Our Diabetes Typology Model reflects a promising first step toward discerning likely DM types from population-based data. This novel tool will improve how large population-based studies can be used to examine behavioral and environmental factors associated with different types of DM.

Neural network classification technique and machine vision for bread crumb grain evaluation

NASA Astrophysics Data System (ADS)

Zayas, Inna Y.; Chung, O. K.; Caley, M.

1995-10-01

Bread crumb grain was studied to develop a model for pattern recognition of bread baked at Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a 512 multiplied by 512 format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigen values, shape of a slice and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of MATLAB package were used for data analysis. Learning vector quantization method and multivariate discriminant analysis were applied to bread slices from what of different sources. A training and test sets of different bread crumb texture classes were obtained. The ranking of subimages was well correlated with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created according to human judgement with image features was low. Recognition of arbitrarily created classes, according to porosity patterns, with several feature patterns was approximately 90%. Correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Least Squares Distance Method of Cognitive Validation and Analysis for Binary Items Using Their Item Response Theory Parameters

ERIC Educational Resources Information Center

Dimitrov, Dimiter M.

2007-01-01

The validation of cognitive attributes required for correct answers on binary test items or tasks has been addressed in previous research through the integration of cognitive psychology and psychometric models using parametric or nonparametric item response theory, latent class modeling, and Bayesian modeling. All previous models, each with their…

Postural abnormalities and contraversive pushing following right hemisphere brain damage.

PubMed

Lafosse, C; Kerckhofs, E; Vereeck, L; Troch, M; Van Hoydonck, G; Moeremans, M; Sneyers, C; Broeckx, J; Dereymaeker, L

2007-06-01

We investigated the presence of postural abnormalities in a consecutive sample of stroke patients, with either left or right brain damage, in relation to their perceived body position in space. The presence or absence of posture-related symptoms was judged by two trained therapists and subsequently analysed by hierarchical classes analysis (HICLAS). The subject classes resulting from the HICLAS model were further validated with respect to posture-related measurements, such as centre of gravity position and head position, as well as measurements related to the postural body scheme, such as the perception of postural and visual verticality. The results of the classification analysis clearly demonstrated a relation between the presence of right brain damage and abnormalities in body geometry. The HICLAS model revealed three classes of subjects: The first class contained almost all the patients without neglect and without any signs of contraversive pushing. They were mainly characterised by a normal body axis in any position. The second class were all neglect patients but predominantly without any contraversive pushing. The third class contained right brain damaged patients, all showing neglect and mostly exhibiting contraversive pushing. The patients in the third class showed a clear resistance to bringing the weight over to the ipsilesional side when the therapist attempted to make the subject achieve a vertical posture across the midline. The clear correspondence between abnormalities of the observed body geometry and the tilt of the subjective postural and visual vertical suggests that a patient's postural body geometry is characterised by leaning towards the side of space where he/she feels aligned with an altered postural body scheme. The presence of contraversive pushing after right brain damage points in to a spatial higher-order processing deficit underlying the higher frequency and severity of the axial postural abnormalities found after right brain lesions.

High-Speed Video Analysis in a Conceptual Physics Class

NASA Astrophysics Data System (ADS)

Desbien, Dwain M.

2011-09-01

The use of probe ware and computers has become quite common in introductory physics classrooms. Video analysis is also becoming more popular and is available to a wide range of students through commercially available and/or free software.2,3 Video analysis allows for the study of motions that cannot be easily measured in the traditional lab setting and also allows real-world situations to be analyzed. Many motions are too fast to easily be captured at the standard video frame rate of 30 frames per second (fps) employed by most video cameras. This paper will discuss using a consumer camera that can record high-frame-rate video in a college-level conceptual physics class. In particular this will involve the use of model rockets to determine the acceleration during the boost period right at launch and compare it to a simple model of the expected acceleration.

Prevalence and co-occurrence of addictive behaviors among former alternative high school youth: A longitudinal follow-up study.

PubMed

Sussman, Steve; Pokhrel, Pallav; Sun, Ping; Rohrbach, Louise A; Spruijt-Metz, Donna

2015-09-01

Recent work has studied addictions using a matrix measure, which taps multiple addictions through single responses for each type. This is the first longitudinal study using a matrix measure. We investigated the use of this approach among former alternative high school youth (average age = 19.8 years at baseline; longitudinal n = 538) at risk for addictions. Lifetime and last 30-day prevalence of one or more of 11 addictions reviewed in other work was the primary focus (i.e., cigarettes, alcohol, hard drugs, shopping, gambling, Internet, love, sex, eating, work, and exercise). These were examined at two time-points one year apart. Latent class and latent transition analyses (LCA and LTA) were conducted in Mplus. Prevalence rates were stable across the two time-points. As in the cross-sectional baseline analysis, the 2-class model (addiction class, non-addiction class) fit the data better at follow-up than models with more classes. Item-response or conditional probabilities for each addiction type did not differ between time-points. As a result, the LTA model utilized constrained the conditional probabilities to be equal across the two time-points. In the addiction class, larger conditional probabilities (i.e., 0.40-0.49) were found for love, sex, exercise, and work addictions; medium conditional probabilities (i.e., 0.17-0.27) were found for cigarette, alcohol, other drugs, eating, Internet and shopping addiction; and a small conditional probability (0.06) was found for gambling. Persons in an addiction class tend to remain in this addiction class over a one-year period.

Psychological features of North Korean female refugees on the MMPI-2: latent profile analysis.

PubMed

Kim, Seong-Hyeon; Kim, Hee Kyung; Lee, Narae

2013-12-01

This study examined the heterogeneity in the Minnesota Multiphasic Personality Inventory-2nd Edition (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) profiles of North Korean female refugee population (N = 2,163) using latent profile analysis (LPA). The North Korean female refugee sample arrived at Hanawon, South Korea's resettlement center for North Korean refugees in 2008 and 2009 and took the MMPI-2 as part of an initial psychological screen. The analysis, which included the T scores of the 6 validity scales and the 10 standard clinical scales, identified 4 classes with distinctive psychological features: Class 1 (nonclinical), Class 2 (demoralized), Class 3 (somatized), and Class 4 (detached). The 4 covariates entered into the model (age, education, affiliation with a religion, and the number of forced repatriations) impacted the likelihood of belonging to certain classes. As hypothesized, older age, fewer years of education, and more incidents of forced repatriation predicted higher proneness to psychopathology. However, contrary to our expectation, having a religious faith did not emerge as a salient protective factor. The current LPA results revealed distinct heterogeneous subgroups that previous research on the MMPI and MMPI-2 profiles of refugee populations overlooked with the assumption of a homogeneous sample. Clinical implications for the treatment of North Korean female refugees and the limitations of the study are discussed. (c) 2013 APA, all rights reserved.

Expendable launch vehicle studies

NASA Technical Reports Server (NTRS)

Bainum, Peter M.; Reiss, Robert

1995-01-01

Analytical support studies of expendable launch vehicles concentrate on the stability of the dynamics during launch especially during or near the region of maximum dynamic pressure. The in-plane dynamic equations of a generic launch vehicle with multiple flexible bending and fuel sloshing modes are developed and linearized. The information from LeRC about the grids, masses, and modes is incorporated into the model. The eigenvalues of the plant are analyzed for several modeling factors: utilizing diagonal mass matrix, uniform beam assumption, inclusion of aerodynamics, and the interaction between the aerodynamics and the flexible bending motion. Preliminary PID, LQR, and LQG control designs with sensor and actuator dynamics for this system and simulations are also conducted. The initial analysis for comparison of PD (proportional-derivative) and full state feedback LQR Linear quadratic regulator) shows that the split weighted LQR controller has better performance than that of the PD. In order to meet both the performance and robustness requirements, the H(sub infinity) robust controller for the expendable launch vehicle is developed. The simulation indicates that both the performance and robustness of the H(sub infinity) controller are better than that for the PID and LQG controllers. The modelling and analysis support studies team has continued development of methodology, using eigensensitivity analysis, to solve three classes of discrete eigenvalue equations. In the first class, the matrix elements are non-linear functions of the eigenvector. All non-linear periodic motion can be cast in this form. Here the eigenvector is comprised of the coefficients of complete basis functions spanning the response space and the eigenvalue is the frequency. The second class of eigenvalue problems studied is the quadratic eigenvalue problem. Solutions for linear viscously damped structures or viscoelastic structures can be reduced to this form. Particular attention is paid to Maxwell and Kelvin models. The third class of problems consists of linear eigenvalue problems in which the elements of the mass and stiffness matrices are stochastic. dynamic structural response for which the parameters are given by probabilistic distribution functions, rather than deterministic values, can be cast in this form. Solutions for several problems in each class will be presented.

Latent Profile Analysis and Conversion to Psychosis: Characterizing Subgroups to Enhance Risk Prediction.

PubMed

Healey, Kristin M; Penn, David L; Perkins, Diana; Woods, Scott W; Keefe, Richard S E; Addington, Jean

2018-02-15

Groups at clinical high risk (CHR) of developing psychosis are heterogeneous, composed of individuals with different clusters of symptoms. It is likely that there exist subgroups, each associated with different symptom constellations and probabilities of conversion. Present study used latent profile analysis (LPA) to ascertain subgroups in a combined sample of CHR (n = 171) and help-seeking controls (HSCs; n = 100; PREDICT study). Indicators in the LPA model included baseline Scale of Prodromal Symptoms (SOPS), Calgary Depression Scale for Schizophrenia (CDSS), and neurocognitive performance as measured by multiple instruments, including category instances (CAT). Subgroups were further characterized using covariates measuring demographic and clinical features. Three classes emerged: class 1 (mild, transition rate 5.6%), lowest SOPS and depression scores, intact neurocognitive performance; class 2 (paranoid-affective, transition rate 14.2%), highest suspiciousness, mild negative symptoms, moderate depression; and class 3 (negative-neurocognitive, transition rate 29.3%), highest negative symptoms, neurocognitive impairment, social cognitive impairment. Classes 2 and 3 evidenced poor social functioning. Results support a subgroup approach to research, assessment, and treatment of help-seeking individuals. Class 3 may be an early risk stage of developing schizophrenia.

Simple Heuristic Approach to Introduction of the Black-Scholes Model

ERIC Educational Resources Information Center

Yalamova, Rossitsa

2010-01-01

A heuristic approach to explaining of the Black-Scholes option pricing model in undergraduate classes is described. The approach draws upon the method of protocol analysis to encourage students to "think aloud" so that their mental models can be surfaced. It also relies upon extensive visualizations to communicate relationships that are…

Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

ERIC Educational Resources Information Center

Tirri, Henry; And Others

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated…

Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study

ERIC Educational Resources Information Center

Li, Ming; Harring, Jeffrey R.

2017-01-01

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more…

A Limitation of the Applicability of Interval Shift Analysis to Program Evaluation

ERIC Educational Resources Information Center

Hardy, Roy

1975-01-01

Interval Shift Analysis (ISA) is an adaptation of the linear programming model used to determine maximum benefits or minimal losses in quantifiable economics problems. ISA is applied to pre and posttest score distributions for 43 classes of second graders. (RC)

Latent class analysis of the feared situations of social anxiety disorder: A population-based study.

PubMed

Peyre, Hugo; Hoertel, Nicolas; Rivollier, Fabrice; Landman, Benjamin; McMahon, Kibby; Chevance, Astrid; Lemogne, Cédric; Delorme, Richard; Blanco, Carlos; Limosin, Frédéric

2016-12-01

Little is known about differences in mental health comorbidity and quality of life in individuals with social anxiety disorder (SAD) according to the number and the types of feared situations. Using a US nationally representative sample, the National Epidemiologic Survey on Alcohol and Related Conditions, we performed latent class analysis to compare the prevalence rates of mental disorders and quality of life measures across classes defined by the number and the types of feared social situations among individuals with SAD. Among the 2,448 participants with a lifetime diagnosis of SAD, we identified three classes of individuals who feared most social situations but differed in the number of feared social situations (generalized severe [N = 378], generalized moderate [N = 1,049] and generalized low [N = 443]) and a class of subjects who feared only performance situations [N = 578]. The magnitude of associations between each class and a wide range of mental disorders and quality of life measures were consistent with a continuum model, supporting that the deleterious effects of SAD on mental health may increase with the number of social situations feared. However, we found that individuals with the "performance only" specifier may constitute an exception to this model because these participants had significantly better mental health than other participants with SAD. Our findings give additional support to the recent changes made in the DSM-5, including the introduction of the "performance only" specifier and the removal of the "generalized" specifier to promote the dimensional approach of the number of social fears. © 2016 Wiley Periodicals, Inc.

Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis

NASA Astrophysics Data System (ADS)

Gao, Yizhu; Zhai, Xiaoming; Andersson, Björn; Zeng, Pingfei; Xin, Tao

2018-06-01

We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume, and Relative density on the basis of the prior level. Then, students gradually master Archimedes' theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.

Determination of Pain Phenotypes in Knee Osteoarthritis: A Latent Class Analysis using Data from the Osteoarthritis Initiative Study

PubMed Central

Kittelson, Andrew J.; Stevens-Lapsley, Jennifer E.; Schmiege, Sarah J.

2017-01-01

Objective Knee osteoarthritis (OA) is a broadly applied diagnosis that may encompass multiple subtypes of pain. The purpose of this study was to identify phenotypes of knee OA, using measures from the following pain-related domains: 1) knee OA pathology, 2) psychological distress, and 3) altered pain neurophysiology. Methods Data were selected from a total of 3494 participants at Visit #6 of the Osteoarthritis Initiative (OAI) study. Latent Class Analysis was applied to the following variables: radiographic OA severity, quadriceps strength, Body Mass Index (BMI), Charlson Comorbidity Index (CCI), Center for Epidemiologic Studies Depression subscale (CES-D), Coping Strategies Questionnaire-Catastrophizing subscale (CSQ-Cat), number of bodily pain sites, and knee joint tenderness at 4 sites. Resulting classes were compared on the following demographic and clinical factors: age, sex, pain severity, disability, walking speed, and use of arthritis-related healthcare. Results A four-class model was identified. Class 1 (4% of the study population) had higher CCI scores. Class 2 (24%) had higher knee joint sensitivity. Class 3 (10%) had greater psychological distress. Class 4 (62%) had lesser radiographic OA, little psychological involvement, greater strength, and less pain sensitivity. Additionally, Class 1 was the oldest, on average. Class 4 was the youngest, had the lowest disability, and least pain. Class 3 had the worst disability and most pain. Conclusions Four distinct pain phenotypes of knee OA were identified. Psychological factors, comorbidity status, and joint sensitivity appear to be important in defining phenotypes of knee OA-related pain. PMID:26414884

Determination of Pain Phenotypes in Knee Osteoarthritis: A Latent Class Analysis Using Data From the Osteoarthritis Initiative.

PubMed

Kittelson, Andrew J; Stevens-Lapsley, Jennifer E; Schmiege, Sarah J

2016-05-01

Knee osteoarthritis (OA) is a broadly applied diagnosis that may describe multiple subtypes of pain. The purpose of this study was to identify phenotypes of knee OA, using measures from the following pain-related domains: 1) knee OA pathology, 2) psychological distress, and 3) altered pain neurophysiology. Data were selected from a total of 3,494 participants at visit 6 of the Osteoarthritis Initiative study. Latent class analysis was applied to the following variables: radiographic OA severity, quadriceps strength, body mass index, the Charlson Comorbidity Index (CCI), the Center for Epidemiologic Studies Depression Scale, the Coping Strategies Questionnaire-Catastrophizing subscale, number of bodily pain sites, and knee joint tenderness at 4 sites. The resulting classes were compared on the following demographic and clinical factors: age, sex, pain severity, disability, walking speed, and use of arthritis-related health care. A 4-class model was identified. Class 1 (4% of the study population) had higher CCI scores. Class 2 (24%) had higher knee joint sensitivity. Class 3 (10%) had greater psychological distress. Class 4 (62%) had lesser radiographic OA, little psychological involvement, greater strength, and less pain sensitivity. Additionally, class 1 was the oldest, on average. Class 4 was the youngest, had the lowest disability, and least pain. Class 3 had the worst disability and most pain. Four distinct pain phenotypes of knee OA were identified. Psychological factors, comorbidity status, and joint sensitivity appear to be important in defining phenotypes of knee OA-related pain. © 2016, American College of Rheumatology.

Strategic Planning towards a World-Class University

NASA Astrophysics Data System (ADS)

Usoh, E. J.; Ratu, D.; Manongko, A.; Taroreh, J.; Preston, G.

2018-02-01

Strategic planning with a focus on world-class university status is an option that cannot be avoided by universities today to survive and succeed in competition as a provider of higher education. The objective of this research is to obtain exploratory research results on the strategic plans of universities that are prepared to generate world-class university status. This research utilised exploratory qualitative research method and data was collected by in-depth interviews method. Interview transcripts were analyzed by using thematic content analysis through NVivo software analysis and manual systems. The main finding of interview shows that most interviewees agreed that UNIMA has been engaged in strategic planning. Contribution from faculties and schools are acknowledged and inform the planning process. However, a new model of strategic planning should be adopted by UNIMA due to the shift towards a “corporate university”. The finding results from documents, literature review and interview were the addition of world-class university characteristics and features to current strategic planning of UNIMA and how to upgrade by considering to use the characteristics and features towards world-class university.

Substance Use, Violence, and Antiretroviral Adherence: A Latent Class Analysis of Women Living with HIV in Canada.

PubMed

Carter, Allison; Roth, Eric Abella; Ding, Erin; Milloy, M-J; Kestler, Mary; Jabbari, Shahab; Webster, Kath; de Pokomandy, Alexandra; Loutfy, Mona; Kaida, Angela

2018-03-01

We used latent class analysis to identify substance use patterns for 1363 women living with HIV in Canada and assessed associations with socio-economic marginalization, violence, and sub-optimal adherence to combination antiretroviral therapy (cART). A six-class model was identified consisting of: abstainers (26.3%), Tobacco Users (8.81%), Alcohol Users (31.9%), 'Socially Acceptable' Poly-substance Users (13.9%), Illicit Poly-substance Users (9.81%) and Illicit Poly-substance Users of All Types (9.27%). Multinomial logistic regression showed that women experiencing recent violence had significantly higher odds of membership in all substance use latent classes, relative to Abstainers, while those reporting sub-optimal cART adherence had higher odds of being members of the poly-substance use classes only. Factors significantly associated with Illicit Poly-substance Users of All Types were sexual minority status, lower income, and lower resiliency. Findings underline a need for increased social and structural supports for women who use substances to support them in leading safe and healthy lives with HIV.

Analysis of a waterborne disease model with socioeconomic classes.

PubMed

Collins, O C; Robertson, Suzanne L; Govinder, K S

2015-11-01

Waterborne diseases such as cholera continue to pose serious public health problems in the world today. Transmission parameters can vary greatly with socioeconomic class (SEC) and the availability of clean water. We formulate a multi-patch waterborne disease model such that each patch represents a particular SEC with its own water source, allowing individuals to move between SECs. For a 2-SEC model, we investigate the conditions under which each SEC is responsible for driving a cholera outbreak. We determine the effect of SECs on disease transmission dynamics by comparing the basic reproduction number of the 2-SEC model to that of a homogeneous model that does not take SECs into account. We conclude by extending several results of the 2-SEC model to an n-SEC model. Copyright © 2015 Elsevier Inc. All rights reserved.

Experimental analysis of bidirectional reflectance distribution function cross section conversion term in direction cosine space.

PubMed

Butler, Samuel D; Nauyoks, Stephen E; Marciniak, Michael A

2015-06-01

Of the many classes of bidirectional reflectance distribution function (BRDF) models, two popular classes of models are the microfacet model and the linear systems diffraction model. The microfacet model has the benefit of speed and simplicity, as it uses geometric optics approximations, while linear systems theory uses a diffraction approach to compute the BRDF, at the expense of greater computational complexity. In this Letter, nongrazing BRDF measurements of rough and polished surface-reflecting materials at multiple incident angles are scaled by the microfacet cross section conversion term, but in the linear systems direction cosine space, resulting in great alignment of BRDF data at various incident angles in this space. This results in a predictive BRDF model for surface-reflecting materials at nongrazing angles, while avoiding some of the computational complexities in the linear systems diffraction model.

Development of guidelines for the definition of the relavant information content in data classes

NASA Technical Reports Server (NTRS)

Schmitt, E.

1973-01-01

The problem of experiment design is defined as an information system consisting of information source, measurement unit, environmental disturbances, data handling and storage, and the mathematical analysis and usage of data. Based on today's concept of effective computability, general guidelines for the definition of the relevant information content in data classes are derived. The lack of a universally applicable information theory and corresponding mathematical or system structure is restricting the solvable problem classes to a small set. It is expected that a new relativity theory of information, generally described by a universal algebra of relations will lead to new mathematical models and system structures capable of modeling any well defined practical problem isomorphic to an equivalence relation at any corresponding level of abstractness.

Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.

PubMed

Klijn, Sven L; Weijenberg, Matty P; Lemmens, Paul; van den Brandt, Piet A; Lima Passos, Valéria

2017-10-01

Background and objective Group-based trajectory modelling is a model-based clustering technique applied for the identification of latent patterns of temporal changes. Despite its manifold applications in clinical and health sciences, potential problems of the model selection procedure are often overlooked. The choice of the number of latent trajectories (class-enumeration), for instance, is to a large degree based on statistical criteria that are not fail-safe. Moreover, the process as a whole is not transparent. To facilitate class enumeration, we introduce a graphical summary display of several fit and model adequacy criteria, the fit-criteria assessment plot. Methods An R-code that accepts universal data input is presented. The programme condenses relevant group-based trajectory modelling output information of model fit indices in automated graphical displays. Examples based on real and simulated data are provided to illustrate, assess and validate fit-criteria assessment plot's utility. Results Fit-criteria assessment plot provides an overview of fit criteria on a single page, placing users in an informed position to make a decision. Fit-criteria assessment plot does not automatically select the most appropriate model but eases the model assessment procedure. Conclusions Fit-criteria assessment plot is an exploratory, visualisation tool that can be employed to assist decisions in the initial and decisive phase of group-based trajectory modelling analysis. Considering group-based trajectory modelling's widespread resonance in medical and epidemiological sciences, a more comprehensive, easily interpretable and transparent display of the iterative process of class enumeration may foster group-based trajectory modelling's adequate use.

GeoGebra Assist Discovery Learning Model for Problem Solving Ability and Attitude toward Mathematics

NASA Astrophysics Data System (ADS)

Murni, V.; Sariyasa, S.; Ardana, I. M.

2017-09-01

This study aims to describe the effet of GeoGebra utilization in the discovery learning model on mathematical problem solving ability and students’ attitude toward mathematics. This research was quasi experimental and post-test only control group design was used in this study. The population in this study was 181 of students. The sampling technique used was cluster random sampling, so the sample in this study was 120 students divided into 4 classes, 2 classes for the experimental class and 2 classes for the control class. Data were analyzed by using one way MANOVA. The results of data analysis showed that the utilization of GeoGebra in discovery learning can lead to solving problems and attitudes towards mathematics are better. This is because the presentation of problems using geogebra can assist students in identifying and solving problems and attracting students’ interest because geogebra provides an immediate response process to students. The results of the research are the utilization of geogebra in the discovery learning can be applied in learning and teaching wider subject matter, beside subject matter in this study.

Development Trajectories and Predictors of the Role Commitment of Nursing Preceptors.

PubMed

Wang, Wei-Fang; Hung, Chich-Hsiu; Li, Chung-Yi

2018-06-01

The commitment of nursing preceptors to their role is an important driving force that supports their clinical teaching and affects teaching quality. Role commitment undergoes dynamic development and thus changes over time. Existing studies have utilized only cross-sectional study designs and have not analyzed the changes in commitment trajectories with related factors. This study aimed to investigate the development trajectories of the commitment of preceptors and to examine the predictors between the trajectories of role commitment among nursing preceptors. A single-group, repeated-measures design was adopted, and 59 participants completed the Commitment to the Preceptor Role Scale and the Preceptor's Perception of Support Scale. The latent class growth analysis method was used to estimate the trajectory class patterns. The Wilcoxon rank-sum test, a nonparametric method, was used to compare the differences in demographic characteristics between the trajectories of commitment among nursing preceptors. Predictors were examined using binary logistic regression analysis. The two-class model was the best-fitting model to describe the trajectories of nursing preceptor commitment. The two classes in this model were "low commitment," which accounted for 90.3% of all the participants, and "high commitment," which accounted for 9.7%. A significant difference was found between the two classes in terms of motivation for being a preceptor (p = .048). Neither demographic characteristics nor organizational support had a predictive effect on the trajectories of commitment development. This study found a low level of role commitment among new preceptors. Moreover, internal motivation was found to be a significant factor affecting the trajectories of this commitment. Therefore, institutions should foster an appropriate environment to enhance the role identity of preceptors as well as cultivate and stimulate their commitment to this role.

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

PubMed Central

Andersson, Claes R; Hvidsten, Torgeir R; Isaksson, Anders; Gustafsson, Mats G; Komorowski, Jan

2007-01-01

Background We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes. PMID:17939860

The Role of Individual Correlates and Class Norms in Defending and Passive Bystanding Behavior in Bullying: A Multilevel Analysis

ERIC Educational Resources Information Center

Pozzoli, Tiziana; Gini, Gianluca; Vieno, Alessio

2012-01-01

This study investigates possible individual and class correlates of defending and passive bystanding behavior in bullying, in a sample of 1,825 Italian primary school (mean age = 10 years 1 month) and middle school (mean age = 13 years 2 months) students. The findings of a series of multilevel regression models show that both individual (e.g.,…

What Do Test Score Really Mean? A Latent Class Analysis of Danish Test Score Performance

ERIC Educational Resources Information Center

McIntosh, James; Munk, Martin D.

2014-01-01

Latent class Poisson count models are used to analyse a sample of Danish test score results from a cohort of individuals born in 1954-1955, tested in 1968, and followed until 2011. The procedure takes account of unobservable effects as well as excessive zeros in the data. We show that the test scores measure manifest or measured ability as it has…

Attachment in psychosis: A latent profile analysis of attachment styles and association with symptoms in a large psychosis cohort.

PubMed

Bucci, Sandra; Emsley, Richard; Berry, Katherine

2017-01-01

Attachment has been identified as one of various possible mechanisms involved in understanding models of psychosis, but measures that reliably and validly assess attachment styles in psychosis are limited. The aim of this study was to identify attachment patterns in psychosis and examine demographic and clinical correlates across attachment groups. Latent profile analysis on attachment data from 588 participants who met criteria for non-affective psychosis was used to classify people into attachment classes. Four latent classes of attachment were identified: secure, insecure-anxious, insecure-avoidant and disorganised. Secure attachment was the most common attachment style, suggesting that a significant number of clients with psychosis are inherently resilient. Disorganised attachment was associated with a higher proportion of sexual and physical abuse and more severe positive symptoms compared to other attachment classes. This is not only the largest study to examine attachment styles, their demographic and clinical profile, and the clinical profile of disorganised attachment more specifically, in psychosis, but also the first study to use a validated self-report measure of attachment in psychosis to identify four classes of attachment style. Findings advance developmental models of attachment and psychosis; participants with disorganised attachment report more frequent trauma history and more severe psychotic symptoms. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Dissociative Subtype of Posttraumatic Stress Disorder (PTSD) Among Adolescents: Co-Occurring PTSD, Depersonalization/Derealization, and Other Dissociation Symptoms.

PubMed

Choi, Kristen R; Seng, Julia S; Briggs, Ernestine C; Munro-Kramer, Michelle L; Graham-Bermann, Sandra A; Lee, Robert C; Ford, Julian D

2017-12-01

The purpose of this study was to examine the co-occurrence of posttraumatic stress disorder (PTSD) and dissociation in a clinical sample of trauma-exposed adolescents by evaluating evidence for the depersonalization/derealization dissociative subtype of PTSD as defined by the DSM-5 and then examining a broader set of dissociation symptoms. A sample of treatment-seeking, trauma-exposed adolescents 12 to 16 years old (N = 3,081) from the National Child Traumatic Stress Network Core Data Set was used to meet the study objectives. Two models of PTSD/dissociation co-occurrence were estimated using latent class analysis, one with 2 dissociation symptoms and the other with 10 dissociation symptoms. After model selection, groups within each model were compared on demographics, trauma characteristics, and psychopathology. Model A, the depersonalization/derealization model, had 5 classes: dissociative subtype/high PTSD; high PTSD; anxious arousal; dysphoric arousal; and a low symptom/reference class. Model B, the expanded dissociation model, identified an additional class characterized by dissociative amnesia and detached arousal. These 2 models provide new information about the specific ways PTSD and dissociation co-occur and illuminate some differences between adult and adolescent trauma symptom expression. A dissociative subtype of PTSD can be distinguished from PTSD alone in adolescents, but assessing a wider range of dissociative symptoms is needed to fully characterize adolescent traumatic stress responses. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Stochastic process approximation for recursive estimation with guaranteed bound on the error covariance

NASA Technical Reports Server (NTRS)

Menga, G.

1975-01-01

An approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.

Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method.

PubMed

Takahashi, Hiro; Kobayashi, Takeshi; Honda, Hiroyuki

2005-01-15

For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to find selectively significant genes for constructing the prognostic model and it is also necessary to eliminate non-specific genes or genes with error before constructing the model. We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3 and 70.0% or the predictor with NSC was 88.2 and 90.0%, respectively. The results have proven that PART was superior for gene screening. The software is available upon request from the authors. honda@nubio.nagoya-u.ac.jp

Fundamental differences between optimization code test problems in engineering applications

NASA Technical Reports Server (NTRS)

Eason, E. D.

1984-01-01

The purpose here is to suggest that there is at least one fundamental difference between the problems used for testing optimization codes and the problems that engineers often need to solve; in particular, the level of precision that can be practically achieved in the numerical evaluation of the objective function, derivatives, and constraints. This difference affects the performance of optimization codes, as illustrated by two examples. Two classes of optimization problem were defined. Class One functions and constraints can be evaluated to a high precision that depends primarily on the word length of the computer. Class Two functions and/or constraints can only be evaluated to a moderate or a low level of precision for economic or modeling reasons, regardless of the computer word length. Optimization codes have not been adequately tested on Class Two problems. There are very few Class Two test problems in the literature, while there are literally hundreds of Class One test problems. The relative performance of two codes may be markedly different for Class One and Class Two problems. Less sophisticated direct search type codes may be less likely to be confused or to waste many function evaluations on Class Two problems. The analysis accuracy and minimization performance are related in a complex way that probably varies from code to code. On a problem where the analysis precision was varied over a range, the simple Hooke and Jeeves code was more efficient at low precision while the Powell code was more efficient at high precision.

Consumption Patterns of Nightlife Attendees in Munich: A Latent-Class Analysis.

PubMed

Hannemann, Tessa-Virginia; Kraus, Ludwig; Piontek, Daniela

2017-09-19

The affinity for substance use among patrons of nightclubs has been well established. With novel psychoactive substances (NPS) quickly emerging on the European drug market, trends, and patterns of use are potentially changing. (1) The detection of subgroups of consumers in the electronic dance music scene of a major German metropolitan city, (2) describing the consumption patterns of these subgroups, (3) exploring the prevalence and type of NPS consumption in this population at nightlife events in Munich. A total of 1571 patrons answered questions regarding their own substance use and the emergence of NPS as well as their experience with these substances. A latent class analysis was employed to detect consumption patterns within the sample. A four class model was determined reflecting different consumption patterns: the conservative class (34.9%) whose substance was limited to cannabis; the traditional class (36.6%) which especially consumed traditional club drugs; the psychedelic class (17.5%) which, in addition to traditional club drugs also consumed psychedelic drugs; and an unselective class (10.9%) which displayed the greatest likelihood of consumption of all assessed drugs. "Smoking mixtures" and methylone were the new substances mentioned most often, the number of substances mentioned differed between latent classes. Specific strategies are needed to reduce harm in those displaying the riskiest substance use. Although NPS use is still a fringe phenomenon its prevalence is greater in this subpopulation than in the general population, especially among users in the high-risk unselective class.

Patterns of Adolescent Sexual Behavior Predicting Young Adult Sexually Transmitted Infections: A Latent Class Analysis Approach

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Butera, Nicole M.; Lanza, Stephanie T.

2014-01-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity. PMID:24449152

Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: a latent class analysis approach.

PubMed

Vasilenko, Sara A; Kugler, Kari C; Butera, Nicole M; Lanza, Stephanie T

2015-04-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity.

Dynamical analysis on f(R, G) cosmology

NASA Astrophysics Data System (ADS)

Santos da Costa, S.; Roig, F. V.; Alcaniz, J. S.; Capozziello, S.; De Laurentis, M.; Benetti, M.

2018-04-01

We use a dynamical system approach to study the cosmological viability of f(R, G) gravity theories. The method consists of formulating the evolution equations as an autonomous system of ordinary differential equations, using suitable variables. The formalism is applied to a class of models in which f(R, G)\\propto RnG1-n and its solutions and corresponding stability are analysed in detail. New accelerating solutions that can be attractors in the phase space are found. We also find that this class of models does not exhibit a matter-dominated epoch, a solution which is inconsistent with current cosmological observations.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Exploring product supply across age classes and forest types

Treesearch

Robert C. Abt; Karen J. Lee; Gerardo Pacheco

1995-01-01

Timber supply modeling has evolved from examining inventory sustainability based on growth/drain relationships to sophisticated inventory and supply models. These analyses have consistently recognized regional, ownership (public/private), and species group (hardwood/softwood) differences. Recognition of product differences is fundamental to market analysis which...

Facilitating hydrological data analysis workflows in R: the RHydro package

NASA Astrophysics Data System (ADS)

Buytaert, Wouter; Moulds, Simon; Skoien, Jon; Pebesma, Edzer; Reusser, Dominik

2015-04-01

The advent of new technologies such as web-services and big data analytics holds great promise for hydrological data analysis and simulation. Driven by the need for better water management tools, it allows for the construction of much more complex workflows, that integrate more and potentially more heterogeneous data sources with longer tool chains of algorithms and models. With the scientific challenge of designing the most adequate processing workflow comes the technical challenge of implementing the workflow with a minimal risk for errors. A wide variety of new workbench technologies and other data handling systems are being developed. At the same time, the functionality of available data processing languages such as R and Python is increasing at an accelerating pace. Because of the large diversity of scientific questions and simulation needs in hydrology, it is unlikely that one single optimal method for constructing hydrological data analysis workflows will emerge. Nevertheless, languages such as R and Python are quickly gaining popularity because they combine a wide array of functionality with high flexibility and versatility. The object-oriented nature of high-level data processing languages makes them particularly suited for the handling of complex and potentially large datasets. In this paper, we explore how handling and processing of hydrological data in R can be facilitated further by designing and implementing a set of relevant classes and methods in the experimental R package RHydro. We build upon existing efforts such as the sp and raster packages for spatial data and the spacetime package for spatiotemporal data to define classes for hydrological data (HydroST). In order to handle simulation data from hydrological models conveniently, a HM class is defined. Relevant methods are implemented to allow for an optimal integration of the HM class with existing model fitting and simulation functionality in R. Lastly, we discuss some of the design challenges of the RHydro package, including integration with big data technologies, web technologies, and emerging data models in hydrology.

Implementation of cooperative learning model type STAD with RME approach to understanding of mathematical concept student state junior high school in Pekanbaru

NASA Astrophysics Data System (ADS)

Nurhayati, Dian Mita; Hartono

2017-05-01

This study aims to determine whether there is a difference in the ability of understanding the concept of mathematics between students who use cooperative learning model Student Teams Achievement Division type with Realistic Mathematic Education approach and students who use regular learning in seventh grade SMPN 35 Pekanbaru. This study was quasi experiments with Posttest-only Control Design. The populations in this research were all the seventh grade students in one of state junior high school in Pekanbaru. The samples were a class that is used as the experimental class and one other as the control class. The process of sampling is using purposive sampling technique. Retrieval of data in this study using the documentation, observation sheets, and test. The test use t-test formula to determine whether there is a difference in student's understanding of mathematical concepts. Before the t-test, should be used to test the homogeneity and normality. Based in the analysis of these data with t0 = 2.9 there is a difference in student's understanding of mathematical concepts between experimental and control class. Percentage of students experimental class with score more than 65 was 76.9% and 56.4% of students control class. Thus be concluded, the ability of understanding mathematical concepts students who use the cooperative learning model type STAD with RME approach better than students using the regular learning. So that cooperative learning model type STAD with RME approach is well used in learning process.

Combined risk assessment of nonstationary monthly water quality based on Markov chain and time-varying copula.

PubMed

Shi, Wei; Xia, Jun

2017-02-01

Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH 3 -N) and permanganate index (COD Mn ) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH 3 -N and COD Mn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class V w , Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH 3 -N and COD Mn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH 3 -N and COD Mn is inferior to class V and class IV water quality standards, respectively.

Influence of Problem-Based Learning Model of Learning to the Mathematical Communication Ability of Students of Grade XI IPA SMAN 14 Padang

NASA Astrophysics Data System (ADS)

Nisa, I. M.

2018-04-01

The ability of mathematical communication is one of the goals of learning mathematics expected to be mastered by students. However, reality in the field found that the ability of mathematical communication the students of grade XI IPA SMA Negeri 14 Padang have not developed optimally. This is evident from the low test results of communication skills mathematically done. One of the factors that causes this happens is learning that has not been fully able to facilitate students to develop mathematical communication skills well. By therefore, to improve students' mathematical communication skills required a model in the learning activities. One of the models learning that can be used is Problem Based learning model Learning (PBL). The purpose of this study is to see whether the ability the students' mathematical communication using the PBL model better than the students' mathematical communication skills of the learning using conventional learning in Class XI IPA SMAN 14 Padang. This research type is quasi experiment with design Randomized Group Only Design. Population in this research that is student of class XI IPA SMAN 14 Padang with sample class XI IPA 3 and class XI IPA 4. Data retrieval is done by using communication skill test mathematically shaped essay. To test the hypothesis used U-Mann test Whitney. Based on the results of data analysis, it can be concluded that the ability mathematical communication of students whose learning apply more PBL model better than the students' mathematical communication skills of their learning apply conventional learning in class XI IPA SMA 14 Padang at α = 0.05. This indicates that the PBL learning model effect on students' mathematical communication ability.

Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

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

Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrologicalmore » parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

2017-12-01

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

The Impact of Programming Experience on Successfully Learning Systems Analysis and Design

ERIC Educational Resources Information Center

Wong, Wang-chan

2015-01-01

In this paper, the author reports the results of an empirical study on the relationship between a student's programming experience and their success in a traditional Systems Analysis and Design (SA&D) class where technical skills such as dataflow analysis and entity relationship data modeling are covered. While it is possible to teach these…

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

PubMed

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

Coordination of knowledge in judging animated motion

NASA Astrophysics Data System (ADS)

Thaden-Koch, Thomas C.; Dufresne, Robert J.; Mestre, Jose P.

2006-12-01

Coordination class theory is used to explain college students’ judgments about animated depictions of moving objects. diSessa’s coordination class theory models a “concept” as a complex knowledge system that can reliably determine a particular type of information in widely varying situations. In the experiment described here, fifty individually interviewed college students judged the realism of two sets of computer animations depicting balls rolling on a pair of tracks. The judgments of students from an introductory physics class were strongly affected by the number of balls depicted (one or two), but the judgments of students from an educational psychology class were not. Coordination analysis of interview transcripts supports the interpretation that physics students’ developing physics knowledge led them to consistently miss or ignore some observations that the other students consistently paid attention to. The analysis highlights the context sensitivity and potential fragility of coordination systems, and leads to the conclusion that students’ developing knowledge systems might not necessarily result in consistently improving performance.

Characterizing Class-Specific Exposure-Viral Load Suppression Response of HIV Antiretrovirals Using A Model-Based Meta-Analysis.

PubMed

Xu, Y; Li, Y F; Zhang, D; Dockendorf, M; Tetteh, E; Rizk, M L; Grobler, J A; Lai, M-T; Gobburu, J; Ankrom, W

2016-08-01

We applied model-based meta-analysis of viral suppression as a function of drug exposure and in vitro potency for short-term monotherapy in human immunodeficiency virus type 1 (HIV-1)-infected treatment-naïve patients to set pharmacokinetic targets for development of nonnucleoside reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer inhibitors (InSTIs). We developed class-specific models relating viral load kinetics from monotherapy studies to potency normalized steady-state trough plasma concentrations. These models were integrated with a literature assessment of doses which demonstrated to have long-term efficacy in combination therapy, in order to set steady-state trough concentration targets of 6.17- and 2.15-fold above potency for NNRTIs and InSTIs, respectively. Both the models developed and the pharmacokinetic targets derived can be used to guide compound selection during preclinical development and to predict the dose-response of new antiretrovirals to inform early clinical trial design. © 2016 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Climate Modeling in the Calculus and Differential Equations Classroom

ERIC Educational Resources Information Center

Kose, Emek; Kunze, Jennifer

2013-01-01

Students in college-level mathematics classes can build the differential equations of an energy balance model of the Earth's climate themselves, from a basic understanding of the background science. Here we use variable albedo and qualitative analysis to find stable and unstable equilibria of such a model, providing a problem or perhaps a…

An Analysis of the Model and Enacted Curricula for a History of Science Course in a Nationwide Teacher Education Program

ERIC Educational Resources Information Center

Nouri, Noushin

2017-01-01

The UTeach program, a national model for undergraduate teacher preparation, includes "Perspectives on Science and Mathematics," a class designed to share content about the History of Science (HOS) with preservice teachers. UTeach provides a model curriculum as a sample for instructors teaching "Perspectives." The purpose of…

Colorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions.

PubMed

Mansfield, Carol; Ekwueme, Donatus U; Tangka, Florence K L; Brown, Derek S; Smith, Judith Lee; Guy, Gery P; Li, Chunyu; Hauber, Brett

2018-05-09

Screening rates for colorectal cancer are below the Healthy People 2020 goal. There are several colorectal cancer screening tests that differ in terms of accuracy, recommended frequency, and administration. In this article, we compare how a set of personal characteristics correlates with preferences for colorectal cancer screening test attributes, past colorectal cancer screening behavior, and future colorectal cancer screening intentions. We conducted a discrete-choice experiment survey to assess relative preferences for attributes of colorectal cancer screening tests among adults aged 50-75 years in USA. We used a latent class logit model to identify classes of preferences and calculated willingness to pay for changes in test attributes. A set of personal characteristics were included in the latent class analysis and analyses of self-reported past screening behavior and self-assessed likelihood of future colorectal cancer screening. Latent class analysis identified three types of respondents. Class 1 valued test accuracy, class 2 valued removing polyps and avoiding discomfort, and class 3 valued cost. Having had a prior colonoscopy and a higher income were predictors of the likelihood of future screening and membership in classes 1 and 2. Health insurance and a self-reported higher risk of developing colorectal cancer were associated with prior screening and higher future screening intentions, but not class membership. We identified distinct classes of preferences focusing on different test features and personal characteristics associated with reported behavior and intentions. Healthcare providers should engage in a careful assessment of patient preferences when recommending colorectal cancer test options to encourage colorectal cancer screening uptake.

Four Distinct Subgroups of Self-Injurious Behavior among Chinese Adolescents: Findings from a Latent Class Analysis

PubMed Central

Xin, Xiuhong; Ming, Qingsen; Zhang, Jibiao; Wang, Yuping; Liu, Mingli; Yao, Shuqiao

2016-01-01

Self-injurious behavior (SIB) among adolescents is an important public health issue worldwide. It is still uncertain whether homogeneous subgroups of SIB can be identified and whether constellations of SIBs can co-occur due to the high heterogeneity of these behaviors. In this study, a cross-sectional study was conducted on a large school-based sample and latent class analysis was performed (n = 10,069, mean age = 15 years) to identify SIB classes based on 11 indicators falling under direct SIB (DSIB), indirect SIB (ISIB), and suicide attempts (SAs). Social and psychological characteristics of each subgroup were examined after controlling for age and gender. Results showed that a four-class model best fit the data and each class had a distinct pattern of co-occurrence of SIBs and external measures. Class 4 (the baseline/normative group, 65.3%) had a low probability of SIB. Class 3 (severe SIB group, 3.9%) had a high probability of SIB and the poorest social and psychological status. Class 1 (DSIB+SA group, 14.2%) had similar scores for external variables compared to class 3, and included a majority of girls [odds ratio (OR) = 1.94]. Class 2 (ISIB group, 16.6%) displayed moderate endorsement of ISIB items, and had a majority of boys and older adolescents (OR = 1.51). These findings suggest that SIB is a heterogeneous entity, but it may be best explained by four homogenous subgroups that display quantitative and qualitative differences. Findings in this study will improve our understanding on SIB and may facilitate the prevention and treatment of SIB. PMID:27392132

Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

NASA Astrophysics Data System (ADS)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

Prevalence and co-occurrence of addictive behaviors among former alternative high school youth: A longitudinal follow-up study

PubMed Central

Sussman, Steve; Pokhrel, Pallav; Sun, Ping; Rohrbach, Louise A.; Spruijt-Metz, Donna

2015-01-01

Background and Aims Recent work has studied addictions using a matrix measure, which taps multiple addictions through single responses for each type. This is the first longitudinal study using a matrix measure. Methods We investigated the use of this approach among former alternative high school youth (average age = 19.8 years at baseline; longitudinal n = 538) at risk for addictions. Lifetime and last 30-day prevalence of one or more of 11 addictions reviewed in other work was the primary focus (i.e., cigarettes, alcohol, hard drugs, shopping, gambling, Internet, love, sex, eating, work, and exercise). These were examined at two time-points one year apart. Latent class and latent transition analyses (LCA and LTA) were conducted in Mplus. Results Prevalence rates were stable across the two time-points. As in the cross-sectional baseline analysis, the 2-class model (addiction class, non-addiction class) fit the data better at follow-up than models with more classes. Item-response or conditional probabilities for each addiction type did not differ between time-points. As a result, the LTA model utilized constrained the conditional probabilities to be equal across the two time-points. In the addiction class, larger conditional probabilities (i.e., 0.40−0.49) were found for love, sex, exercise, and work addictions; medium conditional probabilities (i.e., 0.17−0.27) were found for cigarette, alcohol, other drugs, eating, Internet and shopping addiction; and a small conditional probability (0.06) was found for gambling. Discussion and Conclusions Persons in an addiction class tend to remain in this addiction class over a one-year period. PMID:26551909

What Doesn't Work for Whom? Exploring Heterogeneity in Responsiveness to the Family Check-Up in Early Childhood Using a Mixture Model Approach.

PubMed

Pelham, William E; Dishion, Thomas J; Tein, Jenn-Yun; Shaw, Daniel S; Wilson, Melvin N

2017-11-01

This study applied latent class analysis to a family-centered prevention trial in early childhood to identify subgroups of families with differential responsiveness to the Family Check-Up (FCU) intervention. The sample included 731 families with 2-year-olds randomized to the FCU or control condition and followed through age 5 with yearly follow-up assessments. A two-step mixture model was used to examine whether specific constellations of family characteristics at age 2 (baseline) were related to intervention response across ages 3, 4, and 5. The first step empirically identified latent classes of families based on several family risk and adjustment variables selected on the basis of previous research. The second step modeled the effect of the FCU on longitudinal change in children's problem behavior in each of the empirically derived latent classes. Results suggested a five-class solution, where a significant intervention effect of moderate to large size was observed in one of the five classes-the class characterized by child neglect, legal problems, and parental mental health issues. Pairwise comparisons revealed that the intervention effect was significantly greater in this class of families than in two other classes that were generally less at risk for the development of child disruptive behavior problems, albeit still low-income. Thus, findings suggest that (a) the FCU is most successful in reducing child problem behavior in more highly distressed, low-income families, and (b) the FCU may have little impact for relatively low-risk, low-income families. Future directions include the development of a brief screening process that can triage low-income families into groups that should be targeted for intervention, redirected to other services, monitored prospectively, or left alone.

Tachyon inflation in the large-N formalism

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

Barbosa-Cendejas, Nandinii; De-Santiago, Josue; German, Gabriel

2015-11-01

We study tachyon inflation within the large-N formalism, which takes a prescription for the small Hubble flow slow-roll parameter ε{sub 1} as a function of the large number of e-folds N. This leads to a classification of models through their behaviour at large N. In addition to the perturbative N class, we introduce the polynomial and exponential classes for the ε{sub 1} parameter. With this formalism we reconstruct a large number of potentials used previously in the literature for tachyon inflation. We also obtain new families of potentials from the polynomial class. We characterize the realizations of tachyon inflation bymore » computing the usual cosmological observables up to second order in the Hubble flow slow-roll parameters. This allows us to look at observable differences between tachyon and canonical single field inflation. The analysis of observables in light of the Planck 2015 data shows the viability of some of these models, mostly for certain realization of the polynomial and exponential classes.« less

Treatment of skeletal Class III malocclusions: orthognathic surgery or orthodontic camouflage? How to decide.

PubMed

Benyahia, Hicham; Azaroual, Mohamed Faouzi; Garcia, Claude; Hamou, Edith; Abouqal, Redouane; Zaoui, Fatima

2011-06-01

The choice of treatment in adult skeletal Class III occlusions often poses a particularly tricky problem for the orthodontist. Faced with the option of either orthodontic camouflage or orthognathic surgery, the clinician's clinical experience is of paramount importance, especially in borderline cases. The aim of our study was to uncover a guide model enabling the practitioner to distinguish between skeletal Class III cases which can be suitably treated with orthodontics and those requiring orthognathic surgery. The lateral headfilms of 47 adult patients exhibiting skeletal Class III occlusions were analyzed. The orthodontic group comprised 22 patients and the surgical group 25. Twenty-seven linear, proportional and angular measurements were scrutinized. Stepwise discriminant analysis was used to identify the dentoskeletal and esthetic variables which most distinguished the two groups. The Holdaway angle was chosen to differentiate between patients prior to treatment. This model enables us to classify 87.2% of patients correctly. Copyright © 2011 CEO. Published by Elsevier Masson SAS. All rights reserved.

Internet messenger based smart virtual class learning using ubiquitous computing

NASA Astrophysics Data System (ADS)

Umam, K.; Mardi, S. N. S.; Hariadi, M.

2017-06-01

Internet messenger (IM) has become an important educational technology component in college education, IM makes it possible for students to engage in learning and collaborating at smart virtual class learning (SVCL) using ubiquitous computing. However, the model of IM-based smart virtual class learning using ubiquitous computing and empirical evidence that would favor a broad application to improve engagement and behavior are still limited. In addition, the expectation that IM based SVCL using ubiquitous computing could improve engagement and behavior on smart class cannot be confirmed because the majority of the reviewed studies followed instructions paradigms. This article aims to present the model of IM-based SVCL using ubiquitous computing and showing learners’ experiences in improved engagement and behavior for learner-learner and learner-lecturer interactions. The method applied in this paper includes design process and quantitative analysis techniques, with the purpose of identifying scenarios of ubiquitous computing and realize the impressions of learners and lecturers about engagement and behavior aspect and its contribution to learning

Subtyping depression by clinical features: the Australasian database.

PubMed

Parker, G; Roy, K; Hadzi-Pavlovic, D; Mitchell, P; Wilhelm, K; Menkes, D B; Snowdon, J; Loo, C; Schweitzer, I

2000-01-01

To distinguish psychotic, melancholic and a residual non-melancholic class on the basis of clinical features alone. Previous studies at our Mood Disorders Unit (MDU) favour a hierarchical model, with the classes able to be distinguished by two specific clinical features, but any such intramural study risks rater bias and requires external replication. This replication study involved 27 Australasian psychiatrist raters, thus extending the sample and raters beyond the MDU facility. They collected clinical feature data using a standardized assessment with precoded rating options. A psychotic depression (PD) class was derived by respecting DSM-IV decision rules while a cluster analysis distinguished melancholic (MEL) and non-melancholic classes. The MELs were distinguished virtually entirely by the presence of significant psychomotor disturbance (PMD), as rated by the observationally based CORE measure, with over-representation on only three of an extensive set of 'endogeneity symptoms'. In comparison to PMD, endogeneity symptoms appear to be poor indicators of 'melancholic' type, confounding typology with severity. Results again support the hierarchical model.

An Analysis of Conceptual Flow Patterns and Structures in the Physics Classroom

NASA Astrophysics Data System (ADS)

Eshach, Haim

2010-03-01

The aim of the current research is to characterize the conceptual flow processes occurring in whole-class dialogic discussions with a high level of interanimation; in the present case, of a high-school class learning about image creation on plane mirrors. Using detailed chains of interaction and conceptual flow discourse maps-both developed for the purpose of this research-the classroom discourse, audio-taped and transcribed verbatim, was analyzed and three discussion structures were revealed: accumulation around budding foci concepts, zigzag between foci concepts, and concept tower. These structures as well as two additional factors, suggest the Two-Space Model of the whole class discussion proposed in the present article. The two additional factors are: (1) the teacher intervention; and (2) the conceptual barriers observed among the students, namely, materialistic thinking, and the tendency to attribute "unique characteristics" to optical devices. This model might help teachers to prepare and conduct efficient whole-class discussions which accord with the social constructivist perspective of learning.

Classes of Physical Activity and Sedentary Behavior in 5th Grade Children

PubMed Central

Dowda, Marsha; Dishman, Rod K; Pate, Russell R.

2016-01-01

Objectives To identify classes of physical activity (PA) and sedentary behaviors (SB) in 5th grade children, associated factors, and trajectories of change into 7th grade. Methods This study included n=495 children (221 boys, 274 girls) who participated in the Transitions and Activity Changes in Kids (TRACK) Study. PA was assessed objectively and via self-report. Children, parents, and school administrators completed surveys to assess related factors. Latent class analysis, growth modeling, and adjusted multinomial logistic regression procedures were used to classify children based on self-reported PA and SB and examine associated factors. Results Three classes of behavior were identified: Class 1: Low PA/Low SB, Class 2: Moderate PA/High SB, and Class 3: High PA/High SB (boys) or Class 3: High PA (girls). Class 3 children had higher levels of self-efficacy (boys), and enjoyment, parental support, and physical activity equipment at home (girls). Class 2 boys and Class 3 girls did not experience decline in PA (accelerometer) over time. Conclusions Self-efficacy (boys) and home environment (girls) may play a role in shaping patterns of PA in children. Findings may help to inform future interventions to encourage children to meet national PA guidelines. PMID:27103414

A Better Blend

ERIC Educational Resources Information Center

Demski, Jennifer

2010-01-01

In May 2009, the US Department of Education released a meta-analysis of effectiveness studies of online, face-to-face, and blended learning models. The analysis found that online learning produced better student outcomes than face-to-face classes, and that blended learning offered an even "larger advantage" over face-to-face. The hybrid approach…

Exploration of the Maximum Entropy/Optimal Projection Approach to Control Design Synthesis for Large Space Structures.

DTIC Science & Technology

1985-02-01

Energy Analysis , a branch of dynamic modal analysis developed for analyzing acoustic vibration problems, its present stage of development embodies a...Maximum Entropy Stochastic Modelling and Reduced-Order Design Synthesis is a rigorous new approach to this class of problems. Inspired by Statistical

Childhood adversity profiles and adult psychopathology in a representative Northern Ireland study.

PubMed

McLafferty, Margaret; Armour, Cherie; McKenna, Aine; O'Neill, Siobhan; Murphy, Sam; Bunting, Brendan

2015-10-01

Childhood adversities are key aetiological factors in the onset and persistence of psychopathology. The aims of this study were to identify childhood adversity profiles, and investigate the relationship between the adversity classes and psychopathology in Northern Ireland. The study utilized data from the Northern Ireland Study of Health and Stress, an epidemiological survey (N=1986), which used the CIDI to examine mental health disorders and associated risk factors. Latent Class Analysis revealed 3 distinct typologies; a low risk class (n=1709; 86%), a poly-adversity class (n=122; 6.1%), and an economic adversity class (n=155; 7.8%). Logistic Regression models revealed that individuals in the economic adversity class had a heightened risk of anxiety and substance disorders, with individuals in the poly-adversity class more likely to have a range of mental health problems and suicidality. The findings indicate the importance of considering the impact of co-occurring childhood adversities when planning treatment, prevention, and intervention programmes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mechanical behavior of bulk direct composite versus block composite and lithium disilicate indirect Class II restorations by CAD-FEM modeling.

PubMed

Ausiello, Pietro; Ciaramella, Stefano; Fabianelli, Andrea; Gloria, Antonio; Martorelli, Massimo; Lanzotti, Antonio; Watts, David C

2017-06-01

To study the influence of resin based and lithium disilicate materials on the stress and strain distributions in adhesive class II mesio-occlusal-distal (MOD) restorations using numerical finite element analysis (FEA). To investigate the materials combinations in the restored teeth during mastication and their ability to relieve stresses. One 3D model of a sound lower molar and three 3D class II MOD cavity models with 95° cavity-margin-angle shapes were modelled. Different material combinations were simulated: model A, with a 10μm thick resin bonding layer and a resin composite bulk filling material; model B, with a 70μm resin cement with an indirect CAD-CAM resin composite inlay; model C, with a 70μm thick resin cement with an indirect lithium disilicate machinable inlay. To simulate polymerization shrinkage effects in the adhesive layers and bulk fill composite, the thermal expansion approach was used. Shell elements were employed for representing the adhesive layers. 3D solid CTETRA elements with four grid points were employed for modelling the food bolus and tooth. Slide-type contact elements were used between the tooth surface and food. A vertical occlusal load of 600 N was applied, and nodal displacements on the bottom cutting surfaces were constrained in all directions. All the materials were assumed to be isotropic and elastic and a static linear analysis was performed. Displacements were different in models A, B and C. Polymerization shrinkage hardly affected model A and mastication only partially affected mechanical behavior. Shrinkage stress peaks were mainly located marginally along the enamel-restoration interface at occlusal and mesio-distal sites. However, at the internal dentinal walls, stress distributions were critical with the highest maximum stresses concentrated in the proximal boxes. In models B and C, shrinkage stress was only produced by the 70μm thick resin layer, but the magnitudes depended on the Young's modulus (E) of the inlay materials. Model B mastication behavior (with E=20GPa) was similar to the sound tooth stress relief pattern. Model B internally showed differences from the sound tooth model but reduced maximum stresses than model A and partially than model C. Model C (with E=70GPa) behaved similarly to model B with well redistributed stresses at the occlusal margins and the lateral sides with higher stress concentrations in the proximal boxes. Models B and C showed a more favorable performance than model A with elastic biomechanics similar to the sound tooth model. Bulk filling resin composite with 1% linear polymerization shrinkage negatively affected the mechanical behavior of class II MOD restored teeth. Class II MOD direct resin composite showed greater potential for damage because of higher internal and marginal stress evolution during resin polymerization shrinkage. With a large class II MOD cavity an indirect composite or a lithium disilicate inlay restoration may provide a mechanical response close to that of a sound tooth. Copyright © 2017 The Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

Morphological analysis of Drosophila larval peripheral sensory neuron dendrites and axons using genetic mosaics.

PubMed

Karim, M Rezaul; Moore, Adrian W

2011-11-07

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic development and axonal wiring. Recently the dendritic arborization (DA) sensory neurons of the Drosophila larval peripheral nervous system (PNS) have become powerful genetic models in which to elucidate both general and class-specific mechanisms of neuron differentiation. There are four main DA neuron classes (I-IV)(1). They are named in order of increasing dendrite arbor complexity, and have class-specific differences in the genetic control of their differentiation(2-10). The DA sensory system is a practical model to investigate the molecular mechanisms behind the control of dendritic morphology(11-13) because: 1) it can take advantage of the powerful genetic tools available in the fruit fly, 2) the DA neuron dendrite arbor spreads out in only 2 dimensions beneath an optically clear larval cuticle making it easy to visualize with high resolution in vivo, 3) the class-specific diversity in dendritic morphology facilitates a comparative analysis to find key elements controlling the formation of simple vs. highly branched dendritic trees, and 4) dendritic arbor stereotypical shapes of different DA neurons facilitate morphometric statistical analyses. DA neuron activity modifies the output of a larval locomotion central pattern generator(14-16). The different DA neuron classes have distinct sensory modalities, and their activation elicits different behavioral responses(14,16-20). Furthermore different classes send axonal projections stereotypically into the Drosophila larval central nervous system in the ventral nerve cord (VNC)(21). These projections terminate with topographic representations of both DA neuron sensory modality and the position in the body wall of the dendritic field(7,22,23). Hence examination of DA axonal projections can be used to elucidate mechanisms underlying topographic mapping(7,22,23), as well as the wiring of a simple circuit modulating larval locomotion(14-17). We present here a practical guide to generate and analyze genetic mosaics(24) marking DA neurons via MARCM (Mosaic Analysis with a Repressible Cell Marker)(1,10,25) and Flp-out(22,26,27) techniques (summarized in Fig. 1).

Critical Thinking Skills of Students through Mathematics Learning with ASSURE Model Assisted by Software Autograph

NASA Astrophysics Data System (ADS)

Kristianti, Y.; Prabawanto, S.; Suhendra, S.

2017-09-01

This study aims to examine the ability of critical thinking and students who attain learning mathematics with learning model ASSURE assisted Autograph software. The design of this study was experimental group with pre-test and post-test control group. The experimental group obtained a mathematics learning with ASSURE-assisted model Autograph software and the control group acquired the mathematics learning with the conventional model. The data are obtained from the research results through critical thinking skills tests. This research was conducted at junior high school level with research population in one of junior high school student in Subang Regency of Lesson Year 2016/2017 and research sample of class VIII student in one of junior high school in Subang Regency for 2 classes. Analysis of research data is administered quantitatively. Quantitative data analysis was performed on the normalized gain level between the two sample groups using a one-way anova test. The results show that mathematics learning with ASSURE assisted model Autograph software can improve the critical thinking ability of junior high school students. Mathematical learning using ASSURE-assisted model Autograph software is significantly better in improving the critical thinking skills of junior high school students compared with conventional models.

Teaching Using Computer Games

ERIC Educational Resources Information Center

Miller, Lee Dee; Shell, Duane; Khandaker, Nobel; Soh, Leen-Kiat

2011-01-01

Computer games have long been used for teaching. Current reviews lack categorization and analysis using learning models which would help instructors assess the usefulness of computer games. We divide the use of games into two classes: game playing and game development. We discuss the Input-Process-Outcome (IPO) model for the learning process when…

Crash Models For Rural Intersections: Four-Lane By Two-Lane Stop-Controlled And Two-Lane By Two-Lane Signalized

DOT National Transportation Integrated Search

1999-10-01

This report describes the collection, analysis, and modeling of crash and roadway data for intersections on rural roads in California and Michigan for the years 1993-1995. Three classes of intersections are considered: (1) three-legged intersections ...

A comprehensive analytical model of rotorcraft aerodynamics and dynamics. Part 3: Program manual

NASA Technical Reports Server (NTRS)

Johnson, W.

1980-01-01

The computer program for a comprehensive analytical model of rotorcraft aerodynamics and dynamics is described. This analysis is designed to calculate rotor performance, loads, and noise; the helicopter vibration and gust response; the flight dynamics and handling qualities; and the system aeroelastic stability. The analysis is a combination of structural, inertial, and aerodynamic models that is applicable to a wide range of problems and a wide class of vehicles. The analysis is intended for use in the design, testing, and evaluation of rotors and rotorcraft and to be a basis for further development of rotary wing theories.

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

PubMed Central

BOYSAN, Murat

2014-01-01

Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis.

PubMed

Boysan, Murat

2014-09-01

There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson's product-moment correlation coefficients. The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism.

Latent class profile of psychiatric symptoms and treatment utilization in a sample of patients with co-occurring disorders.

PubMed

Villalobos-Gallegos, Luis; Marín-Navarrete, Rodrigo; Roncero, Calos; González-Cantú, Hugo

2017-01-01

To identify symptom-based subgroups within a sample of patients with co-occurring disorders (CODs) and to analyze intersubgroup differences in mental health services utilization. Two hundred and fifteen patients with COD from an addiction clinic completed the Symptom Checklist 90-Revised. Subgroups were determined using latent class profile analysis. Services utilization data were collected from electronic records during a 3-year span. The five-class model obtained the best fit (Bayesian information criteria [BIC] = 3,546.95; adjusted BIC = 3,363.14; bootstrapped likelihood ratio test p < 0.0001). Differences between classes were quantitative, and groups were labeled according to severity: mild (26%), mild-moderate (28.8%), moderate (18.6%), moderate-severe (17.2%), and severe (9.3%). A significant time by class interaction was obtained (chi-square [χ2[15

Evaluation of consumer satisfaction using the tetra-class model.

PubMed

Clerfeuille, Fabrice; Poubanne, Yannick; Vakrilova, Milena; Petrova, Guenka

2008-09-01

A number of studies have shown the importance of consumers' satisfaction toward pharmacy services. The measurement of patient satisfaction through different elements of services provided is challenging within the context of a dynamic economic environment. Patient satisfaction is the result of long-term established habits and expectations to the pharmacy as an institution. Few studies to date have attempted to discern whether these changes have led to increased patient satisfaction and loyalty, particularly within developing nations. The objective of this study was to evaluate the elements of the services provided in Bulgarian pharmacies and their contribution to consumer satisfaction using a tetra-class model. Three main hypotheses were tested in pharmacies to validate the model in the case of complex services. Additionally, the contribution of the different service elements to the clients' satisfaction was studied. The analysis was based on a survey of customers in central and district pharmacies in Sofia, Bulgaria. The data were analyzed through a correspondence analysis which was applied to the results of the 752 distributed questionnaires. It was observed that different dimensions of the pharmacies contribute uniquely to customer satisfaction, with consumer gender contributing greatly toward satisfaction, with type/location of pharmacy, consumer age, and educational degree also playing a part. The duration of time over which the consumers have been clients at a given pharmacy influences the subsequent service categorization. This research demonstrated that the tetra-class model is suitable for application in the pharmaceutical sector. The model results could be beneficial for both researchers and pharmacy managers.

Classifying GRB 170817A/GW170817 in a Fermi duration-hardness plane

NASA Astrophysics Data System (ADS)

Horváth, I.; Tóth, B. G.; Hakkila, J.; Tóth, L. V.; Balázs, L. G.; Rácz, I. I.; Pintér, S.; Bagoly, Z.

2018-03-01

GRB 170817A, associated with the LIGO-Virgo GW170817 neutron-star merger event, lacks the short duration and hard spectrum of a Short gamma-ray burst (GRB) expected from long-standing classification models. Correctly identifying the class to which this burst belongs requires comparison with other GRBs detected by the Fermi GBM. The aim of our analysis is to classify Fermi GRBs and to test whether or not GRB 170817A belongs—as suggested—to the Short GRB class. The Fermi GBM catalog provides a large database with many measured variables that can be used to explore gamma-ray burst classification. We use statistical techniques to look for clustering in a sample of 1298 gamma-ray bursts described by duration and spectral hardness. Classification of the detected bursts shows that GRB 170817A most likely belongs to the Intermediate, rather than the Short GRB class. We discuss this result in light of theoretical neutron-star merger models and existing GRB classification schemes. It appears that GRB classification schemes may not yet be linked to appropriate theoretical models, and that theoretical models may not yet adequately account for known GRB class properties. We conclude that GRB 170817A may not fit into a simple phenomenological classification scheme.

PONS2train: tool for testing the MLP architecture and local traning methods for runoff forecast

NASA Astrophysics Data System (ADS)

Maca, P.; Pavlasek, J.; Pech, P.

2012-04-01

The purpose of presented poster is to introduce the PONS2train developed for runoff prediction via multilayer perceptron - MLP. The software application enables the implementation of 12 different MLP's transfer functions, comparison of 9 local training algorithms and finally the evaluation the MLP performance via 17 selected model evaluation metrics. The PONS2train software is written in C++ programing language. Its implementation consists of 4 classes. The NEURAL_NET and NEURON classes implement the MLP, the CRITERIA class estimates model evaluation metrics and for model performance evaluation via testing and validation datasets. The DATA_PATTERN class prepares the validation, testing and calibration datasets. The software application uses the LAPACK, BLAS and ARMADILLO C++ linear algebra libraries. The PONS2train implements the first order local optimization algorithms: standard on-line and batch back-propagation with learning rate combined with momentum and its variants with the regularization term, Rprop and standard batch back-propagation with variable momentum and learning rate. The second order local training algorithms represents: the Levenberg-Marquardt algorithm with and without regularization and four variants of scaled conjugate gradients. The other important PONS2train features are: the multi-run, the weight saturation control, early stopping of trainings, and the MLP weights analysis. The weights initialization is done via two different methods: random sampling from uniform distribution on open interval or Nguyen Widrow method. The data patterns can be transformed via linear and nonlinear transformation. The runoff forecast case study focuses on PONS2train implementation and shows the different aspects of the MLP training, the MLP architecture estimation, the neural network weights analysis and model uncertainty estimation.

Who Should Be Targeted for the Prevention of Birth Defects? A Latent Class Analysis Based on a Large, Population-Based, Cross-Sectional Study in Shaanxi Province, Western China.

PubMed

Zhu, Zhonghai; Cheng, Yue; Yang, Wenfang; Li, Danyang; Yang, Xue; Liu, Danli; Zhang, Min; Yan, Hong; Zeng, Lingxia

2016-01-01

The wide range and complex combinations of factors that cause birth defects impede the development of primary prevention strategies targeted at high-risk subpopulations. Latent class analysis (LCA) was conducted to identify mutually exclusive profiles of factors associated with birth defects among women between 15 and 49 years of age using data from a large, population-based, cross-sectional study conducted in Shaanxi Province, western China, between August and October, 2013. The odds ratios (ORs) and 95% confidence intervals (CIs) of associated factors and the latent profiles of indicators of birth defects and congenital heart defects were computed using a logistic regression model. Five discrete subpopulations of participants were identified as follows: No folic acid supplementation in the periconceptional period (reference class, 21.37%); low maternal education level + unhealthy lifestyle (class 2, 39.75%); low maternal education level + unhealthy lifestyle + disease (class 3, 23.71%); unhealthy maternal lifestyle + advanced age (class 4, 4.71%); and multi-risk factor exposure (class 5, 10.45%). Compared with the reference subgroup, the other subgroups consistently had a significantly increased risk of birth defects (ORs and 95% CIs: class 2, 1.75 and 1.21-2.54; class 3, 3.13 and 2.17-4.52; class 4, 5.02 and 3.20-7.88; and class 5, 12.25 and 8.61-17.42, respectively). For congenital heart defects, the ORs and 95% CIs were all higher, and the magnitude of OR differences ranged from 1.59 to 16.15. A comprehensive intervention strategy targeting maternal exposure to multiple risk factors is expected to show the strongest results in preventing birth defects.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis

PubMed Central

Sipsma, Heather L; Falb, Kathryn L; Willie, Tiara; Bradley, Elizabeth H; Bienkowski, Lauren; Meerdink, Ned; Gupta, Jhumka

2015-01-01

Objective To examine patterns of conflict-related violence and intimate partner violence (IPV) and their associations with emotional distress among Congolese refugee women living in Rwanda. Design Cross-sectional study. Setting Two Congolese refugee camps in Rwanda. Participants 548 ever-married Congolese refugee women of reproductive age (15–49 years) residing in Rwanda. Primary outcome measure Our primary outcome was emotional distress as measured using the Self-Report Questionnaire-20 (SRQ-20). For analysis, we considered participants with scores greater than 10 to be experiencing emotional distress and participants with scores of 10 or less not to be experiencing emotional distress. Results Almost half of women (49%) reported experiencing physical, emotional or sexual violence during the conflict, and less than 10% of women reported experiencing of any type of violence after fleeing the conflict. Lifetime IPV was reported by approximately 22% of women. Latent class analysis derived four distinct classes of violence experiences, including the Low All Violence class, the High Violence During Conflict class, the High IPV class and the High Violence During and After Conflict class. In multivariate regression models, latent class was strongly associated with emotional distress. Compared with women in the Low All Violence class, women in the High Violence During and After Conflict class and women in the High Violence During Conflict had 2.7 times (95% CI 1.11 to 6.74) and 2.3 times (95% CI 1.30 to 4.07) the odds of experiencing emotional distress in the past 4 weeks, respectively. Furthermore, women in the High IPV class had a 4.7 times (95% CI 2.53 to 8.59) greater odds of experiencing emotional distress compared with women in the Low All Violence class. Conclusions Experiences of IPV do not consistently correlate with experiences of conflict-related violence, and women who experience high levels of IPV may have the greatest likelihood for poor mental health in conflict-affected settings. PMID:25908672

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Modelling the influence of total suspended solids on E. coli removal in river water.

PubMed

Qian, Jueying; Walters, Evelyn; Rutschmann, Peter; Wagner, Michael; Horn, Harald

2016-01-01

Following sewer overflows, fecal indicator bacteria enter surface waters and may experience different lysis or growth processes. A 1D mathematical model was developed to predict total suspended solids (TSS) and Escherichia coli concentrations based on field measurements in a large-scale flume system simulating a combined sewer overflow. The removal mechanisms of natural inactivation, UV inactivation, and sedimentation were modelled. For the sedimentation process, one, two or three particle size classes were incorporated separately into the model. Moreover, the UV sensitivity coefficient α and natural inactivation coefficient kd were both formulated as functions of TSS concentration. It was observed that the E. coli removal was predicted more accurately by incorporating two particle size classes. However, addition of a third particle size class only improved the model slightly. When α and kd were allowed to vary with the TSS concentration, the model was able to predict E. coli fate and transport at different TSS concentrations accurately and flexibly. A sensitivity analysis revealed that the mechanisms of UV and natural inactivation were more influential at low TSS concentrations, whereas the sedimentation process became more important at elevated TSS concentrations.

Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

NASA Astrophysics Data System (ADS)

Munahefi, D. N.; Waluya, S. B.; Rochmad

2018-03-01

The purpose of this research identified the effectiveness of Problem Based Learning (PBL) models based on Self Regulation Leaning (SRL) on the ability of mathematical creative thinking and analyzed the ability of mathematical creative thinking of high school students in solving mathematical problems. The population of this study was students of grade X SMA N 3 Klaten. The research method used in this research was sequential explanatory. Quantitative stages with simple random sampling technique, where two classes were selected randomly as experimental class was taught with the PBL model based on SRL and control class was taught with expository model. The selection of samples at the qualitative stage was non-probability sampling technique in which each selected 3 students were high, medium, and low academic levels. PBL model with SRL approach effectived to students’ mathematical creative thinking ability. The ability of mathematical creative thinking of low academic level students with PBL model approach of SRL were achieving the aspect of fluency and flexibility. Students of academic level were achieving fluency and flexibility aspects well. But the originality of students at the academic level was not yet well structured. Students of high academic level could reach the aspect of originality.

Patterns of Dating Violence Perpetration and Victimization in U.S. Young Adult Males and Females.

PubMed

Spencer, Rachael A; Renner, Lynette M; Clark, Cari Jo

2016-09-01

Dating violence (DV) is frequently reported by young adults in intimate relationships in the United States, but little is known about patterns of DV perpetration and victimization. In this study, we examined sexual and physical violence perpetration and victimization reported by young adults to determine how the violence patterns differ by sex and race/ethnicity. Data from non-Hispanic White, non-Hispanic Black, and Hispanic participants in Wave 3 of the National Longitudinal Study of Adolescent to Adult Health were analyzed. DV was assessed using responses to four questions focused on perpetration and four questions focused on victimization. The information on DV was taken from the most violent relationship reported by participants prior to Wave 3. Latent class analysis was first conducted separately by sex, adjusting for age, race/ethnicity, and financial stress, then by race/ethnicity, adjusting for age and financial stress. Relative model fit was established by comparing Bayesian Information Criteria (BIC), adjusted BIC, entropy, interpretability of latent classes, and certainty of latent class assignment for covariate-adjusted models. The results indicate that patterns of violence differed by sex and for females, by race/ethnicity. A three-class model was the best fit for males. For females, separate four-class models were parsimonious for White, Black, and Hispanic females. Financial stress was a significant predictor of violence classification for males and females and age predicted membership in White and Black female models. Variations in DV patterns by sex and race/ethnicity suggest the need for a more nuanced understanding of differences in DV. © The Author(s) 2015.

Population Analysis of Disabled Children by Departments in France

NASA Astrophysics Data System (ADS)

Meidatuzzahra, Diah; Kuswanto, Heri; Pech, Nicolas; Etchegaray, Amélie

2017-06-01

In this study, a statistical analysis is performed by model the variations of the disabled about 0-19 years old population among French departments. The aim is to classify the departments according to their profile determinants (socioeconomic and behavioural profiles). The analysis is focused on two types of methods: principal component analysis (PCA) and multiple correspondences factorial analysis (MCA) to review which one is the best methods for interpretation of the correlation between the determinants of disability (independent variable). The PCA is the best method for interpretation of the correlation between the determinants of disability (independent variable). The PCA reduces 14 determinants of disability to 4 axes, keeps 80% of total information, and classifies them into 7 classes. The MCA reduces the determinants to 3 axes, retains only 30% of information, and classifies them into 4 classes.

On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2011-01-01

Cognitive diagnostic models (CDMs) attempt to uncover latent skills or attributes that examinees must possess in order to answer test items correctly. The DINA (deterministic input, noisy "and") model is a popular CDM that has been widely used. It is shown here that a logistic version of the model can easily be fit with standard software for…

Are polychlorinated biphenyl residues adequately describe by aroclor mixture equivalents. Isomer-specific principal components analysis of such residues in fish and turtles

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

Schwartz, T.R.; Stalling, D.L.; Rice, C.L.

1987-01-01

Polychlorinated biphenyl (PCB) residues from fish and turtles were analyzed with SIMCA (Soft Independent Modeling of Class Analogy), a principal components analysis technique. A series of technical Aroclors were also analyzed to provide a reference data set for pattern recognition. Environmental PCB residues are often expressed in terms of relative Aroclor composition. In this work, we assessed the similarity of Aroclors to class models derived for fish and turtles to ascertain if the PCB residues in the samples could be described by an Aroclor or Aroclor mixture. Using PCA, we found that these samples could not be described by anmore » Aroclor or Aroclor mixture and that it would be inappropriate to report these samples as such. 18 references, 3 figures, 3 tables.« less

Rapid Erosion Modeling in a Western Kenya Watershed using Visible Near Infrared Reflectance, Classification Tree Analysis and 137Cesium.

PubMed

deGraffenried, Jeff B; Shepherd, Keith D

2009-12-15

Human induced soil erosion has severe economic and environmental impacts throughout the world. It is more severe in the tropics than elsewhere and results in diminished food production and security. Kenya has limited arable land and 30 percent of the country experiences severe to very severe human induced soil degradation. The purpose of this research was to test visible near infrared diffuse reflectance spectroscopy (VNIR) as a tool for rapid assessment and benchmarking of soil condition and erosion severity class. The study was conducted in the Saiwa River watershed in the northern Rift Valley Province of western Kenya, a tropical highland area. Soil 137 Cs concentration was measured to validate spectrally derived erosion classes and establish the background levels for difference land use types. Results indicate VNIR could be used to accurately evaluate a large and diverse soil data set and predict soil erosion characteristics. Soil condition was spectrally assessed and modeled. Analysis of mean raw spectra indicated significant reflectance differences between soil erosion classes. The largest differences occurred between 1,350 and 1,950 nm with the largest separation occurring at 1,920 nm. Classification and Regression Tree (CART) analysis indicated that the spectral model had practical predictive success (72%) with Receiver Operating Characteristic (ROC) of 0.74. The change in 137 Cs concentrations supported the premise that VNIR is an effective tool for rapid screening of soil erosion condition.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang

2018-02-21

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.

A Latent Class Analysis of Maternal Depressive Symptoms over 12 Years and Offspring Adjustment in Adolescence

PubMed Central

Campbell, Susan B.; Morgan-Lopez, Antonio A.; Cox, Martha J.; McLoyd, Vonnie C.

2009-01-01

We used data from the NICHD Study of Early Child Care and Youth Development and latent class analysis to model patterns of maternal depressive symptoms from infant age 1 month to the transition to adolescence (age 12), and then examined adolescent adjustment at age 15 as a function of the course and severity of maternal symptoms. We identified five latent classes of symptoms in these 1357 women while also taking into account sociodemographic measures: never depressed; stable subclinical; early-decreasing; moderately elevated; chronic. Women with few symptoms were more likely to be married, better educated, and in better physical health than women with more elevated symptoms. Family size and whether the pregnancy was planned also differentiated among classes. At age 15, adolescents whose mothers were in the chronic, elevated, and stable subclinical latent classes reported more internalizing and externalizing problems and acknowledged engaging in more risky behavior than did children of never-depressed mothers. Latent class differences in self-reported loneliness and dysphoria were also found. Finally, several significant interactions between sex and latent class suggested that girls whose mothers reported elevated symptoms of depression over time experienced more internalizing distress and dysphoric mood relative to their male counterparts. Discussion focuses on adolescent adjustment, especially among offspring whose mothers report stable symptoms of depression across their childhoods. PMID:19685946

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

Emami, Razieh; Mukohyama, Shinji; Namba, Ryo

Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost and/or gradient instabilities. In this work, we seek a new class of vector-driven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi de-Sitter solutions to be an attractor analogous to the standard slow-roll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models,more » we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.« less

Stable solutions of inflation driven by vector fields

NASA Astrophysics Data System (ADS)

Emami, Razieh; Mukohyama, Shinji; Namba, Ryo; Zhang, Ying-li

2017-03-01

Many models of inflation driven by vector fields alone have been known to be plagued by pathological behaviors, namely ghost and/or gradient instabilities. In this work, we seek a new class of vector-driven inflationary models that evade all of the mentioned instabilities. We build our analysis on the Generalized Proca Theory with an extension to three vector fields to realize isotropic expansion. We obtain the conditions required for quasi de-Sitter solutions to be an attractor analogous to the standard slow-roll one and those for their stability at the level of linearized perturbations. Identifying the remedy to the existing unstable models, we provide a simple example and explicitly show its stability. This significantly broadens our knowledge on vector inflationary scenarios, reviving potential phenomenological interests for this class of models.

Effect of digital problem-based learning cases on student learning outcomes in ophthalmology courses.

PubMed

Kong, Jun; Li, Xiaoyan; Wang, Youdong; Sun, Wei; Zhang, Jinsong

2009-09-01

To assess the impact of digital problem-based learning (PBL) cases on student learning in ophthalmology courses. Ninety students were randomly divided into 3 classes (30 students per class). The first class studied under a didactic model. The other 2 classes were divided into 6 groups (10 students per group) and received PBL teaching; 3 groups studied via cases presented in digital form and the others studied via paper-form cases. The results of theoretical and case analysis examinations were analyzed using the chi(2) test. Student performance on the interval practice was analyzed using the Kruskal-Wallis test. Questionnaires were used to evaluate student and facilitator perceptions. Students in the digital groups exhibited better performance in the practice procedures according to tutorial evaluations compared with the other groups (P < .05). The 2 PBL classes had significantly higher mean results of theoretical and case analysis examinations (P < .001), but there was no significant difference between the 2 PBL classes. Ninety-three percent of students in the digital groups (vs 73% in the paper groups) noted that the cases greatly stimulated their interest. Introducing PBL into ophthalmology could improve educational quality and effectiveness. Digital PBL cases stimulate interest and motivate students to further improve diagnosis and problem-handling skills.

Use of multiattribute utility theory for formulary management in a health system.

PubMed

Chung, Seonyoung; Kim, Sooyon; Kim, Jeongmee; Sohn, Kieho

2010-01-15

The application, utility, and flexibility of the multiattribute utility theory (MAUT) when used as a formulary decision methodology in a Korean medical center were evaluated. A drug analysis model using MAUT consisting of 10 steps was designed for two drug classes of dihydropyridine calcium channel blockers (CCBs) and angiotensin II receptor blockers (ARBs). These two drug classes contain the most diverse agents among cardiovascular drugs on Samsung Medical Center's drug formulary. The attributes identified for inclusion in the drug analysis model were effectiveness, safety, patient convenience, and cost, with relative weights of 50%, 30%, 10%, and 10%, respectively. The factors were incorporated into the model to quantify the contribution of each attribute. For each factor, a utility scale of 0-100 was established, and the total utility score for each alternative was calculated. An attempt was made to make the model adaptable to changing health care and regulatory circumstances. The analysis revealed amlodipine besylate to be an alternative agent, with the highest total utility score among the dihydropyridine CCBs, while barnidipine hydrochloride had the lowest score. For ARBs, losartan potassium had the greatest total utility score, while olmesartan medoxomil had the lowest. A drug analysis model based on the MAUT was successfully developed and used in making formulary decisions for dihydropyridine CCBs and ARBs for a Korean health system. The model incorporates sufficient utility and flexibility of a drug's attributes and can be used as an alternative decision-making tool for formulary management in health systems.

RooStatsCms: A tool for analysis modelling, combination and statistical studies

NASA Astrophysics Data System (ADS)

Piparo, D.; Schott, G.; Quast, G.

2010-04-01

RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search channels for new phenomena in High Energy Physics. It provides a variety of methods described in literature implemented as classes, whose design is oriented to the execution of multiple CPU intensive jobs on batch systems or on the Grid.

Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis.

PubMed

Mirnaghi, Fatemeh S; Soucy, Nicholas; Hollebone, Bruce P; Brown, Carl E

2018-05-19

The characterization of spilled petroleum products in an oil spill is necessary for identifying the spill source, selection of clean-up strategies, and evaluating potential environmental and ecological impacts. Existing standard methods for the chemical characterization of spilled oils are time-consuming due to the lengthy sample preparation for analysis. The main objective of this study is the development of a rapid screening method for the fingerprinting of spilled petroleum products using excitation/emission matrix (EEM) fluorescence spectroscopy, thereby delivering a preliminary evaluation of the petroleum products within hours after a spill. In addition, the developed model can be used for monitoring the changes of aromatic compositions of known spilled oils over time. This study involves establishing a fingerprinting model based on the composition of polycyclic and heterocyclic aromatic hydrocarbons (PAH and HAHs, respectively) of 130 petroleum products at different states of evaporative weathering. The screening model was developed using parallel factor analysis (PARAFAC) of a large EEM dataset. The significant fluorescing components for each sample class were determined. After which, through principal component analysis (PCA), the variation of scores of their modeled factors was discriminated based on the different classes of petroleum products. This model was then validated using gas chromatography-mass spectrometry (GC-MS) analysis. The rapid fingerprinting and the identification of unknown and new spilled oils occurs through matching the spilled product with the products of the developed model. Finally, it was shown that HAH compounds in asphaltene and resins contribute to ≥4-ring PAHs compounds in petroleum products. Copyright © 2018. Published by Elsevier Ltd.

The 21st century skills with model eliciting activities on linear program

NASA Astrophysics Data System (ADS)

Handajani, Septriana; Pratiwi, Hasih; Mardiyana

2018-04-01

Human resources in the 21st century are required to master various forms of skills, including critical thinking skills and problem solving. The teaching of the 21st century is a teaching that integrates literacy skills, knowledge, skills, attitudes, and mastery of ICT. This study aims to determine whether there are differences in the effect of applying Model Elliciting Activities (MEAs) that integrates 21st century skills, namely 4C and conventional learning to learning outcomes. This research was conducted at Vocational High School in the odd semester of 2017 and uses the experimental method. The experimental class is treated MEAs that integrates 4C skills and the control class is given conventional learning. Methods of data collection in this study using the method of documentation and test methods. The data analysis uses Z-test. Data obtained from experiment class and control class. The result of this study showed there are differences in the effect of applying MEAs that integrates 4C skills and conventional learning to learning outcomes. Classes with MEAs that integrates 4C skills give better learning outcomes than the ones in conventional learning classes. This happens because MEAs that integrates 4C skills can improved creativity skills, communication skills, collaboration skills, and problem-solving skills.

Clarifying Interpersonal Heterogeneity in Borderline Personality Disorder Using Latent Mixture Modeling

PubMed Central

Wright, Aidan G.C.; Hallquist, Michael N.; Morse, Jennifer Q.; Scott, Lori N.; Stepp, Stephanie D.; Nolf, Kimberly A.; Pilkonis, Paul A.

2013-01-01

Significant interpersonal impairment is a cardinal feature of borderline personality disorder (BPD). However, past research has demonstrated that the interpersonal profile associated with BPD varies across samples, evidence for considerable interpersonal heterogeneity. The current study used Inventory of Interpersonal Problems – Circumplex (IIP-C; Alden, Wiggins, & Pincus, 1990) scale scores to investigate interpersonal inhibitions and excesses in a large sample (N = 255) selected for significant borderline pathology. Results indicated that BPD symptom counts were unrelated to the primary dimensions of the IIP-C, but were related to generalized interpersonal distress. A latent class analysis clarified this finding by revealing six homogeneous interpersonal classes with prototypical profiles associated with Intrusive, Vindictive, Avoidant, Nonassertive, and moderate and severe Exploitable interpersonal problems. These classes differed in clinically relevant features (e.g., antisocial behaviors, self-injury, past suicide attempts). Findings are discussed in terms of the incremental clinical utility of the interpersonal circumplex model and the implications for developmental and nosological models of BPD. PMID:23514179

Effects of an Inverted Instructional Delivery Model on Achievement of Ninth-Grade Physical Science Honors Students

NASA Astrophysics Data System (ADS)

Howell, Donna

This mixed-methods action research study was designed to assess the achievement of ninth-grade Physical Science Honors students by analysis of pre and posttest data. In addition, perceptual data from students, parents, and the researcher were collected to form a complete picture of the flipped lecture format versus the traditional lecture format. The researcher utilized a 4MAT learning cycle in two Physical Science Honors classes. One of these classes was traditionally delivered with lecture-type activities taking place inside the classroom and homework-type activities taking place at home; the other inverted, or flipped, delivered with lecture-type activities taking place outside the classroom and homework-type activities taking place inside the classroom. Existing unit pre and posttests for both classes were analyzed for differences in academic achievement. At the completion of the units, the flipped class students and parents were surveyed, and student focus groups were convened to ascertain their perceptions of the flipped classroom delivery model. Statistical analysis of posttest data revealed that there is no significant difference between the traditional lecture delivery format and the flipped delivery format. Analysis of perceptual data revealed six themes that must be considered when deciding to flip the classroom: how to hold students accountable for viewing the at-home videos, accessibility of students to the required technology, technical considerations relating to the video production, comprehension of the material both during and after viewing the videos, pedagogy of the overall flipped method, and preference for the flipped method overall. Findings revealed that students, parents, and the researcher all had a preference for the flipped class format, provided the above issues are addressed. The flipped class format encourages students to become more responsible for their learning, and, in addition, students reported that the hands-on inquiry activities done in class aided them in learning the subject matter. It is recommended, however, that before instructors decide to flip the classroom, they ensure that all students have access to needed technology, that there is a plan in place for ensuring that the students actually view the assigned videos, that they have a way to create the videos and ensure adequate quality, and that some discussion is held in class after each assigned video to ensure comprehension of the material.

A Study of Pupil Control Ideology: A Person-Oriented Approach to Data Analysis

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2010-01-01

Responses of urban school teachers to the Pupil Control Ideology questionnaire were studied using Latent Class Analysis. The results of the analysis suggest that the best fitting model to the data is a two-cluster solution. In particular, the pupil control ideology of the sample delineates into two clusters of teachers, those with humanistic and…

Optical Variability of BL Lacertae During the Major Outburst of 1997

NASA Technical Reports Server (NTRS)

Ghosh, K. K.; Ramsey, B. D.; Sadun, A. C.; Soundararajaperumal, S.; Wang, J.

2000-01-01

We have undertaken an investigation of recent flux variability in BL Lac. We present optical observations taken over 22 nights documenting major as well as minor outbursts. This has been combined, for purposes of multifrequency analysis, with published X-ray and T-ray data taken for an additional single night, On two nights in particular, including the night of the X-ray observations, a major outburst of about a full magnitude of variation was recorded. All the data have been analyzed with theoretical models. Attempts were made to use synchrotron self-Compton and external Comptonization models to explain the data; however, both classes of models were found lacking. More satisfactory results were obtained using an analytical model proposed by Wang et al. that involves the evolution of synchrotron spectra in a homogeneous jet due to the injection of relativistic electrons, taking into account radiation losses during the outbursts. It is hoped that the results of this study of BL Lac, an archetype for the class of blazars in general, represent a more generic phenomenon applicable to the entire class.

Optical Variability of BL Lacertae During the Major Outburst of 1997

NASA Technical Reports Server (NTRS)

Ghosh, K. K.; Ramsey, B. D.; Sadun, A. C.; Soundarajaperumal, S.; Wang, J.

1999-01-01

We have undertaken an investigation of recent flux variability in BL Lac. We present optical observations taken over 12 nights documenting major as well as minor outbursts. This has been combined, for purposes of multifrequency analysis, with published X-ray and gamma ray data taken for an additional single night. On two nights in particular, which includes the night of the X-ray observations, a major outburst of about a full magnitude of variation was recorded. All the data have been analyzed with theoretical models. Attempts were made to use synchrotron self-Compton and external Comptonization models to explain the data; however both classes of models were found lacking. More satisfactory results were obtained using an analytical model proposed by Wang et al. that involves the evolution of synchrotron spectra in a homogeneous jet due to the injection of relativistic electrons, taking into account radiation losses during the outbursts. It is hoped that the results of this study of BL Lac, an archetype for the class of blazars in general, represent a more generic phenomenon applicable to the entire class.

Life sciences domain analysis model

PubMed Central

Freimuth, Robert R; Freund, Elaine T; Schick, Lisa; Sharma, Mukesh K; Stafford, Grace A; Suzek, Baris E; Hernandez, Joyce; Hipp, Jason; Kelley, Jenny M; Rokicki, Konrad; Pan, Sue; Buckler, Andrew; Stokes, Todd H; Fernandez, Anna; Fore, Ian; Buetow, Kenneth H

2012-01-01

Objective Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research. Materials and methods The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types. Results The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research. Discussion The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts. Conclusions The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science. PMID:22744959

Uncertainty analysis of least-cost modeling for designing wildlife linkages.

PubMed

Beier, Paul; Majka, Daniel R; Newell, Shawn L

2009-12-01

Least-cost models for focal species are widely used to design wildlife corridors. To evaluate the least-cost modeling approach used to develop 15 linkage designs in southern California, USA, we assessed robustness of the largest and least constrained linkage. Species experts parameterized models for eight species with weights for four habitat factors (land cover, topographic position, elevation, road density) and resistance values for each class within a factor (e.g., each class of land cover). Each model produced a proposed corridor for that species. We examined the extent to which uncertainty in factor weights and class resistance values affected two key conservation-relevant outputs, namely, the location and modeled resistance to movement of each proposed corridor. To do so, we compared the proposed corridor to 13 alternative corridors created with parameter sets that spanned the plausible ranges of biological uncertainty in these parameters. Models for five species were highly robust (mean overlap 88%, little or no increase in resistance). Although the proposed corridors for the other three focal species overlapped as little as 0% (mean 58%) of the alternative corridors, resistance in the proposed corridors for these three species was rarely higher than resistance in the alternative corridors (mean difference was 0.025 on a scale of 1 10; worst difference was 0.39). As long as the model had the correct rank order of resistance values and factor weights, our results suggest that the predicted corridor is robust to uncertainty. The three carnivore focal species, alone or in combination, were not effective umbrellas for the other focal species. The carnivore corridors failed to overlap the predicted corridors of most other focal species and provided relatively high resistance for the other focal species (mean increase of 2.7 resistance units). Least-cost modelers should conduct uncertainty analysis so that decision-makers can appreciate the potential impact of model uncertainty on conservation decisions. Our approach to uncertainty analysis (which can be called a worst-case scenario approach) is appropriate for complex models in which distribution of the input parameters cannot be specified.

An Analysis of Turkey's PISA 2015 Results Using Two-Level Hierarchical Linear Modelling

ERIC Educational Resources Information Center

Atas, Dogu; Karadag, Özge

2017-01-01

In the field of education, most of the data collected are multi-level structured. Cities, city based schools, school based classes and finally students in the classrooms constitute a hierarchical structure. Hierarchical linear models give more accurate results compared to standard models when the data set has a structure going far as individuals,…

Network inoculation: Heteroclinics and phase transitions in an epidemic model

NASA Astrophysics Data System (ADS)

Yang, Hui; Rogers, Tim; Gross, Thilo

2016-08-01

In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.

Symptoms of prolonged grief and posttraumatic stress following loss: A latent class analysis.

PubMed

Maccallum, Fiona; Bryant, Richard A

2018-04-01

Individuals vary in how they respond to bereavement. Those who experience poor bereavement outcomes often report symptoms from more than one diagnostic category. This study sought to identify groups of individuals who share similar patterns of prolonged grief disorder and posttraumatic stress disorder symptoms to determine whether these profiles are differentially related to negative appraisals thought to contribute to prolonged grief disorder and posttraumatic stress disorder symptomatology. Participants were 185 bereaved adults. Latent class analysis was used to identify subgroups of individuals who showed similar patterns of co-occurrence of prolonged grief disorder and posttraumatic stress disorder symptoms. Multinomial regression was used to examine the extent to which appraisal domains and sociodemographic and loss factors predicted class membership. Latent class analysis revealed three classes of participants: a low symptom group, a high prolonged grief disorder symptom group, and a high prolonged grief disorder and posttraumatic stress disorder symptom group. Membership of the prolonged grief disorder group and prolonged grief disorder and posttraumatic stress disorder group was predicted by higher mean negative self-related appraisals. Demographic and loss-related factors did not predict group membership. These findings have implications for understanding co-occurrence of prolonged grief disorder and posttraumatic stress disorder symptoms following bereavement. Findings are consistent with theoretical models highlighting the importance of negative self-related beliefs in prolonged grief disorder.

Uncertainty Quantification and Regional Sensitivity Analysis of Snow-related Parameters in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Badawy, B.; Fletcher, C. G.

2017-12-01

The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.

1/f Noise from nonlinear stochastic differential equations.

PubMed

Ruseckas, J; Kaulakys, B

2010-03-01

We consider a class of nonlinear stochastic differential equations, giving the power-law behavior of the power spectral density in any desirably wide range of frequency. Such equations were obtained starting from the point process models of 1/fbeta noise. In this article the power-law behavior of spectrum is derived directly from the stochastic differential equations, without using the point process models. The analysis reveals that the power spectrum may be represented as a sum of the Lorentzian spectra. Such a derivation provides additional justification of equations, expands the class of equations generating 1/fbeta noise, and provides further insights into the origin of 1/fbeta noise.

Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.

PubMed

Wang, Xinghu; Hong, Yiguang; Ji, Haibo

2016-07-01

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

Numerical analysis of the effect of the kind of activating agent and the impregnation ratio on the parameters of the microporous structure of the active carbons

NASA Astrophysics Data System (ADS)

Kwiatkowski, Mirosław

2015-09-01

The paper presents the results of the research on the application of the LBET class adsorption models with the fast multivariant identification procedure as a tool for analysing the microporous structure of the active carbons obtained by chemical activation using potassium and sodium hydroxides as an activator. The proposed technique of the fast multivariant fitting of the LBET class models to the empirical adsorption data was employed particularly to evaluate the impact of the used activator and the impregnation ratio on the obtained microporous structure of the carbonaceous adsorbents.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia.

PubMed

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-11-01

Scant information exists on PTSD symptoms and structure in youth from developing countries. We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Results suggest that PTSD is a continuously distributed latent trait.

Inequality and mortality: demographic hypotheses regarding advanced and peripheral capitalism.

PubMed

Gregory, J W; Piché, V

1983-01-01

This paper analyzes mortality differences between social classes and between advanced and peripheral regions of the world economy. The demographic analysis of mortality is integrated with the study of political economy, which emphasizes the entire process of social reproduction. As part of this dialectic model, both the struggle of the working class to improve health and the interest of capital in maximizing profits are examined. Data from Québec and Upper Volta are used to illustrate the hypothesis that substantially higher mortality rates exist for the working class compared with the bourgeoisie and in the less developed peripheral regions compared with the more developed regions.

Application of CAD/CAE class systems to aerodynamic analysis of electric race cars

NASA Astrophysics Data System (ADS)

Grabowski, L.; Baier, A.; Buchacz, A.; Majzner, M.; Sobek, M.

2015-11-01

Aerodynamics is one of the most important factors which influence on every aspect of a design of a car and car driving parameters. The biggest influence aerodynamics has on design of a shape of a race car body, especially when the main objective of the race is the longest distance driven in period of time, which can not be achieved without low energy consumption and low drag of a car. Designing shape of the vehicle body that must generate the lowest possible drag force, without compromising the other parameters of the drive. In the article entitled „Application of CAD/CAE class systems to aerodynamic analysis of electric race cars” are being presented problems solved by computer analysis of cars aerodynamics and free form modelling. Analysis have been subjected to existing race car of a Silesian Greenpower Race Team. On a basis of results of analysis of existence of Kammback aerodynamic effect innovative car body were modeled. Afterwards aerodynamic analysis were performed to verify existence of aerodynamic effect for innovative shape and to recognize aerodynamics parameters of the shape. Analysis results in the values of coefficients and aerodynamic drag forces. The resulting drag forces Fx, drag coefficients Cx(Cd) and aerodynamic factors Cx*A allowed to compare all of the shapes to each other. Pressure distribution, air velocities and streams courses were useful in determining aerodynamic features of analyzed shape. For aerodynamic tests was used Ansys Fluent CFD software. In a paper the ways of surface modeling with usage of Realize Shape module and classic surface modeling were presented. For shapes modeling Siemens NX 9.0 software was used. Obtained results were used to estimation of existing shapes and to make appropriate conclusions.

University Rankings, Global Models, and Emerging Hegemony: Critical Analysis from Japan

ERIC Educational Resources Information Center

Ishikawa, Mayumi

2009-01-01

The study analyzes how the emergence of dominant models in higher education and power they embody affect non-Western, non-English language universities such as those in Japan. Based on extended micro-level participant observation in a Japanese research university aspiring to become a "world-class" institution, their struggles and the…

Differentiated Rates of Growth across Preschool Dual Language Learners

ERIC Educational Resources Information Center

Lambert, Richard G.; Kim, Do-Hong; Durham, Sean; Burts, Diane C.

2017-01-01

This study illustrates why preschool children who are dual language learners (DLLs) are not a homogeneous group. An empirically developed model of preschool DLL subgroups, based on latent class analysis, was presented. The model reflects three separate subgroups of DLL children present in many classrooms where DLL children are served: Bilinguals,…

Latent Class Analysis of Antisocial Behavior: Interaction of Serotonin Transporter Genotype and Maltreatment

PubMed Central

Li, James J.

2010-01-01

To improve understanding about genetic and environmental influences on antisocial behavior (ASB), we tested the association of the 44-base pair polymorphism of the serotonin transporter gene (5-HTTLPR) and maltreatment using latent class analysis in 2,488 boys and girls from Wave 1 of the National Longitudinal Study of Adolescent Health. In boys, ASB was defined by three classes (Exclusive Covert, Mixed Covert and Overt, and No Problems) whereas in girls, ASB was defined by two classes (Exclusive Covert, No Problems). In boys, 5-HTTLPR and maltreatment were not significantly related to ASB. However, in girls, maltreatment, but not 5-HTTLPR, was significantly associated with ASB. A significant interaction between 5-HTTLPR and maltreatment was also observed, where maltreated girls homozygous for the short allele were 12 times more likely to be classified in the Exclusive Covert group than in the No Problems group. Structural differences in the latent structure of ASB at Wave 2 and Wave 3 prevented repeat LCA modeling. However, using counts of ASB, 5-HTTLPR, maltreatment, and its interaction were unrelated to overt and covert ASB at Wave 2 and only maltreatment was related to covert ASB at Wave 3. We discuss these findings within the context of sex differences in ASB and relevant models of gene-environment interplay across developmental periods. PMID:20405199

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

USGS Publications Warehouse

Danielson, Thomas J.; Loftin, Cyndy; Tsomides, Leonidas; DiFranco, Jeanne L.; Connors, Beth; Courtemanch, David L.; Drummond, Francis; Davies, Susan

2012-01-01

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

Comparison of the occlusal contact area of virtual models and actual models: a comparative in vitro study on Class I and Class II malocclusion models.

PubMed

Lee, Hyemin; Cha, Jooly; Chun, Youn-Sic; Kim, Minji

2018-06-19

The occlusal registration of virtual models taken by intraoral scanners sometimes shows patterns which seem much different from the patients' occlusion. Therefore, this study aims to evaluate the accuracy of virtual occlusion by comparing virtual occlusal contact area with actual occlusal contact area using a plaster model in vitro. Plaster dental models, 24 sets of Class I models and 20 sets of Class II models, were divided into a Molar, Premolar, and Anterior group. The occlusal contact areas calculated by the Prescale method and the virtual occlusion by scanning method were compared, and the ratio of the molar and incisor area were compared in order to find any particular tendencies. There was no significant difference between the Prescale results and the scanner results in both the molar and premolar groups (p = 0.083 and 0.053, respectively). On the other hand, there was a significant difference between the Prescale and the scanner results in the anterior group with the scanner results presenting overestimation of the occlusal contact points (p < 0.05). In Molars group, the regression analysis shows that the two variables express linear correlation and has a linear equation with a slope of 0.917. R 2 is 0.930. Groups of Premolars and Anteriors had a week linear relationship and greater dispersion. Difference between the actual and virtual occlusion revealed in the anterior portion, where overestimation was observed in the virtual model obtained from the scanning method. Nevertheless, molar and premolar areas showed relatively accurate occlusal contact area in the virtual model.

Putting Bourdieu to work for class analysis: reflections on some recent contributions.

PubMed

Flemmen, Magne

2013-06-01

Recent developments in class analysis, particularly associated with so-called 'cultural class analysis'; have seen the works of Pierre Bourdieu take centre stage. Apart from the general influence of 'habitus' and 'cultural capital', some scholars have tried to reconstruct class analysis with concepts drawn from Bourdieu. This involves a theoretical reorientation, away from the conventional concerns of class analysis with property and market relations, towards an emphasis on the multiple forms of capital. Despite the significant potential of these developments, such a reorientation dismisses or neglects the relations of power and domination founded in the economic institutions of capitalism as a crucial element of what class is. Through a critique of some recent attempts by British authors to develop a 'Bourdieusian' class theory, the paper reasserts the centrality of the relations of power and domination that used to be the domain of class analysis. The paper suggests some elements central to a reworked class analysis that benefits from the power of Bourdieu's ideas while retaining a perspective on the fundamentals of class relations in capitalism. © London School of Economics and Political Science 2013.

Structural equation modeling in environmental risk assessment.

PubMed

Buncher, C R; Succop, P A; Dietrich, K N

1991-01-01

Environmental epidemiology requires effective models that take individual observations of environmental factors and connect them into meaningful patterns. Single-factor relationships have given way to multivariable analyses; simple additive models have been augmented by multiplicative (logistic) models. Each of these steps has produced greater enlightenment and understanding. Models that allow for factors causing outputs that can affect later outputs with putative causation working at several different time points (e.g., linkage) are not commonly used in the environmental literature. Structural equation models are a class of covariance structure models that have been used extensively in economics/business and social science but are still little used in the realm of biostatistics. Path analysis in genetic studies is one simplified form of this class of models. We have been using these models in a study of the health and development of infants who have been exposed to lead in utero and in the postnatal home environment. These models require as input the directionality of the relationship and then produce fitted models for multiple inputs causing each factor and the opportunity to have outputs serve as input variables into the next phase of the simultaneously fitted model. Some examples of these models from our research are presented to increase familiarity with this class of models. Use of these models can provide insight into the effect of changing an environmental factor when assessing risk. The usual cautions concerning believing a model, believing causation has been proven, and the assumptions that are required for each model are operative.

Prevalence of and Factors Associated With Minimal Hepatic Encephalopathy in Patients With Cirrhosis of Liver.

PubMed

Bale, Abhijith; Pai, C Ganesh; Shetty, Shiran; Balaraju, Girisha; Shetty, Anurag

2018-06-01

Minimal hepatic encephalopathy (MHE), though highly prevalent, is a frequently underdiagnosed complication of cirrhosis of the liver. Because lack of time is reported as the major reason for non-testing, identifying patients at high risk of MHE would help in targeting them for screening. We aimed to determine the factors associated with MHE to help identify patient subgroups with a higher risk of MHE for targeted screening. Patients with cirrhosis of liver presenting between April 2015 and November 2016 were included. Those with a Psychometric Hepatic Encephalopathy Score (PHES) of ≤-5 points on psychometric testing were diagnosed to have MHE. Various demographic, clinical and laboratory parameters were included in a univariate and later multiple logistic regression models. Of the 180 (male = 166, 92.2%) patients included 94 (52.2%) had MHE. Though serum albumin, serum total bilirubin, serum aspartate aminotransferase, international normalized ration, Child-Turcotte-Pugh and Model-For-End-Stage-Liver-Disease scores were significant on univariate analysis, only CTP score was found to be significantly associated with MHE ( P  = 0.002) on multivariate analysis. A higher CTP class was associated with a higher risk of the presence of MHE. The Odds ratio for having MHE was higher with CTP classes of B ( P  ≤ 0.001) and C ( P  ≤ 0.001) compared to class A. MHE is a common complication in patients with cirrhosis of liver and higher CTP scores independently predict the presence of MHE. Patients with CTP class B and C have a higher risk of suffering from MHE than CTP class A. Screening of patients in CTP class B and C is likely to increase the MHE detection rates while saving time, although select CTP class A patients may also need screening in view of public safety or poor quality of life.

Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations

NASA Astrophysics Data System (ADS)

Yira, Yacouba; Diekkrüger, Bernd; Steup, Gero; Yaovi Bossa, Aymar

2017-04-01

This study evaluates climate change impacts on water resources using an ensemble of six regional climate models (RCMs)-global climate models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project.

After evaluation of the historical runs of the climate models' ensemble, a statistical bias correction (empirical quantile mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs-GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components.

The mean hydrological and climate variables for two periods (1971-2000 and 2021-2050) were compared to assess the potential impact of climate change on water resources up to the middle of the 21st century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCM-GCM ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021-2050; (iii) the applied bias correction method only affected the magnitude of the climate change signal; (iv) individual climate models results lead to opposite discharge change signals; and (v) the results for the RCM-GCM ensemble are too uncertain to give any clear direction for future hydrological development. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. A water-energy budget analysis provides further insight into the behavior of the catchment.

Grain growth in Class I protostar Per-emb-50: a dust continuum analysis with NOEMA & SMA .

NASA Astrophysics Data System (ADS)

Agurto-Gangas, C.; Pineda, J. E.; Testi, L.; Caselli, P.; Szucs, L.; Tazzari, M.; Dunham, M.; Stephens, I. W.; Miotello, A.

A good understanding of when dust grains grow from sub-micrometer to millimeter sizes occurs is crucial for models of planet formation. This provides the first step towards the production of pebbles and planetesimals in protoplanetary disks. Thanks to detailed studies of the spectral index in Class II disks, it is well established that Class II objects have already dust grains of millimetres sizes, however, it is not clear when in the star formation process this grain growth occurs. Here, we present interferometric data from NOEMA at 3 mm and SMA at 1.3 mm of the Class I protostar, Per-emb-50, to determine the flux density spectral index at mm-wavelengths of the unresolved disk and the surrounding envelope. We find a spectral index in the unresolved disk 30% smaller than the envelope, alpha env=2.18, comparable to values obtained toward Class 0 sources.

General Blending Models for Data From Mixture Experiments

PubMed Central

Brown, L.; Donev, A. N.; Bissett, A. C.

2015-01-01

We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online. PMID:26681812

Heterogeneity in the pharmacodynamics of two long-acting methylphenidate formulations for children with attention deficit/hyperactivity disorder. A growth mixture modelling analysis.

PubMed

Sonuga-Barke, Edmund J S; Van Lier, Pol; Swanson, James M; Coghill, David; Wigal, Sharon; Vandenberghe, Mieke; Hatch, Simon

2008-06-01

To use growth mixture modelling (GMM) to identify subgroups of children with attention deficit hyperactive disorder (ADHD) who have different pharmacodynamic profiles in response to extended release methylphenidate as assessed in a laboratory classroom setting. GMM analysis was performed on data from the COMACS study (Comparison of Methylphenidates in the Analog Classroom Setting): a large (n = 184) placebo-controlled cross-over study comparing three treatment conditions in the Laboratory School Protocol (with a 1.5-h cycle of attention and deportment assessments). Two orally administered, once-daily methylphenidate (MPH) bioequivalent formulations [Metadate CD/Equasym XL (MCD-EQXL) and Concerta XL (CON)] were compared with placebo (PLA). Three classes of children with distinct severity profiles in the PLA condition were identified. For both MCD-EQXL and CON, the more severe their PLA symptoms the better, the children's response. However, the formulations produced different growth curves by class, with CON having essentially a flat profile for all three classes (i.e. no effect of PLA severity) and MCD-EQXL showing a marked decline in symptoms immediately post-dosing in the two most severe classes compared with the least severe. Comparison of daily doses matched for immediate-release (IR) components accounted for this difference. The results suggest considerable heterogeneity in the pharmacodynamics of MPH response by children with ADHD. When treatment response for near-equal, bioequivalent daily doses the two formulations was compared, marked differences were seen for children in the most severe classes with a strong curvilinear trajectory for MCD-EQXL related to the greater IR component.

Modeling Of Object- And Scene-Prototypes With Hierarchically Structured Classes

NASA Astrophysics Data System (ADS)

Ren, Z.; Jensch, P.; Ameling, W.

1989-03-01

The success of knowledge-based image analysis methodology and implementation tools depends largely on an appropriately and efficiently built model wherein the domain-specific context information about and the inherent structure of the observed image scene have been encoded. For identifying an object in an application environment a computer vision system needs to know firstly the description of the object to be found in an image or in an image sequence, secondly the corresponding relationships between object descriptions within the image sequence. This paper presents models of image objects scenes by means of hierarchically structured classes. Using the topovisual formalism of graph and higraph, we are currently studying principally the relational aspect and data abstraction of the modeling in order to visualize the structural nature resident in image objects and scenes, and to formalize. their descriptions. The goal is to expose the structure of image scene and the correspondence of image objects in the low level image interpretation. process. The object-based system design approach has been applied to build the model base. We utilize the object-oriented programming language C + + for designing, testing and implementing the abstracted entity classes and the operation structures which have been modeled topovisually. The reference images used for modeling prototypes of objects and scenes are from industrial environments as'well as medical applications.

Robustness analysis of non-ordinary Petri nets for flexible assembly/disassembly processes based on structural decomposition

NASA Astrophysics Data System (ADS)

Hsieh, Fu-Shiung

2011-03-01

Design of robust supervisory controllers for manufacturing systems with unreliable resources has received significant attention recently. Robustness analysis provides an alternative way to analyse a perturbed system to quickly respond to resource failures. Although we have analysed the robustness properties of several subclasses of ordinary Petri nets (PNs), analysis for non-ordinary PNs has not been done. Non-ordinary PNs have weighted arcs and have the advantage to compactly model operations requiring multiple parts or resources. In this article, we consider a class of flexible assembly/disassembly manufacturing systems and propose a non-ordinary flexible assembly/disassembly Petri net (NFADPN) model for this class of systems. As the class of flexible assembly/disassembly manufacturing systems can be regarded as the integration and interactions of a set of assembly/disassembly subprocesses, a bottom-up approach is adopted in this article to construct the NFADPN models. Due to the routing flexibility in NFADPN, there may exist different ways to accomplish the tasks. To characterise different ways to accomplish the tasks, we propose the concept of completely connected subprocesses. As long as there exists a set of completely connected subprocesses for certain type of products, the production of that type of products can still be maintained without requiring the whole NFADPN to be live. To take advantage of the alternative routes without enforcing liveness for the whole system, we generalise the concept of persistent production proposed to NFADPN. We propose a condition for persistent production based on the concept of completely connected subprocesses. We extend robustness analysis to NFADPN by exploiting its structure. We identify several patterns of resource failures and characterise the conditions to maintain operation in the presence of resource failures.

Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models

NASA Astrophysics Data System (ADS)

Giesen, Joachim; Mueller, Klaus; Taneva, Bilyana; Zolliker, Peter

Conjoint analysis is a family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual's or a population's preferences on a class of options that can be described by parameters and their levels. We consider preference data obtained in choice-based conjoint analysis studies, where one observes test persons' choices on small subsets of the options. There are many ways to analyze choice-based conjoint analysis data. Here we discuss the intuition behind a classification based approach, and compare this approach to one based on statistical assumptions (discrete choice models) and to a regression approach. Our comparison on real and synthetic data indicates that the classification approach outperforms the discrete choice models.

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

PubMed

Tenzer, S; Peters, B; Bulik, S; Schoor, O; Lemmel, C; Schatz, M M; Kloetzel, P-M; Rammensee, H-G; Schild, H; Holzhütter, H-G

2005-05-01

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

Education within Sustainable Development: Critical Thinking Formation on ESL Class

NASA Astrophysics Data System (ADS)

Pevneva, Inna; Gavrishina, Olga; Smirnova, Anna; Rozhneva, Elena; Yakimova, Nataliya

2017-11-01

The article is devoted to consideration of the critical thinking formation in course of foreign language teaching within the education for sustainable development as a crucial skill of perspective employee and a future leader of Russian employment market. The necessity to include the component of problem education and critical thinking methodology in course of the foreign language class is justified along with analysis of the basic principles of critical thinking and certain strategies that can be implied in class. This model targets communicative language competences of students as well as critical thinking due to interconnection of various types of cognitive activities in class. The role in personality development of the students is considered along with the formation and enhancing of critical thinking skills within the modern personality-oriented approach.

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial.

PubMed

Oliveri, Paolo

2017-08-22

Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers. Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes. Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Solar flare induced cosmic noise absorption

NASA Astrophysics Data System (ADS)

Ogunmodimu, Olugbenga; Honary, Farideh; Rogers, Neil; Falayi, E. O.; Bolaji, O. S.

2018-06-01

Solar flare events are a major observing emphasis for space weather because they affect the ionosphere and can eject high-energy particles that can adversely affect Earth's technologies. In this study we model 38.2 MHz cosmic noise absorption (CNA) by utilising measurements from the Imaging Riometer for Ionospheric Studies (IRIS) at Kilpisjärvi, Finland obtained during solar cycle 23 (1996-2009). We utilised X-ray archive for the same period from the Geostationary Operational Environmental Satellite (GOES) to study solar flare induced cosmic noise absorption. We identified the threshold of flare (M4 class) that could bear significant influence on CNA. Through epoch analysis, we show the magnitude of absorption that each class of flare could produce. Using the parameters of flare and absorption we present a model that could provide the basis for nowcast of CNA induced by M and X-class solar flares.

Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models.

PubMed

Nakajima, Kenichi; Nakata, Tomoaki; Matsuo, Shinro; Jacobson, Arnold F

2016-10-01

(123)I meta-iodobenzylguanidine (MIBG) imaging has been extensively used for prognostication in patients with chronic heart failure (CHF). The purpose of this study was to create mortality risk charts for short-term (2 years) and long-term (5 years) prediction of cardiac mortality. Using a pooled database of 1322 CHF patients, multivariate analysis, including (123)I-MIBG late heart-to-mediastinum ratio (HMR), left ventricular ejection fraction (LVEF), and clinical factors, was performed to determine optimal variables for the prediction of 2- and 5-year mortality risk using subsets of the patients (n = 1280 and 933, respectively). Multivariate logistic regression analysis was performed to create risk charts. Cardiac mortality was 10 and 22% for the sub-population of 2- and 5-year analyses. A four-parameter multivariate logistic regression model including age, New York Heart Association (NYHA) functional class, LVEF, and HMR was used. Annualized mortality rate was <1% in patients with NYHA Class I-II and HMR ≥ 2.0, irrespective of age and LVEF. In patients with NYHA Class III-IV, mortality rate was 4-6 times higher for HMR < 1.40 compared with HMR ≥ 2.0 in all LVEF classes. Among the subset of patients with b-type natriuretic peptide (BNP) results (n = 491 and 359 for 2- and 5-year models, respectively), the 5-year model showed incremental value of HMR in addition to BNP. Both 2- and 5-year risk prediction models with (123)I-MIBG HMR can be used to identify low-risk as well as high-risk patients, which can be effective for further risk stratification of CHF patients even when BNP is available. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Using Networks To Understand Medical Data: The Case of Class III Malocclusions

PubMed Central

Scala, Antonio; Auconi, Pietro; Scazzocchio, Marco; Caldarelli, Guido; McNamara, James A.; Franchi, Lorenzo

2012-01-01

A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science. PMID:23028552

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy.

PubMed

Kuligowski, Julia; Carrión, David; Quintás, Guillermo; Garrigues, Salvador; de la Guardia, Miguel

2011-01-01

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use

PubMed Central

Reboussin, Beth A.; Ialongo, Nicholas S.

2011-01-01

Summary Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder which is most often diagnosed in childhood with symptoms often persisting into adulthood. Elevated rates of substance use disorders have been evidenced among those with ADHD, but recent research focusing on the relationship between subtypes of ADHD and specific drugs is inconsistent. We propose a latent transition model (LTM) to guide our understanding of how drug use progresses, in particular marijuana use, while accounting for the measurement error that is often found in self-reported substance use data. We extend the LTM to include a latent class predictor to represent empirically derived ADHD subtypes that do not rely on meeting specific diagnostic criteria. We begin by fitting two separate latent class analysis (LCA) models by using second-order estimating equations: a longitudinal LCA model to define stages of marijuana use, and a cross-sectional LCA model to define ADHD subtypes. The LTM model parameters describing the probability of transitioning between the LCA-defined stages of marijuana use and the influence of the LCA-defined ADHD subtypes on these transition rates are then estimated by using a set of first-order estimating equations given the LCA parameter estimates. A robust estimate of the LTM parameter variance that accounts for the variation due to the estimation of the two sets of LCA parameters is proposed. Solving three sets of estimating equations enables us to determine the underlying latent class structures independently of the model for the transition rates and simplifying assumptions about the correlation structure at each stage reduces the computational complexity. PMID:21461139

Constraints Modeling in FRBR Data Model Using OCL

NASA Astrophysics Data System (ADS)

Rudić, Gordana

2011-09-01

Transformation of the conceptual FRBR data model to the class diagram in UML 2.0 notation is given. The class diagram is formed using MagicDraw CASE tool. The paper presents a class diagram for the first group of FRBR entities ie. classes (the product of intellectual or artistic endeavour). It is demonstrated how to model constraints over relationships between classes in FRBR object data model using OCL 2.0.

Joint modality fusion and temporal context exploitation for semantic video analysis

NASA Astrophysics Data System (ADS)

Papadopoulos, Georgios Th; Mezaris, Vasileios; Kompatsiaris, Ioannis; Strintzis, Michael G.

2011-12-01

In this paper, a multi-modal context-aware approach to semantic video analysis is presented. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for each modality. Subsequently, a graphical modeling-based approach is proposed for jointly performing modality fusion and temporal context exploitation. Novelties of this work include the combined use of contextual information and multi-modal fusion, and the development of a new representation for providing motion distribution information to HMMs. Specifically, an integrated Bayesian Network is introduced for simultaneously performing information fusion of the individual modality analysis results and exploitation of temporal context, contrary to the usual practice of performing each task separately. Contextual information is in the form of temporal relations among the supported classes. Additionally, a new computationally efficient method for providing motion energy distribution-related information to HMMs, which supports the incorporation of motion characteristics from previous frames to the currently examined one, is presented. The final outcome of this overall video analysis framework is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach to four datasets belonging to the domains of tennis, news and volleyball broadcast video are presented.

Photospheres of hot stars. IV - Spectral type O4

NASA Technical Reports Server (NTRS)

Bohannan, Bruce; Abbott, David C.; Voels, Stephen A.; Hummer, David G.

1990-01-01

The basic stellar parameters of a supergiant (Zeta Pup) and two main-sequence stars, 9 Sgr and HD 46223, at spectral class O4 are determined using line profile analysis. The stellar parameters are determined by comparing high signal-to-noise hydrogen and helium line profiles with those from stellar atmosphere models which include the effect of radiation scattered back onto the photosphere from an overlying stellar wind, an effect referred to as wind blanketing. At spectral class O4, the inclusion of wind-blanketing in the model atmosphere reduces the effective temperature by an average of 10 percent. This shift in effective temperature is also reflected by shifts in several other stellar parameters relative to previous O4 spectral-type calibrations. It is also shown through the analysis of the two O4 V stars that scatter in spectral type calibrations is introduced by assuming that the observed line profile reflects the photospheric stellar parameters.

Lyapunov function-based control laws for revolute robot arms - Tracking control, robustness, and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz-Delgado, Kenneth; Bayard, David S.

1992-01-01

A new class of joint level control laws for all-revolute robot arms is introduced. The analysis is similar to a recently proposed energy-like Liapunov function approach, except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. This approach gives way to a much simpler analysis and leads to a new class of control designs which guarantee both global asymptotic stability and local exponential stability. When Coulomb and viscous friction and parameter uncertainty are present as model perturbations, a sliding mode-like modification of the control law results in a robustness-enhancing outer loop. Adaptive control is formulated within the same framework. A linear-in-the-parameters formulation is adopted and globally asymptotically stable adaptive control laws are derived by simply replacing unknown model parameters by their estimates (i.e., certainty equivalence adaptation).

A new class of energy based control laws for revolute robot arms - Tracking control, robustness enhancement and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz, Kenneth; Bayard, David S.

1988-01-01

A class of joint-level control laws for all-revolute robot arms is introduced. The analysis is similar to the recently proposed energy Liapunov function approach except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. By using energy Liapunov functions with the modified potential energy, a much simpler analysis can be used to show closed-loop global asymptotic stability and local exponential stability. When Coulomb and viscous friction and model parameter errors are present, a sliding-mode-like modification of the control law is proposed to add a robustness-enhancing outer loop. Adaptive control is also addressed within the same framework. A linear-in-the-parameters formulation is adopted, and globally asymptotically stable adaptive control laws are derived by replacing the model parameters in the nonadaptive control laws by their estimates.

Effective field theory analysis on μ problem in low-scale gauge mediation

NASA Astrophysics Data System (ADS)

Zheng, Sibo

2012-02-01

Supersymmetric models based on the scenario of gauge mediation often suffer from the well-known μ problem. In this paper, we reconsider this problem in low-scale gauge mediation in terms of effective field theory analysis. In this paradigm, all high energy input soft mass can be expressed via loop expansions. If the corrections coming from messenger thresholds are small, as we assume in this letter, then all RG evaluations can be taken as linearly approximation for low-scale supersymmetric breaking. Due to these observations, the parameter space can be systematically classified and studied after constraints coming from electro-weak symmetry breaking are imposed. We find that some old proposals in the literature are reproduced, and two new classes are uncovered. We refer to a microscopic model, where the specific relations among coefficients in one of the new classes are well motivated. Also, we discuss some primary phenomenologies.

Theoretical and observational constraints on Tachyon Inflation

NASA Astrophysics Data System (ADS)

Barbosa-Cendejas, Nandinii; De-Santiago, Josue; German, Gabriel; Hidalgo, Juan Carlos; Rigel Mora-Luna, Refugio

2018-03-01

We constrain several models in Tachyonic Inflation derived from the large-N formalism by considering theoretical aspects as well as the latest observational data. On the theoretical side, we assess the field range of our models by means of the excursion of the equivalent canonical field. On the observational side, we employ BK14+PLANCK+BAO data to perform a parameter estimation analysis as well as a Bayesian model selection to distinguish the most favoured models among all four classes here presented. We observe that the original potential V propto sech(T) is strongly disfavoured by observations with respect to a reference model with flat priors on inflationary observables. This realisation of Tachyon inflation also presents a large field range which may demand further quantum corrections. We also provide examples of potentials derived from the polynomial and the perturbative classes which are both statistically favoured and theoretically acceptable.

Seismic hazard analysis for Jayapura city, Papua

NASA Astrophysics Data System (ADS)

Robiana, R.; Cipta, A.

2015-04-01

Jayapura city had destructive earthquake which occurred on June 25, 1976 with the maximum intensity VII MMI scale. Probabilistic methods are used to determine the earthquake hazard by considering all possible earthquakes that can occur in this region. Earthquake source models using three types of source models are subduction model; comes from the New Guinea Trench subduction zone (North Papuan Thrust), fault models; derived from fault Yapen, TareraAiduna, Wamena, Memberamo, Waipago, Jayapura, and Jayawijaya, and 7 background models to accommodate unknown earthquakes. Amplification factor using geomorphological approaches are corrected by the measurement data. This data is related to rock type and depth of soft soil. Site class in Jayapura city can be grouped into classes B, C, D and E, with the amplification between 0.5 - 6. Hazard maps are presented with a 10% probability of earthquake occurrence within a period of 500 years for the dominant periods of 0.0, 0.2, and 1.0 seconds.

Log-Linear Models for Gene Association

PubMed Central

Hu, Jianhua; Joshi, Adarsh; Johnson, Valen E.

2009-01-01

We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens’ sampling distribution is used to restrict the number of interacting components assigned high posterior probability, and the calculation of posterior model probabilities is expedited by approximations based on the likelihood ratio statistic. Simulation studies are used to evaluate the efficiency of the resulting algorithm for known interaction structures. Finally, the algorithm is validated in a microarray study for which it was possible to obtain biological confirmation of detected interactions. PMID:19655032

Object-oriented analysis and design of a health care management information system.

PubMed

Krol, M; Reich, D L

1999-04-01

We have created a prototype for a universal object-oriented model of a health care system compatible with the object-oriented approach used in version 3.0 of the HL7 standard for communication messages. A set of three models has been developed: (1) the Object Model describes the hierarchical structure of objects in a system--their identity, relationships, attributes, and operations; (2) the Dynamic Model represents the sequence of operations in time as a collection of state diagrams for object classes in the system; and (3) functional Diagram represents the transformation of data within a system by means of data flow diagrams. Within these models, we have defined major object classes of health care participants and their subclasses, associations, attributes and operators, states, and behavioral scenarios. We have also defined the major processes and subprocesses. The top-down design approach allows use, reuse, and cloning of standard components.

Predicting spatial and temporal distribution of Indo-Pacific lionfish (Pterois volitans) in Biscayne Bay through habitat suitability modeling

USGS Publications Warehouse

Bernal, Nicholas A.; DeAngelis, Donald L.; Schofield, Pamela J.; Sullivan Sealey, Kathleen

2014-01-01

Invasive species may exhibit higher levels of growth and reproduction when environmental conditions are most suitable, and thus their effects on native fauna may be intensified. Understanding potential impacts of these species, especially in the nascent stages of a biological invasion, requires critical information concerning spatial and temporal distributions of habitat suitability. Using empirically supported environmental variables (e.g., temperature, salinity, dissolved oxygen, rugosity, and benthic substrate), our models predicted habitat suitability for the invasive lionfish (Pterois volitans) in Biscayne Bay, Florida. The use of Geographic Information Systems (GIS) as a platform for the modeling process allowed us to quantify correlations between temporal (seasonal) fluctuations in the above variables and the spatial distribution of five discrete habitat quality classes, whose ranges are supported by statistical deviations from the apparent best conditions described in prior studies. Analysis of the resulting models revealed little fluctuation in spatial extent of the five habitat classes on a monthly basis. Class 5, which represented the area with environmental variables closest to the best conditions for lionfish, occupied approximately one-third of Biscayne Bay, with subsequent habitats declining in area. A key finding from this study was that habitat suitability increased eastward from the coastline, where higher quality habitats were adjacent to the Atlantic Ocean and displayed marine levels of ambient water quality. Corroboration of the models with sightings from the USGS-NAS database appeared to support our findings by nesting 79 % of values within habitat class 5; however, field testing (i.e., lionfish surveys) is necessary to confirm the relationship between habitat classes and lionfish distribution.

Ranges of applicability for the continuum beam model in the mechanics of carbon nanotubes and nanorods

NASA Technical Reports Server (NTRS)

Harik, V. M.

2001-01-01

Limitations in the validity of the continuum beam model for carbon nanotubes (NTs) and nanorods are examined. Applicability of all assumptions used in the model is restricted by the two criteria for geometric parameters that characterize the structure of NTs. The key non-dimensional parameters that control the NT buckling behavior are derived via dimensional analysis of the nanomechanical problem. A mechanical law of geometric similitude for NT buckling is extended from continuum mechanics for different molecular structures. A model applicability map, where two classes of beam-like NTs are identified, is constructed for distinct ranges of non-dimensional parameters. Expressions for the critical buckling loads and strains are tailored for two classes of NTs and compared with the data provided by the molecular dynamics simulations. copyright 2001 Elsevier Science Ltd. All rights reserved.

Stochastic Parametrization for the Impact of Neglected Variability Patterns

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Hien, Steffen; Achatz, Ulrich; Horenko, Illia

2017-04-01

An efficient description of the gravity wave variability and the related spontaneous emission processes requires an empirical stochastic closure for the impact of neglected variability patterns (subgridscales or SGS). In particular, we focus on the analysis of the IGW emission within a tangent linear model which requires a stochastic SGS parameterization for taking the self interaction of the ageostrophic flow components into account. For this purpose, we identify the best SGS model in terms of exactness and simplicity by deploying a wide range of different data-driven model classes, including standard stationary regression models, autoregression and artificial neuronal networks models - as well as the family of nonstationary models like FEM-BV-VARX model class (Finite Element based vector autoregressive time series analysis with bounded variation of the model parameters). The models are used to investigate the main characteristics of the underlying dynamics and to explore the significant spatial and temporal neighbourhood dependencies. The best SGS model in terms of exactness and simplicity is obtained for the nonstationary FEM-BV-VARX setting, determining only direct spatial and temporal neighbourhood as significant - and allowing to drastically reduce the number of informations that are required for the optimal SGS. Additionally, the models are characterized by sets of vector- and matrix-valued parameters that must be inferred from big data sets provided by simulations - making it a task that can not be solved without deploying high-performance computing facilities (HPC).

Statistical modeling of space shuttle environmental data

NASA Technical Reports Server (NTRS)

Tubbs, J. D.; Brewer, D. W.

1983-01-01

Statistical models which use a class of bivariate gamma distribution are examined. Topics discussed include: (1) the ratio of positively correlated gamma varieties; (2) a method to determine if unequal shape parameters are necessary in bivariate gamma distribution; (3) differential equations for modal location of a family of bivariate gamma distribution; and (4) analysis of some wind gust data using the analytical results developed for modeling application.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PubMed

Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

2015-01-01

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

The Team Climate Inventory as a Measure of Primary Care Teams' Processes: Validation of the French Version

PubMed Central

Beaulieu, Marie-Dominique; Dragieva, Nataliya; Del Grande, Claudio; Dawson, Jeremy; Haggerty, Jeannie L.; Barnsley, Jan; Hogg, William E.; Tousignant, Pierre; West, Michael A.

2014-01-01

Purpose: Evaluate the psychometric properties of the French version of the short 19-item Team Climate Inventory (TCI) and explore the contributions of individual and organizational characteristics to perceived team effectiveness. Method: The TCI was completed by 471 of the 618 (76.2%) healthcare professionals and administrative staff working in a random sample of 37 primary care practices in the province of Quebec. Results: Exploratory factor analysis confirmed the original four-factor model. Cronbach's alphas were excellent (from 0.88 to 0.93). Latent class analysis revealed three-class response structure. Respondents in practices with professional governance had a higher probability of belonging to the “High TCI” class than did practices with community governance (36.7% vs. 19.1%). Administrative staff tended to fall into the “Suboptimal TCI” class more frequently than did physicians (36.5% vs. 19.0%). Conclusion: Results confirm the validity of our French version of the short TCI. The association between professional governance and better team climate merits further exploration. PMID:24726073

Modelling plume dispersion pattern from a point source using spatial auto-correlational analysis

NASA Astrophysics Data System (ADS)

Ujoh, F.; Kwabe, D.

2014-02-01

The main objective of the study is to estimate the rate and model the pattern of plume rise from Dangote Cement Plc. A handheld Garmin GPS was employed for collection of coordinates at a single kilometre graduation from the centre of the factory to 10 kilometres. Plume rate was estimated using the Gaussian model while Kriging, using ArcGIS, was adopted for modelling the pattern of plume dispersion over a 10 kilometre radius around the factory. ANOVA test was applied for statistical analysis of the plume coefficients. The results indicate that plume dispersion is generally high with highest values recorded for the atmospheric stability classes A and B, while the least values are recorded for the atmospheric stability classes F and E. The variograms derived from the Kriging reveal that the pattern of plume dispersion is outwardly radial and omni-directional. With the exception of 3 stability sub-classes (DH, EH and FH) out of a total of 12, the 24-hour average of particulate matters (PM10 and PM2.5) within the study area is outrageously higher (highest value at 21392.3) than the average safety limit of 150 ug/m3 - 230 ug/m3 prescribed by the 2006 WHO guidelines. This indicates the presence of respirable and non-respirable pollutants that create poor ambient air quality. The study concludes that the use of geospatial technology can be adopted in modelling dispersion of pollutants from a point source. The study recommends ameliorative measures to reduce the rate of plume emission at the factory.

BDDCS Class Prediction for New Molecular Entities

PubMed Central

Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z.; Oprea, Tudor I.

2012-01-01

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport are not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the times. The unbalanced stratification of the dataset didn’t affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirmed the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the dataset. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction. PMID:22224483

Attachment typologies and posttraumatic stress disorder (PTSD), depression and anxiety: a latent profile analysis approach

PubMed Central

Armour, Cherie; Elklit, Ask; Shevlin, Mark

2011-01-01

Background Bartholomew (1990) proposed a four category adult attachment model based on Bowlby's (1973) proposal that attachment is underpinned by an individual's view of the self and others. Previous cluster analytic techniques have identified four and two attachment styles based on the Revised Adult Attachment Scale (RAAS). In addition, attachment styles have been proposed to meditate the association between stressful life events and subsequent psychiatric status. Objective The current study aimed to empirically test the attachment typology proposed by Collins and Read (1990). Specifically, LPA was used to determine if the proposed four styles can be derived from scores on the dimensions of closeness/dependency and anxiety. In addition, we aimed to test if the resultant attachment styles predicted the severity of psychopathology in response to a whiplash trauma. Method A large sample of Danish trauma victims (N=1577) participated. A Latent Profile Analysis was conducted, using Mplus 5.1, on scores from the RAAS scale to ascertain if there were underlying homogeneous attachment classes/subgroups. Class membership was used in a series of one-way ANOVA tests to determine if classes were significantly different in terms of mean scores on measures of psychopathology. Results The three class solution was considered optimal. Class one was termed Fearful (18.6%), Class two Preoccupied (34.5%), and Class three Secure (46.9%). The secure class evidenced significantly lower mean scores on PTSD, depression, and anxiety measures compared to other classes, whereas the fearful class evidenced significantly higher mean scores compared to other classes. Conclusions The results demonstrated evidence of three discrete classes of attachment styles, which were labelled secure, preoccupied, and fearful. This is in contrast to previous cluster analytic techniques which have identified four and two attachment styles based on the RAAS.In addition, Securely attached individuals display lower levels of psychopathology post whiplash trauma. PMID:22893805

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

The structural identifiability and parameter estimation of a multispecies model for the transmission of mastitis in dairy cows with postmilking teat disinfection.

PubMed

White, L J; Evans, N D; Lam, T J G M; Schukken, Y H; Medley, G F; Godfrey, K R; Chappell, M J

2002-01-01

A mathematical model for the transmission of two interacting classes of mastitis causing bacterial pathogens in a herd of dairy cows is presented and applied to a specific data set. The data were derived from a field trial of a specific measure used in the control of these pathogens, where half the individuals were subjected to the control and in the others the treatment was discontinued. The resultant mathematical model (eight non-linear simultaneous ordinary differential equations) therefore incorporates heterogeneity in the host as well as the infectious agent and consequently the effects of control are intrinsic in the model structure. A structural identifiability analysis of the model is presented demonstrating that the scope of the novel method used allows application to high order non-linear systems. The results of a simultaneous estimation of six unknown system parameters are presented. Previous work has only estimated a subset of these either simultaneously or individually. Therefore not only are new estimates provided for the parameters relating to the transmission and control of the classes of pathogens under study, but also information about the relationships between them. We exploit the close link between mathematical modelling, structural identifiability analysis, and parameter estimation to obtain biological insights into the system modelled.

An object-oriented software approach for a distributed human tracking motion system

NASA Astrophysics Data System (ADS)

Micucci, Daniela L.

2003-06-01

Tracking is a composite job involving the co-operation of autonomous activities which exploit a complex information model and rely on a distributed architecture. Both information and activities must be classified and related in several dimensions: abstraction levels (what is modelled and how information is processed); topology (where the modelled entities are); time (when entities exist); strategy (why something happens); responsibilities (who is in charge of processing the information). A proper Object-Oriented analysis and design approach leads to a modular architecture where information about conceptual entities is modelled at each abstraction level via classes and intra-level associations, whereas inter-level associations between classes model the abstraction process. Both information and computation are partitioned according to level-specific topological models. They are also placed in a temporal framework modelled by suitable abstractions. Domain-specific strategies control the execution of the computations. Computational components perform both intra-level processing and intra-level information conversion. The paper overviews the phases of the analysis and design process, presents major concepts at each abstraction level, and shows how the resulting design turns into a modular, flexible and adaptive architecture. Finally, the paper sketches how the conceptual architecture can be deployed into a concrete distribute architecture by relying on an experimental framework.

Chemical pollution assessment and prioritisation model for the Upper and Middle Vaal water management areas of South Africa.

PubMed

Dzwairo, B; Otieno, F A O

2014-12-01

A chemical pollution assessment and prioritisation model was developed for the Upper and Middle Vaal water management areas of South Africa in order to provide a simple and practical Pollution Index to assist with mitigation and rehabilitation activities. Historical data for 2003 to 2008 from 21 river sites were cubic-interpolated to daily values. Nine parameters were considered for this purpose, that is, ammonium, chloride, electrical conductivity, dissolved oxygen, pH, fluoride, nitrate, phosphate and sulphate. Parameter selection was based on sub-catchment pollution characteristics and availability of a consistent data range, against a harmonised guideline which provided five classes. Classes 1, 2, 3 and 4 used ideal catchment background values for Vaal Dam, Vaal Barrage, Blesbokspruit/Suikerbosrant and Klip Rivers, respectively. Class 5 represented values which fell above those for Klip River. The Pollution Index, as provided by the model, identified pollution prioritisation monitoring points on Rietspruit-W:K2, Natalspruit:K12, Blesbokspruit:B1, Rietspruit-L:R1/R2, Taaibosspruit:T1 and Leeuspruit:L1. Pre-classification indicated that pollution sources were domestic, industrial and mine effluent. It was concluded that rehabilitation and mitigation measures should prioritise points with high classes. Ability of the model to perform simple scenario building and analysis was considered to be an effective tool for acid mine drainage pollution assessment.

Personality and changes in comorbidity patterns among anxiety and depressive disorders.

PubMed

Spinhoven, Philip; de Rooij, Mark; Heiser, Willem; Smit, Jan H; Penninx, Brenda W J H

2012-11-01

This prospective study examined the prognostic value of the Big Five personality model for changes in comorbidity patterns of emotional disorders both from a person- and trait-centered perspective. Moreover, it is investigated whether the predictive effect of personality can be attributed to symptom severity at baseline. We followed a cohort of 2566 persons (18-65 years) recruited in primary and specialized mental health care during two years. Personality dimensions at baseline were assessed with the NEO-FFI. The Diagnostic and Statistical Manual of Mental Disorders (4th ed.)-based diagnostic interviews with the CIDI allowed assessment of changes in comorbidity patterns of anxiety and depressive disorders over two years. Data were analyzed with latent class analysis (LCA) and latent transition analysis (LTA). LCA identified a four-class latent comorbidity class solution (Few Disorders, Fear Disorders, Distress Disorders, and Comorbid Fear and Distress Disorders) and a five-class latent personality class solution (High Resilients, Medium Resilients, Low Overcontrollers, Medium Overcontrollers, and High Overcontrollers). LTA showed that the likelihood of remaining in the same latent class was larger than that of transitioning to a less severe comorbidity class. Also, after correcting for symptom severity, medium and high Overcontrollers as well as participants with lower levels of conscientiousness were less likely to transition to a less severe comorbidity class. In particular, the individual trait of conscientiousness may be less dependent on current levels of anxiety and depressive symptoms and be a key pathoplastic or even predisposing variable in anxiety and depression and needs more theoretical and empirical study. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

On Local Homogeneity and Stochastically Ordered Mixed Rasch Models

ERIC Educational Resources Information Center

Kreiner, Svend; Hansen, Mogens; Hansen, Carsten Rosenberg

2006-01-01

Mixed Rasch models add latent classes to conventional Rasch models, assuming that the Rasch model applies within each class and that relative difficulties of items are different in two or more latent classes. This article considers a family of stochastically ordered mixed Rasch models, with ordinal latent classes characterized by increasing total…

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

USGS Publications Warehouse

Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam

2012-01-01

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.

Application of class-modelling techniques to infrared spectra for analysis of pork adulteration in beef jerkys.

PubMed

Kuswandi, Bambang; Putri, Fitra Karima; Gani, Agus Abdul; Ahmad, Musa

2015-12-01

The use of chemometrics to analyse infrared spectra to predict pork adulteration in the beef jerky (dendeng) was explored. In the first step, the analysis of pork in the beef jerky formulation was conducted by blending the beef jerky with pork at 5-80 % levels. Then, they were powdered and classified into training set and test set. The second step, the spectra of the two sets was recorded by Fourier Transform Infrared (FTIR) spectroscopy using atenuated total reflection (ATR) cell on the basis of spectral data at frequency region 4000-700 cm(-1). The spectra was categorised into four data sets, i.e. (a) spectra in the whole region as data set 1; (b) spectra in the fingerprint region (1500-600 cm(-1)) as data set 2; (c) spectra in the whole region with treatment as data set 3; and (d) spectra in the fingerprint region with treatment as data set 4. The third step, the chemometric analysis were employed using three class-modelling techniques (i.e. LDA, SIMCA, and SVM) toward the data sets. Finally, the best result of the models towards the data sets on the adulteration analysis of the samples were selected and the best model was compared with the ELISA method. From the chemometric results, the LDA model on the data set 1 was found to be the best model, since it could classify and predict 100 % accuracy of the sample tested. The LDA model was applied toward the real samples of the beef jerky marketed in Jember, and the results showed that the LDA model developed was in good agreement with the ELISA method.

Latent Classes of Adolescent Sexual and Romantic Relationship Experiences: Implications for Adult Sexual Health and Relationship Outcomes

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Lanza, Stephanie T.

2015-01-01

Adolescents’ sexual and romantic relationship experiences are multidimensional, but often studied as single constructs. Thus, it is not clear how different patterns of sexual and relationship experience may interact to differentially predict later outcomes. In this study we used latent class analysis to model patterns (latent classes) of adolescent sexual and romantic experiences, and then examined how these classes are associated with young adult sexual health and relationship outcomes in data from the National Longitudinal Study of Adolescent to Adult Health. We identified six adolescent relationship classes: No Relationship (33%), Waiting (22%), Intimate (38%), Private (3%), Low Involvement (3%), and Physical (2%). Adolescents in the Waiting and Intimate classes were more likely to have married by young adulthood than those in other classes, and those in the Physical class had a greater number of sexual partners and higher rates of STIs. Some gender differences were found; for example, women in the Low-involvement and Physical classes in adolescence had average or high odds of marriage, whereas men in these classes had relatively low odds of marriage. Our findings identify more and less normative patterns of romantic and sexual experiences in late adolescence, and elucidate associations between adolescent experiences and adult outcomes. PMID:26445133

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu

2007-01-01

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…

Providing Information to Parents of Children with Mental Health Problems: A Discrete Choice Conjoint Analysis of Professional Preferences

ERIC Educational Resources Information Center

Cunningham, Charles E.; Deal, Ken; Rimas, Heather; Chen, Yvonne; Buchanan, Don H.; Sdao-Jarvie, Kathie

2009-01-01

We used discrete choice conjoint analysis to model the ways 645 children's mental health (CMH) professionals preferred to provide information to parents seeking CMH services. Participants completed 20 choice tasks presenting experimentally varied combinations of the study's 14 4-level CMH information transfer attributes. Latent class analysis…

Analysis of Student Participation in University Classes: An Interdisciplinary Experience

ERIC Educational Resources Information Center

Trigueros, Carmen; Rivera, Enrique; Pavesio, Maite; Torres, Juan

2005-01-01

The interest to improve the quality of teaching in higher education has led to the involvement of a group of faculty members at the University of Granada Faculty of Education in an Action-Research (A-R) experience titled "Towards a model of collaborative lecture throughout the analysis of their educational tasks." This has been an…

Identifying a combined construct of grief and explosive anger as a response to injustice amongst survivors of mass conflict: A latent class analysis of data from Timor-Leste.

PubMed

Rees, Susan J; Tay, Alvin Kuowei; Savio, Elisa; Maria Da Costa, Zelia; Silove, Derrick

2017-01-01

Previous studies have identified high rates of explosive anger amongst post-conflict populations including Timor-Leste. We sought to test whether explosive anger was integrally associated with symptoms of grief amongst the Timorese, a society that has experienced extensive conflict-related losses. In 2010 and 2011 we recruited adults (n = 2964), 18-years and older, living in an urban and a rural village in Timor-Leste. We applied latent class analysis to identify subpopulations based on symptoms of explosive anger and grief. The best fitting model comprised three classes: grief (24%), grief-anger (25%), and a low symptom group (51%). There were more women and urban dwellers in the grief and grief-anger classes compared to the reference class. Persons in the grief and grief-anger classes experienced higher rates of witnessing murder and atrocities and traumatic losses, ongoing poverty, and preoccupations with injustice for the two historical periods of conflict (the Indonesian occupation and the later internal conflict). Compared to the reference class, only the grief-anger class reported greater exposure to extreme deprivations during the conflict, ongoing family conflict, and preoccupations with injustice for contemporary times; and compared to the grief class, greater exposure to traumatic losses, poverty, family conflict and preoccupations with injustice for both the internal conflict and contemporary times. A substantial number of adults in this post-conflict country experienced a combined constellation of grief and explosive anger associated with extensive traumatic losses, deprivations, and preoccupations with injustice. Importantly, grief-anger may be linked to family conflict in this post-conflict environment.

Identifying a combined construct of grief and explosive anger as a response to injustice amongst survivors of mass conflict: A latent class analysis of data from Timor-Leste

PubMed Central

Rees, Susan J.; Tay, Alvin Kuowei; Savio, Elisa; Maria Da Costa, Zelia; Silove, Derrick

2017-01-01

Previous studies have identified high rates of explosive anger amongst post-conflict populations including Timor-Leste. We sought to test whether explosive anger was integrally associated with symptoms of grief amongst the Timorese, a society that has experienced extensive conflict-related losses. In 2010 and 2011 we recruited adults (n = 2964), 18-years and older, living in an urban and a rural village in Timor-Leste. We applied latent class analysis to identify subpopulations based on symptoms of explosive anger and grief. The best fitting model comprised three classes: grief (24%), grief-anger (25%), and a low symptom group (51%). There were more women and urban dwellers in the grief and grief-anger classes compared to the reference class. Persons in the grief and grief-anger classes experienced higher rates of witnessing murder and atrocities and traumatic losses, ongoing poverty, and preoccupations with injustice for the two historical periods of conflict (the Indonesian occupation and the later internal conflict). Compared to the reference class, only the grief-anger class reported greater exposure to extreme deprivations during the conflict, ongoing family conflict, and preoccupations with injustice for contemporary times; and compared to the grief class, greater exposure to traumatic losses, poverty, family conflict and preoccupations with injustice for both the internal conflict and contemporary times. A substantial number of adults in this post-conflict country experienced a combined constellation of grief and explosive anger associated with extensive traumatic losses, deprivations, and preoccupations with injustice. Importantly, grief-anger may be linked to family conflict in this post-conflict environment. PMID:28430793

A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

NASA Astrophysics Data System (ADS)

Clerici, Aldo; Perego, Susanna; Tellini, Claudio; Vescovi, Paolo

2006-08-01

Among the many GIS based multivariate statistical methods for landslide susceptibility zonation, the so called “Conditional Analysis method” holds a special place for its conceptual simplicity. In fact, in this method landslide susceptibility is simply expressed as landslide density in correspondence with different combinations of instability-factor classes. To overcome the operational complexity connected to the long, tedious and error prone sequence of commands required by the procedure, a shell script mainly based on the GRASS GIS was created. The script, starting from a landslide inventory map and a number of factor maps, automatically carries out the whole procedure resulting in the construction of a map with five landslide susceptibility classes. A validation procedure allows to assess the reliability of the resulting model, while the simple mean deviation of the density values in the factor class combinations, helps to evaluate the goodness of landslide density distribution. The procedure was applied to a relatively small basin (167 km2) in the Italian Northern Apennines considering three landslide types, namely rotational slides, flows and complex landslides, for a total of 1,137 landslides, and five factors, namely lithology, slope angle and aspect, elevation and slope/bedding relations. The analysis of the resulting 31 different models obtained combining the five factors, confirms the role of lithology, slope angle and slope/bedding relations in influencing slope stability.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Latent class analysis of diagnostic science assessment data using Bayesian networks

NASA Astrophysics Data System (ADS)

Steedle, Jeffrey Thomas

2008-10-01

Diagnostic science assessments seek to draw inferences about student understanding by eliciting evidence about the mental models that underlie students' reasoning about physical systems. Measurement techniques for analyzing data from such assessments embody one of two contrasting assessment programs: learning progressions and facet-based assessments. Learning progressions assume that students have coherent theories that they apply systematically across different problem contexts. In contrast, the facet approach makes no such assumption, so students should not be expected to reason systematically across different problem contexts. A systematic comparison of these two approaches is of great practical value to assessment programs such as the National Assessment of Educational Progress as they seek to incorporate small clusters of related items in their tests for the purpose of measuring depth of understanding. This dissertation describes an investigation comparing learning progression and facet models. Data comprised student responses to small clusters of multiple-choice diagnostic science items focusing on narrow aspects of understanding of Newtonian mechanics. Latent class analysis was employed using Bayesian networks in order to model the relationship between students' science understanding and item responses. Separate models reflecting the assumptions of the learning progression and facet approaches were fit to the data. The technical qualities of inferences about student understanding resulting from the two models were compared in order to determine if either modeling approach was more appropriate. Specifically, models were compared on model-data fit, diagnostic reliability, diagnostic certainty, and predictive accuracy. In addition, the effects of test length were evaluated for both models in order to inform the number of items required to obtain adequately reliable latent class diagnoses. Lastly, changes in student understanding over time were studied with a longitudinal model in order to provide educators and curriculum developers with a sense of how students advance in understanding over the course of instruction. Results indicated that expected student response patterns rarely reflected the assumptions of the learning progression approach. That is, students tended not to systematically apply a coherent set of ideas across different problem contexts. Even those students expected to express scientifically-accurate understanding had substantial probabilities of reporting certain problematic ideas. The learning progression models failed to make as many substantively-meaningful distinctions among students as the facet models. In statistical comparisons, model-data fit was better for the facet model, but the models were quite comparable on all other statistical criteria. Studying the effects of test length revealed that approximately 8 items are needed to obtain adequate diagnostic certainty, but more items are needed to obtain adequate diagnostic reliability. The longitudinal analysis demonstrated that students either advance in their understanding (i.e., switch to the more advanced latent class) over a short period of instruction or stay at the same level. There was no significant relationship between the probability of changing latent classes and time between testing occasions. In all, this study is valuable because it provides evidence informing decisions about modeling and reporting on student understanding, it assesses the quality of measurement available from short clusters of diagnostic multiple-choice items, and it provides educators with knowledge of the paths that student may take as they advance from novice to expert understanding over the course of instruction.

An Investigation of Difficulties Experienced by Students Developing Unified Modelling Language (UML) Class and Sequence Diagrams

ERIC Educational Resources Information Center

Sien, Ven Yu

2011-01-01

Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they…

Design and analysis for thematic map accuracy assessment: Fundamental principles

Treesearch

Stephen V. Stehman; Raymond L. Czaplewski

1998-01-01

Land-cover maps are used in numerous natural resource applications to describe the spatial distribution and pattern of land-cover, to estimate areal extent of various cover classes, or as input into habitat suitability models, land-cover change analyses, hydrological models, and risk analyses. Accuracy assessment quantifies data quality so that map users may evaluate...

Evaluating Students' Abilities to Construct Mathematical Models from Data Using Latent Class Analysis

ERIC Educational Resources Information Center

Brandriet, Alexandra; Rupp, Charlie A.; Lazenby, Katherine; Becker, Nicole M.

2018-01-01

Analyzing and interpreting data is an important science practice that contributes toward the construction of models from data; yet, there is evidence that students may struggle with making meaning of data. The study reported here focused on characterizing students' approaches to analyzing rate and concentration data in the context of method of…

Tuition at PhD-Granting Institutions: A Supply and Demand Model.

ERIC Educational Resources Information Center

Koshal, Rajindar K.; And Others

1994-01-01

Builds and estimates a model that explains educational supply and demand behavior at PhD-granting institutions in the United States. The statistical analysis based on 1988-89 data suggests that student quantity, educational costs, average SAT score, class size, percentage of faculty with a PhD, graduation rate, ranking, and existence of a medical…

Rule Following and Rule Use in the Balance-Scale Task

ERIC Educational Resources Information Center

Shultz, Thomas R.; Takane, Yoshio

2007-01-01

Quinlan et al. [Quinlan, p., van der Mass, H., Jansen, B., Booij, O., & Rendell, M. (this issue). Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. "Cognition", doi:10.1016/j.cognition.2006.02.004] use Latent Class Analysis (LCA) to criticize a connectionist model of development on the…

American Association of University Women: Branch Operations Data Modeling Case

ERIC Educational Resources Information Center

Harris, Ranida B.; Wedel, Thomas L.

2015-01-01

A nationally prominent woman's advocacy organization is featured in this case study. The scenario may be used as a teaching case, an assignment, or a project in systems analysis and design as well as database design classes. Students are required to document the system operations and requirements, apply logical data modeling concepts, and design…

Classroom characteristics and implementation of a substance use prevention curriculum in European countries.

PubMed

Caria, Maria Paola; Faggiano, Fabrizio; Bellocco, Rino; Galanti, Maria Rosaria

2013-12-01

Partial implementation may explain modest effectiveness of many school-based preventive programmes against substance use. We studied whether specific characteristics of the class could predict the level of implementation of a curriculum delivered by class teachers in schools from some European countries. Secondary analysis of data from an evaluation trial. In seven European countries, 78 schools (173 classes) were randomly assigned to a 12-unit, interactive, standardized programme based on the comprehensive social influence model. Curriculum completeness, application fidelity, average unit duration and use of role-play were monitored using structured report forms. Predictors of implementation were measured by aggregating at class level information from the baseline student survey. Class size, gender composition, mean age, factors related to substance use and to affection to school were analysed, with associations estimated by multilevel regression models. Implementation was not significantly predicted by mean age, proportion of students with positive academic expectation or liking school. Proportion of boys was associated with a shorter time devoted to each unit [β = -0.19, 95% confidence intervals (CI) -0.32 to -0.06]. Class size was inversely related to application fidelity [Odds ratio (OR) 0.92, 95% CI 0.85 to 0.99]. Prevalence of substance use was associated with a decreased odds of implementing all the curriculum units (OR 0.81, 95% CI 0.65 to 0.99). Students' connectedness to their class was associated with increased odds of teachers using role-play (OR 1.52, 95% CI 1.03 to 2.29). Teachers' implementation of preventive programmes may be affected by structural and social characteristics of classes and therefore benefit from organizational strategies and teachers' training in class management techniques.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

The effect of team accelerated instruction on students’ mathematics achievement and learning motivation

NASA Astrophysics Data System (ADS)

Sri Purnami, Agustina; Adi Widodo, Sri; Charitas Indra Prahmana, Rully

2018-01-01

This study aimed to know the improvement of achievement and motivation of learning mathematics by using Team Accelerated Instruction. The research method used was the experiment with descriptive pre-test post-test experiment. The population in this study was all students of class VIII junior high school in Jogjakarta. The sample was taken using cluster random sampling technique. The instrument used in this research was questionnaire and test. Data analysis technique used was Wilcoxon test. It concluded that there was an increase in motivation and student achievement of class VII on linear equation system material by using the learning model of Team Accelerated Instruction. Based on the results of the learning model Team Accelerated Instruction can be used as a variation model in learning mathematics.

Directional data analysis under the general projected normal distribution

PubMed Central

Wang, Fangpo; Gelfand, Alan E.

2013-01-01

The projected normal distribution is an under-utilized model for explaining directional data. In particular, the general version provides flexibility, e.g., asymmetry and possible bimodality along with convenient regression specification. Here, we clarify the properties of this general class. We also develop fully Bayesian hierarchical models for analyzing circular data using this class. We show how they can be fit using MCMC methods with suitable latent variables. We show how posterior inference for distributional features such as the angular mean direction and concentration can be implemented as well as how prediction within the regression setting can be handled. With regard to model comparison, we argue for an out-of-sample approach using both a predictive likelihood scoring loss criterion and a cumulative rank probability score criterion. PMID:24046539

A method of real-time fault diagnosis for power transformers based on vibration analysis

NASA Astrophysics Data System (ADS)

Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

2015-11-01

In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

Framing discourse for optimal learning in science and mathematics

NASA Astrophysics Data System (ADS)

Megowan, Mary Colleen

2007-12-01

This study explored the collaborative thinking and learning that occurred in physics and mathematics classes where teachers practiced Modeling Instruction. Four different classes were videotaped---a middle school mathematics resource class, a 9th grade physical science class, a high school honors physics class and a community college engineering physics course. Videotapes and transcripts were analyzed to discover connections between the conceptual structures and spatial representations that shaped students' conversations about space and time. Along the way, it became apparent that students' and teachers' cultural models of schooling were a significant influence, sometimes positive and sometimes negative, in students' engagement and metaphor selection. A growing number of researchers are exploring the importance of semiotics in physics and mathematics, but typically their unit of analysis is the individual student. To examine the distributed cognition that occurred in this unique learning setting, not just among students but also in connection with their tools, artifacts and representations, I extended the unit of analysis for my research to include small groups and their collaborative work with whiteboarded representations of contextual problems and laboratory exercises. My data revealed a number of interesting insights. Students who constructed spatial representations and used them to assist their reasoning, were more apt to demonstrate a coherent grasp of the elements, operations, relations and rules that govern the model under investigation than those who relied on propositional algebraic representations of the model. In classrooms where teachers permitted and encouraged students to take and hold the floor during whole-group discussions, students learned to probe one another more deeply and conceptually. Shared representations (whether spatial or propositional/algebraic), such as those that naturally occurred when students worked together in small groups to prepare collaborative displays of their thinking, were more apt to stimulate conceptually oriented conversations among students than individual work, i.e., what each student had written on his or her worksheet. This research was supported, in part, by grants from the National Science Foundation (#0337795 and #0312038). Any opinions, findings, conclusions or recommendations expressed herein are those of the author and do not necessarily reflect the views of the National Science Foundation.

The structure of paranoia in the general population.

PubMed

Bebbington, Paul E; McBride, Orla; Steel, Craig; Kuipers, Elizabeth; Radovanovic, Mirjana; Brugha, Traolach; Jenkins, Rachel; Meltzer, Howard I; Freeman, Daniel

2013-06-01

Psychotic phenomena appear to form a continuum with normal experience and beliefs, and may build on common emotional interpersonal concerns. We tested predictions that paranoid ideation is exponentially distributed and hierarchically arranged in the general population, and that persecutory ideas build on more common cognitions of mistrust, interpersonal sensitivity and ideas of reference. Items were chosen from the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II) questionnaire and the Psychosis Screening Questionnaire in the second British National Survey of Psychiatric Morbidity (n = 8580), to test a putative hierarchy of paranoid development using confirmatory factor analysis, latent class analysis and factor mixture modelling analysis. Different types of paranoid ideation ranged in frequency from less than 2% to nearly 30%. Total scores on these items followed an almost perfect exponential distribution (r = 0.99). Our four a priori first-order factors were corroborated (interpersonal sensitivity; mistrust; ideas of reference; ideas of persecution). These mapped onto four classes of individual respondents: a rare, severe, persecutory class with high endorsement of all item factors, including persecutory ideation; a quasi-normal class with infrequent endorsement of interpersonal sensitivity, mistrust and ideas of reference, and no ideas of persecution; and two intermediate classes, characterised respectively by relatively high endorsement of items relating to mistrust and to ideas of reference. The paranoia continuum has implications for the aetiology, mechanisms and treatment of psychotic disorders, while confirming the lack of a clear distinction from normal experiences and processes.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia

PubMed Central

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-01-01

Background Scant information exists on PTSD symptoms and structure in youth from developing countries. Methods We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Results Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Conclusions Results suggest that PTSD is a continuously distributed latent trait. PMID:25382359

Class-first analysis in a continuum: an approach to the complexities of schools, society, and insurgent science

NASA Astrophysics Data System (ADS)

Valdiviezo, Laura Alicia

2010-06-01

This essay addresses Katherine Richardson Bruna's paper: Mexican Immigrant Transnational Social Capital and Class Transformation: Examining the Role of Peer Mediation in Insurgent Science, through five main points . First, I offer a comparison between the traditional analysis of classism in Latin America and Richardson Bruna's call for a class-first analysis in the North American social sciences where there has been a tendency to obviate the specific examination of class relations and class issues. Secondly, I discuss that a class-first analysis solely cannot suffice to depict the complex dimensions in the relations of schools and society. Thus, I suggest a continuum in the class-first analysis. Third, I argue that social constructions surrounding issues of language, ethnicity, and gender necessarily intersect with issues of class and that, in fact, those other constructions offer compatible epistemologies that aid in representing the complexity of social and institutional practices in the capitalist society. Richardson Bruna's analysis of Augusto's interactions with his teacher and peers in the science class provides a fourth point of discussion in this essay. As a final point in my response I discuss Richardson Bruna's idea of making accessible class-first analysis knowledge to educators and especially to science teachers.

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes.

PubMed

König, Caroline; Alquézar, René; Vellido, Alfredo; Giraldo, Jesús

2018-03-01

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.

Searching for chemical classes among metal-poor stars using medium-resolution spectroscopy

NASA Astrophysics Data System (ADS)

Cruz, Monique A.; Cogo-Moreira, Hugo; Rossi, Silvia

2018-04-01

Astronomy is in the era of large spectroscopy surveys, with the spectra of hundreds of thousands of stars in the Galaxy being collected. Although most of these surveys have low or medium resolution, which makes precise abundance measurements not possible, there is still important information to be extracted from the available data. Our aim is to identify chemically distinct classes among metal-poor stars, observed by the Sloan Digital Sky Survey, using line indices. The present work focused on carbon-enhanced metal-poor (CEMP) stars and their subclasses. We applied the latent profile analysis technique to line indices for carbon, barium, iron and europium, in order to separate the sample into classes with similar chemical signatures. This technique provides not only the number of possible groups but also the probability of each object to belong to each class. The method was able to distinguish at least two classes among the observed sample, with one of them being probable CEMP stars enriched in s-process elements. However, it was not able to separate CEMP-no stars from the rest of the sample. Latent profile analysis is a powerful model-based tool to be used in the identification of patterns in astrophysics. Our tests show the potential of the technique for the attainment of additional chemical information from `poor' data.

Rural N(SO) and German middle-class mothers' interaction with their 3- and 6-month-old infants: A longitudinal cross-cultural analysis.

PubMed

Lamm, Bettina; Gudi, Helene; Fassbender, Ina; Freitag, Claudia; Graf, Frauke; Goertz, Claudia; Spangler, Sibylle; Teubert, Manuel; Knopf, Monika; Lohaus, Arnold; Schwarzer, Gudrun; Keller, Heidi

2015-08-01

This study aims to analyze culture-specific development of maternal interactional behavior longitudinally. Rural Cameroonian Nso mothers (n = 72) and German middle-class mothers (n = 106) were observed in free-play interactions with their 3- and 6-month-old infants. Results reveal the expected shift from a social to a nonsocial focus only in the German middle-class mothers' play interactions but not the rural Nso mothers' play. Nso mothers continue their proximal interactional style with a focus on body contact and body stimulation, whereas German middle-class mothers prefer a distal style of interaction with increasing object-centeredness. These cultural differences are in line with broader cultural models and become more accentuated as the infants grow older. (c) 2015 APA, all rights reserved).

Promoting motivation through mode of instruction: The relationship between use of affective teaching techniques and motivation to learn science

NASA Astrophysics Data System (ADS)

Sanchez Rivera, Yamil

The purpose of this study is to add to what we know about the affective domain and to create a valid instrument for future studies. The Motivation to Learn Science (MLS) Inventory is based on Krathwohl's Taxonomy of Affective Behaviors (Krathwohl et al., 1964). The results of the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) demonstrated that the MLS Inventory is a valid and reliable instrument. Therefore, the MLS Inventory is a uni-dimensional instrument composed of 9 items with convergent validity (no divergence). The instrument had a high Chronbach Alpha value of .898 during the EFA analysis and .919 with the CFA analysis. Factor loadings on the 9 items ranged from .617 to .800. Standardized regression weights ranged from .639 to .835 in the CFA analysis. Various indices (RMSEA = .033; NFI = .987; GFI = .985; CFI = 1.000) demonstrated a good fitness of the proposed model. Hierarchical linear modeling was used to statistical analyze data where students' motivation to learn science scores (level-1) were nested within teachers (level-2). The analysis was geared toward identifying if teachers' use of affective behavior (a level-2 classroom variable) was significantly related with students' MLS scores (level-1 criterion variable). Model testing proceeded in three phases: intercept-only model, means-as-outcome model, and a random-regression coefficient model. The intercept-only model revealed an intra-class correlation coefficient of .224 with an estimated reliability of .726. Therefore, data suggested that only 22.4% of the variance in MLS scores is between-classes and the remaining 77.6% is at the student-level. Due to the significant variance in MLS scores, X2(62.756, p<.0001), teachers' TAB scores were added as a level-2 predictor. The regression coefficient was non-significant (p>.05). Therefore, the teachers' self-reported use of affective behaviors was not a significant predictor of students' motivation to learn science.

[Assessment on the ability of emergency response at the county center for disease control and prevention level in flooding-prone areas].

PubMed

Chen, Wei; Zeng, Guang

2006-02-01

To establish a comprehensive assessment model on the ability of emergency response within the public health system in flooding-prone areas. A hierarchy process theory was used to establish the initial assessing framework. Delphi method was used to screen and choose the ultimate indicators and their weights before an assessment model was set up under the 'synthetic scored method' to assess the ability of the emergency response among twenty county public health units. We then used the 'analysis of variation (ANOVA)' methodology to test the feasibility of distinguishing the ability of emergency response among different county health units and correlation analysis was used to assess the independence of indicators in the assessing model. A comprehensive model was then established including twenty first-class indicators and fifty-six second-class indicators and the degree of ability to emergency response with flooding of public health units was evaluated. There were five public health units having higher, ten having moderate but five with lower levels on emergency response. The assessment model was proved to be a good method in differentiating the ability of public health units, using independent indicators. The assessment model which we established seemed to be practical and reliable.

A Teaching Model for Scaffolding 4th Grade Students' Scientific Explanation Writing

NASA Astrophysics Data System (ADS)

Yang, Hsiu-Ting; Wang, Kuo-Hua

2014-08-01

Improving students scientific explanations is one major goal of science education. Both writing activities and concept mapping are reported as effective strategies for enhancing student learning of science. The purpose of this study was to examine the effect of a teaching model, named the DCI model, which integrates a Descriptive explanation writing activity, Concept mapping, and an Interpretive explanation writing activity, is introduced in a 4th grade science class to see if it would improve students' scientific explanations and understanding. A quasi-experimental design, including a non-randomized comparison group and a pre- and post-test design, was adopted for this study. An experimental group of 25 students were taught using the DCI teaching model, while a comparison group received a traditional lecture teaching. A rubric and content analysis was used to assess students' scientific explanations. The independent sample t test was used to measure difference in conceptual understanding between the two groups, before and after instruction. Then, the paired t test analysis was used to understand the promotion of the DCI teaching model. The results showed that students in the experimental group performed better than students in the comparison group, both in scientific concept understanding and explanation. Suggestions for using concept mapping and writing activities (the DCI teaching model) in science classes are provided in this study.

Poverty and Depression among Men: The Social Class Worldview Model and Counseling Implications.

ERIC Educational Resources Information Center

Liu, William M.

This paper outlines a theory for understanding social class in men's lives, and argues that poverty and depression are a function of social class and internalized classism. It begins by defining poverty, then explains the Social Class Worldview Model, which is a subjective social class model, and the Modern Classism Theory, which allows clinicians…

Reliability Analysis for AFTI-F16 SRFCS Using ASSIST and SURE

NASA Technical Reports Server (NTRS)

Wu, N. Eva

2001-01-01

This paper reports the results of a study on reliability analysis of an AFTI-16 Self-Repairing Flight Control System (SRFCS) using software tools SURE (Semi-Markov Unreliability Range Evaluator and ASSIST (Abstract Semi-Markov Specification Interface to the SURE Tool). The purpose of the study is to investigate the potential utility of the software tools in the ongoing effort of the NASA Aviation Safety Program, where the class of systems must be extended beyond the originally intended serving class of electronic digital processors. The study concludes that SURE and ASSIST are applicable to reliability, analysis of flight control systems. They are especially efficient for sensitivity analysis that quantifies the dependence of system reliability on model parameters. The study also confirms an earlier finding on the dominant role of a parameter called a failure coverage. The paper will remark on issues related to the improvement of coverage and the optimization of redundancy level.

The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications.

PubMed

Ramnarine, Varune Rohan; Alshalalfa, Mohammed; Mo, Fan; Nabavi, Noushin; Erho, Nicholas; Takhar, Mandeep; Shukin, Robert; Brahmbhatt, Sonal; Gawronski, Alexander; Kobelev, Maxim; Nouri, Mannan; Lin, Dong; Tsai, Harrison; Lotan, Tamara L; Karnes, R Jefferey; Rubin, Mark A; Zoubeidi, Amina; Gleave, Martin E; Sahinalp, Cenk; Wyatt, Alexander W; Volik, Stanislav V; Beltran, Himisha; Davicioni, Elai; Wang, Yuzhuo; Collins, Colin C

2018-05-10

Treatment induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate resistant (mCRPC) prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and for most patients, results in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now the expression of lncRNAs during NEtD and their clinical associations were unexplored. We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores>2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include, E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome. To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

Disability pension by occupational class - the impact of work-related factors: The Hordaland Health Study Cohort

PubMed Central

2011-01-01

Background The social gradient in disability pension is well recognized, however mechanisms accounting for the gradient are largely unknown. The aim of this study was to examine the association between occupational class and subsequent disability pension among middle-aged men and women, and to what extent work-related factors accounted for the association. Methods A subsample (N = 7031) of the population-based Hordaland Health Study (HUSK) conducted in 1997-99, provided self-reported information on health and work-related factors, and were grouped in four strata by Erikson, Goldthorpe and Portocareros occupational class scheme. The authors obtained follow-up data on disability pension by linking the health survey to national registries of benefit (FD-trygd). They employed Cox regression analysis and adjusted for gender, health (medical conditions, mental health, self-perceived health, somatic symptoms) and work-related factors (working hours, years in current occupation, physical demands, job demands, job control). Results A strong gradient in disability pension by occupational class was found. In the fully adjusted model the risk (hazard ratio) ranged from 1.41 (95% CI 0.84 to 2.33) in the routine non-manual class, 1.87 (95% CI 1.07 to 3.27) in the skilled manual class and 2.12 (95% CI 1.14 to 3.95) in the unskilled manual class, employing the administrator and professional class as reference. In the gender and health-adjusted model work-related factors mediated the impact of occupational class on subsequent disability pension with 5% in the routine non-manual class, 26% in the skilled manual class and 24% in the unskilled manual class. The impact of job control and physical demands was modest, and mainly seen among skilled and unskilled manual workers. Conclusions Workers in the skilled and unskilled manual classes had a substantial unexplained risk of disability pension. Work-related factors only had a moderate impact on the disability risk. Literature indicates an accumulation of hazards in the manual classes. This should be taken into account when interpreting the gradient in disability pension. PMID:21619716

Disability pension by occupational class--the impact of work-related factors: the Hordaland Health Study Cohort.

PubMed

Haukenes, Inger; Mykletun, Arnstein; Knudsen, Ann Kristin; Hansen, Hans-Tore; Mæland, John Gunnar

2011-05-30

The social gradient in disability pension is well recognized, however mechanisms accounting for the gradient are largely unknown. The aim of this study was to examine the association between occupational class and subsequent disability pension among middle-aged men and women, and to what extent work-related factors accounted for the association. A subsample (N = 7031) of the population-based Hordaland Health Study (HUSK) conducted in 1997-99, provided self-reported information on health and work-related factors, and were grouped in four strata by Erikson, Goldthorpe and Portocareros occupational class scheme. The authors obtained follow-up data on disability pension by linking the health survey to national registries of benefit (FD-trygd). They employed Cox regression analysis and adjusted for gender, health (medical conditions, mental health, self-perceived health, somatic symptoms) and work-related factors (working hours, years in current occupation, physical demands, job demands, job control). A strong gradient in disability pension by occupational class was found. In the fully adjusted model the risk (hazard ratio) ranged from 1.41 (95% CI 0.84 to 2.33) in the routine non-manual class, 1.87 (95% CI 1.07 to 3.27) in the skilled manual class and 2.12 (95% CI 1.14 to 3.95) in the unskilled manual class, employing the administrator and professional class as reference. In the gender and health-adjusted model work-related factors mediated the impact of occupational class on subsequent disability pension with 5% in the routine non-manual class, 26% in the skilled manual class and 24% in the unskilled manual class. The impact of job control and physical demands was modest, and mainly seen among skilled and unskilled manual workers. Workers in the skilled and unskilled manual classes had a substantial unexplained risk of disability pension. Work-related factors only had a moderate impact on the disability risk. Literature indicates an accumulation of hazards in the manual classes. This should be taken into account when interpreting the gradient in disability pension.

Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

NASA Astrophysics Data System (ADS)

He, Xin; Frey, Eric C.

2007-03-01

Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

Alteration Mineralogy of Adirondack-class Rocks in Gusev Crater, Mars

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Ruff, S. W.

2009-12-01

The rock Adirondack is the type example of a class of basaltic rocks analyzed by the Mars Exploration Rover Spirit in Gusev crater. Thermal infrared spectra of Adirondack-class rocks acquired by the Mini-TES instrument are distinguishable from spectra of other rock classes by the presence of an emissivity peak at 430 cm-1 and a minimum near 510 cm-1, which are characteristic of olivine. This is the primary spectral class on the plains of Gusev, but spectra of rocks exhibiting similar low wavenumber spectral character have been acquired along the rover traverse in the Columbia Hills, and we have confirmed that these also are Adirondack-class. Linear mixture modeling of their infrared spectra (enabled by applying a correction for dust on the Mini-TES optics) suggests that they are mafic with sulfate minerals present as alteration phases (up to 25%) in the majority of these rocks, broadly consistent with APXS-measured chemistry. The RAT-brushed surface of an unusual plains rock referred to as Mazatzal exhibits a spectral shape and modeled mineralogy consistent with the absence of olivine and the presence of amorphous phases low in silica, and is a coating unlike any other observed on Mars. We have also used a previously-demonstrated factor analysis and target transformation (FATT) technique with Adirondack-class rock spectra to retrieve the spectral shapes of independently-varying components within the data set. Using this approach, we have identified four shapes attributable to two distinct surface components, fine particulate surface dust, and a second dust component similar to downwelling sky radiance and/or dust on the Mini-TES optics. The two surface shapes do not resemble those of the two canonical surface types measured from orbit. One of the surface shapes is very similar to that of the lherzolitic Shergottite ALH A77005. Preliminary linear mixture analysis of this shape shows that it is dominated by olivine (~57%, ~Fo45) and pyroxene (~28%), with minor amounts of oxides and basaltic glass (~15%). This ultramafic composition is similar to that derived from linear mixture modeling of the measured Mini-TES spectra, but differs in detail from the APXS-derived normative mineralogy and Mössbauer ol:px. These differences may be artifacts of the penetration depths and spot sizes of the measurements, or assumptions inherent in the conversions from chemistry and spectra to norms and abundances; work in progress is aimed at explaining these differences. The other shape is modeled with high-silica phases (29%), sulfates (~24%), olivine (~19%), pyroxene (~15%), and oxides (~12%), suggesting it represents a highly altered mineralogy. We linearly modeled the highest-quality measured spectra of Adirondack-class rocks using only the FATT-derived spectral shapes. Surface components are modeled by varying proportions of the two surface shapes, with all containing ≥40% of the ultramafic shape. These preliminary results suggest that Adirondack-class rocks are a single lithology exhibiting sulfate-bearing surface alteration that is variable from rock to rock. We are in the process of converting the mineralogies derived from measured and FATT-derived spectra into bulk oxides and will present quantitative comparisons with APXS data and qualitative comparisons with Mössbauer data.

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

NASA Astrophysics Data System (ADS)

Kar, Supratik; Roy, Juganta K.; Leszczynski, Jerzy

2017-06-01

Advances in solar cell technology require designing of new organic dye sensitizers for dye-sensitized solar cells with high power conversion efficiency to circumvent the disadvantages of silicon-based solar cells. In silico studies including quantitative structure-property relationship analysis combined with quantum chemical analysis were employed to understand the primary electron transfer mechanism and photo-physical properties of 273 arylamine organic dyes from 11 diverse chemical families explicit to iodine electrolyte. The direct quantitative structure-property relationship models enable identification of the essential electronic and structural attributes necessary for quantifying the molecular prerequisites of 11 classes of arylamine organic dyes, responsible for high power conversion efficiency of dye-sensitized solar cells. Tetrahydroquinoline, N,N'-dialkylaniline and indoline have been least explored classes under arylamine organic dyes for dye-sensitized solar cells. Therefore, the identified properties from the corresponding quantitative structure-property relationship models of the mentioned classes were employed in designing of "lead dyes". Followed by, a series of electrochemical and photo-physical parameters were computed for designed dyes to check the required variables for electron flow of dye-sensitized solar cells. The combined computational techniques yielded seven promising lead dyes each for all three chemical classes considered. Significant (130, 183, and 46%) increment in predicted %power conversion efficiency was observed comparing with the existing dye with highest experimental %power conversion efficiency value for tetrahydroquinoline, N,N'-dialkylaniline and indoline, respectively maintaining required electrochemical parameters.

Separate class true discovery rate degree of association sets for biomarker identification.

PubMed

Crager, Michael R; Ahmed, Murat

2014-01-01

In 2008, Efron showed that biological features in a high-dimensional study can be divided into classes and a separate false discovery rate (FDR) analysis can be conducted in each class using information from the entire set of features to assess the FDR within each class. We apply this separate class approach to true discovery rate degree of association (TDRDA) set analysis, which is used in clinical-genomic studies to identify sets of biomarkers having strong association with clinical outcome or state while controlling the FDR. Careful choice of classes based on prior information can increase the identification power of the separate class analysis relative to the overall analysis.

SURE reliability analysis: Program and mathematics

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; White, Allan L.

1988-01-01

The SURE program is a new reliability analysis tool for ultrareliable computer system architectures. The computational methods on which the program is based provide an efficient means for computing accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. Once a semi-Markov model is described using a simple input language, the SURE program automatically computes the upper and lower bounds on the probability of system failure. A parameter of the model can be specified as a variable over a range of values directing the SURE program to perform a sensitivity analysis automatically. This feature, along with the speed of the program, makes it especially useful as a design tool.

Ultralight axion in supersymmetry and strings and cosmology at small scales

NASA Astrophysics Data System (ADS)

Halverson, James; Long, Cody; Nath, Pran

2017-09-01

Dynamical mechanisms to generate an ultralight axion of mass ˜10-21- 10-22 eV in supergravity and strings are discussed. An ultralight particle of this mass provides a candidate for dark matter that may play a role for cosmology at scales of 10 kpc or less. An effective operator approach for the axion mass provides a general framework for models of ultralight axions, and in one case recovers the scale 10-21- 10-22 eV as the electroweak scale times the square of the hierarchy with an O (1 ) Wilson coefficient. We discuss several classes of models realizing this framework where an ultralight axion of the necessary size can be generated. In one class of supersymmetric models an ultralight axion is generated by instanton-like effects. In the second class higher-dimensional operators involving couplings of Higgs, standard model singlets, and axion fields naturally lead to an ultralight axion. Further, for the class of models considered the hierarchy between the ultralight scale and the weak scale is maintained. We also discuss the generation of an ultralight scale within string-based models. In the single-modulus Kachru-Kallosh-Linde-Trivedi moduli stabilization scheme an ultralight axion would require an ultralow weak scale. However, within the large volume scenario, the desired hierarchy between the axion scale and the weak scale is achieved. A general analysis of couplings of Higgs fields to instantons within the string framework is discussed and it is shown that the condition necessary for achieving such couplings is the existence of vector-like zero modes of the instanton. Some of the phenomenological aspects of these models are also discussed.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Modeling and performance analysis of QoS data

NASA Astrophysics Data System (ADS)

Strzeciwilk, Dariusz; Zuberek, Włodzimierz M.

2016-09-01

The article presents the results of modeling and analysis of data transmission performance on systems that support quality of service. Models are designed and tested, taking into account multiservice network architecture, i.e. supporting the transmission of data related to different classes of traffic. Studied were mechanisms of traffic shaping systems, which are based on the Priority Queuing with an integrated source of data and the various sources of data that is generated. Discussed were the basic problems of the architecture supporting QoS and queuing systems. Designed and built were models based on Petri nets, supported by temporal logics. The use of simulation tools was to verify the mechanisms of shaping traffic with the applied queuing algorithms. It is shown that temporal models of Petri nets can be effectively used in the modeling and analysis of the performance of computer networks.

Refining Pathways: A Model Comparison Approach

PubMed Central

Moffa, Giusi; Erdmann, Gerrit; Voloshanenko, Oksana; Hundsrucker, Christian; Sadeh, Mohammad J.; Boutros, Michael; Spang, Rainer

2016-01-01

Cellular signalling pathways consolidate multiple molecular interactions into working models of signal propagation, amplification, and modulation. They are described and visualized as networks. Adjusting network topologies to experimental data is a key goal of systems biology. While network reconstruction algorithms like nested effects models are well established tools of computational biology, their data requirements can be prohibitive for their practical use. In this paper we suggest focussing on well defined aspects of a pathway and develop the computational tools to do so. We adapt the framework of nested effect models to focus on a specific aspect of activated Wnt signalling in HCT116 colon cancer cells: Does the activation of Wnt target genes depend on the secretion of Wnt ligands or do mutations in the signalling molecule β-catenin make this activation independent from them? We framed this question into two competing classes of models: Models that depend on Wnt ligands secretion versus those that do not. The model classes translate into restrictions of the pathways in the network topology. Wnt dependent models are more flexible than Wnt independent models. Bayes factors are the standard Bayesian tool to compare different models fairly on the data evidence. In our analysis, the Bayes factors depend on the number of potential Wnt signalling target genes included in the models. Stability analysis with respect to this number showed that the data strongly favours Wnt ligands dependent models for all realistic numbers of target genes. PMID:27248690

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

PubMed

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

Hypersonic vehicle control law development using H infinity and mu-synthesis

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Chowdhry, Rajiv S.; Mcminn, John D.; Shaughnessy, John D.

1992-01-01

Applicability and effectiveness of robust control techniques to a single-stage-to-orbit (SSTO) airbreathing hypersonic vehicle on an ascent accelerating path and their effectiveness are explored in this paper. An SSTO control system design problem, requiring high accuracy tracking of velocity and altitude commands while limiting angle of attack oscillations, minimizing control power usage and stabilizing the vehicle all in the presence of atmospheric turbulence and uncertainty in the system, was formulated to compare results of the control designs using H infinity and mu-synthesis procedures. The math model, an integrated flight/propulsion dynamic model of a conical accelerator class vehicle, was linearized as the vehicle accelerated through Mach 8. Controller analysis was conducted using the singular value technique and the mu-analysis approach. Analysis results were obtained in both the frequency and the time domains. The results clearly demonstrate the inherent advantages of the structured singular value framework for this class of problems. Since payload performance margins are so critical for the SSTO mission, it is crucial that adequate stability margins be provided without sacrificing any payload mass.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

PubMed

Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

2007-01-01

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

A Real-World Network Modeling Project

DTIC Science & Technology

2014-02-12

about the project, which accounts for a third of their class grade. As can be expected, giving substantial weight to the project increases active student...analysis, humanitarian aid warehouses, Israeli traffic analysis, London Olympic Games transport, medical evacuation, Monterey fire department...responsiveness, Monterey Peninsula evacuation, natural gas pipeline transport, rail transport of new cars, ski lifts for Keystone Colorado, small boat attack

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Forecasting of Seasonal Rainfall using ENSO and IOD teleconnection with Classification Models

NASA Astrophysics Data System (ADS)

De Silva, T.; Hornberger, G. M.

2017-12-01

Seasonal to annual forecasts of precipitation patterns are very important for water infrastructure management. In particular, such forecasts can be used to inform decisions about the operation of multipurpose reservoir systems in the face of changing climate conditions. Success in making useful forecasts often is achieved by considering climate teleconnections such as the El-Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) as related to sea surface temperature variations. We present an analysis to explore the utility of using rainfall relationships in Sri Lanka with ENSO and IOD to predict rainfall to the Mahaweli, river basin. Forecasting of rainfall as classes - above normal, normal, and below normal - can be useful for water resource management decision making. Quadratic discrimination analysis (QDA) and random forest models are used to identify the patterns of rainfall classes with respect to ENSO and IOD indices. These models can be used to forecast the likelihood of areal rainfall anomalies using predicted climate indices. Results can be used for decisions regarding allocation of water for agriculture and electricity generation within the Mahaweli project of Sri Lanka.

When do latent class models overstate accuracy for diagnostic and other classifiers in the absence of a gold standard?

PubMed

Spencer, Bruce D

2012-06-01

Latent class models are increasingly used to assess the accuracy of medical diagnostic tests and other classifications when no gold standard is available and the true state is unknown. When the latent class is treated as the true class, the latent class models provide measures of components of accuracy including specificity and sensitivity and their complements, type I and type II error rates. The error rates according to the latent class model differ from the true error rates, however, and empirical comparisons with a gold standard suggest the true error rates often are larger. We investigate conditions under which the true type I and type II error rates are larger than those provided by the latent class models. Results from Uebersax (1988, Psychological Bulletin 104, 405-416) are extended to accommodate random effects and covariates affecting the responses. The results are important for interpreting the results of latent class analyses. An error decomposition is presented that incorporates an error component from invalidity of the latent class model. © 2011, The International Biometric Society.

A Latent Class Analysis of Gambling Activity Patterns in a Canadian University Sample of Emerging Adults: Socio-demographic, Motivational, and Mental Health Correlates.

PubMed

Sanscartier, Matthew D; Edgerton, Jason D; Roberts, Lance W

2017-12-02

This analysis of gambling habits of Canadian university students (ages 18-25) dovetails two recent developments in the field of gambling studies. First, the popularity of latent class analysis to identify heterogeneous classes of gambling patterns in different populations; second, the validation of the Gambling Motives Questionnaire (with financial motives) among university students-specifically to understand both how and why emerging adults gamble. Our results support a four-class model of gambling activity patterns, consisting of female-preponderant casual and chance-based gambling groups, and male-preponderant skill-based and extensive gambling groups. Each class shows a specific combination of motives, underscoring the necessity for nuanced responses to problem gambling among emerging adults. More specifically, gambling for the skill-based group appears primarily to be a source of thrill and a way to cope; for the chance-based group, gambling appears but one symptom of a set of wider issues involving depression, anxiety, substance use, and low self-esteem; while extensive gamblers seem to seek excitement, sociality, and coping, in that order. Only the chance-based group was significantly more likely than casual gamblers to be motivated by financial reasons. Situating our analysis in the literature, we suggest that interventions for the predominantly male subtypes should address gambling directly (e.g. re-focusing excitement seeking into other activities, instilling more productive coping mechanisms) while interventions for predominantly female subtypes should address low self-esteem in conjunction with depression, substance abuse, and problematic levels of gambling. We conclude future research should focus on links between self-esteem, depression, substance abuse, and financial motives for gambling among female emerging adults.

Cartographic Modeling: Computer-assisted Analysis of Spatially Defined Neighborhoods

NASA Technical Reports Server (NTRS)

Berry, J. K.; Tomlin, C. D.

1982-01-01

Cartographic models addressing a wide variety of applications are composed of fundamental map processing operations. These primitive operations are neither data base nor application-specific. By organizing the set of operations into a mathematical-like structure, the basis for a generalized cartographic modeling framework can be developed. Among the major classes of primitive operations are those associated with reclassifying map categories, overlaying maps, determining distance and connectivity, and characterizing cartographic neighborhoods. The conceptual framework of cartographic modeling is established and techniques for characterizing neighborhoods are used as a means of demonstrating some of the more sophisticated procedures of computer-assisted map analysis. A cartographic model for assessing effective roundwood supply is briefly described as an example of a computer analysis. Most of the techniques described have been implemented as part of the map analysis package developed at the Yale School of Forestry and Environmental Studies.

Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis.

PubMed

Roshan, Abdul-Rahman A; Gad, Haidy A; El-Ahmady, Sherweit H; Khanbash, Mohamed S; Abou-Shoer, Mohamed I; Al-Azizi, Mohamed M

2013-08-14

This work describes a simple model developed for the authentication of monofloral Yemeni Sidr honey using UV spectroscopy together with chemometric techniques of hierarchical cluster analysis (HCA), principal component analysis (PCA), and soft independent modeling of class analogy (SIMCA). The model was constructed using 13 genuine Sidr honey samples and challenged with 25 honey samples of different botanical origins. HCA and PCA were successfully able to present a preliminary clustering pattern to segregate the genuine Sidr samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model presented a clear demarcation of the samples and was used to identify genuine Sidr honey samples as well as detect admixture with lower priced polyfloral honey by detection limits >10%. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other honey types worldwide.

Literacy Models and the Reconstruction of History Education: A Comparative Discourse Analysis of Two Lesson Plans

ERIC Educational Resources Information Center

Collin, Ross; Reich, Gabriel A.

2015-01-01

This article presents discourse analyses of two lesson plans designed for secondary school history classes. Although the plans focus on the same topic, they rely on different models of content area literacy: disciplinary literacy, or reading and writing like experts in a given domain, and critical literacy, or reading and writing to address…

Dynamical Analysis of Density-dependent Selection in a Discrete one-island Migration Model

Treesearch

James H. Roberds; James F. Selgrade

2000-01-01

A system of non-linear difference equations is used to model the effects of density-dependent selection and migration in a population characterized by two alleles at a single gene locus. Results for the existence and stability of polymorphic equilibria are established. Properties for a genetically important class of equilibria associated with complete dominance in...

Heavy charged scalars from c\\overline{s} fusion: a generic search strategy applied to a 3HDM with U(1) × U(1) family symmetry

NASA Astrophysics Data System (ADS)

Camargo-Molina, José Eliel; Mandal, Tanumoy; Pasechnik, Roman; Wessén, Jonas

2018-03-01

We describe a class of three Higgs doublet models (3HDMs) with a softly broken U(1) × U(1) family symmetry that enforces a Cabibbo-like quark mixing while forbidding tree-level flavour changing neutral currents. The hierarchy in the observed quark masses is partly explained by a softer hierarchy in the vacuum expectation values of the three Higgs doublets. As a consequence, the physical scalar spectrum contains a Standard Model (SM) like Higgs boson h 125 while exotic scalars couple the strongest to the second quark family, leading to rather unconventional discovery channels that could be probed at the Large Hadron Collider. In particular, we describe a search strategy for the lightest charged Higgs boson H ±, through the process c\\overline{s}\\to {H}+\\to {W}+{h}_{125} , using a multivariate analysis that leads to an excellent discriminatory power against the SM background. Although the analysis is applied to the proposed class of 3HDMs, we employ a model-independent formulation such that it can be applied to any other model with the same discovery channel.

Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

PubMed

Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

2016-02-29

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Probabilistic Scenario-based Seismic Risk Analysis for Critical Infrastructures Method and Application for a Nuclear Power Plant

NASA Astrophysics Data System (ADS)

Klügel, J.

2006-12-01

Deterministic scenario-based seismic hazard analysis has a long tradition in earthquake engineering for developing the design basis of critical infrastructures like dams, transport infrastructures, chemical plants and nuclear power plants. For many applications besides of the design of infrastructures it is of interest to assess the efficiency of the design measures taken. These applications require a method allowing to perform a meaningful quantitative risk analysis. A new method for a probabilistic scenario-based seismic risk analysis has been developed based on a probabilistic extension of proven deterministic methods like the MCE- methodology. The input data required for the method are entirely based on the information which is necessary to perform any meaningful seismic hazard analysis. The method is based on the probabilistic risk analysis approach common for applications in nuclear technology developed originally by Kaplan & Garrick (1981). It is based (1) on a classification of earthquake events into different size classes (by magnitude), (2) the evaluation of the frequency of occurrence of events, assigned to the different classes (frequency of initiating events, (3) the development of bounding critical scenarios assigned to each class based on the solution of an optimization problem and (4) in the evaluation of the conditional probability of exceedance of critical design parameters (vulnerability analysis). The advantage of the method in comparison with traditional PSHA consists in (1) its flexibility, allowing to use different probabilistic models for earthquake occurrence as well as to incorporate advanced physical models into the analysis, (2) in the mathematically consistent treatment of uncertainties, and (3) in the explicit consideration of the lifetime of the critical structure as a criterion to formulate different risk goals. The method was applied for the evaluation of the risk of production interruption losses of a nuclear power plant during its residual lifetime.

Patterns of harm reduction service utilization and HIV incidence among people who inject drugs in Ukraine: A two-part latent profile analysis.

PubMed

Ompad, Danielle C; Wang, Jiayu; Dumchev, Konstantin; Barska, Julia; Samko, Maria; Zeziulin, Oleksandr; Saliuk, Tetiana; Varetska, Olga; DeHovitz, Jack

2017-05-01

Program utilization patterns are described within a large network of harm reduction service providers in Ukraine. The relationship between utilization patterns and HIV incidence is determined among people who inject drugs (PWID) controlling for oblast-level HIV incidence and treatment/syringe coverage. Data were extracted from the network's monitoring and evaluation database (January 2011-September 2014, n=327,758 clients). Latent profile analysis was used to determine harm reduction utilization patterns using the number of HIV tests received annually and the number of condoms, syringes, and services (i.e., information and counseling sessions) received monthly over a year. Cox proportional hazards regression determined the relations between HIV seroconversion and utilization class membership. In the final 4-class model, class 1 (34.0% of clients) received 0.1 HIV tests, 1.3 syringes, 0.6 condom and minimal counseling and information sessions per month; class 2 (33.6%) received 8.6 syringes, 3.2 condoms, and 0.5 HIV tests and counseling and information sessions; class 3 (19.1%) received 1 HIV test, 11.9 syringes, 4.3 condoms, and 0.7 information and counseling sessions; class 4 (13.3%) received 1 HIV test, 26.1 syringes, 10.3 condoms, and 1.8 information and 1.9 counseling sessions. Class 4 clients had significantly decreased risk for HIV seroconversion as compared to those in class 1 after controlling for oblast-level characteristics. Injection drug use continues to be a major mode of HIV transmission in Ukraine, making evaluation of harm reduction efforts in reducing HIV incidence among PWID critical. These analyses suggest that receiving more syringes and condoms decreased risk of HIV. Scaling up HIV testing and harm reduction services is warranted. Copyright © 2016. Published by Elsevier B.V.

Dietary Flavonoid and Lignan Intake and Mortality in Prospective Cohort Studies: Systematic Review and Dose-Response Meta-Analysis.

PubMed

Grosso, Giuseppe; Micek, Agnieszka; Godos, Justyna; Pajak, Andrzej; Sciacca, Salvatore; Galvano, Fabio; Giovannucci, Edward L

2017-06-15

Recent evidence has suggested that flavonoid and lignan intake may be associated with decreased risk of chronic and degenerative diseases. The aim of this meta-analysis was to assess the association between dietary flavonoid and lignan intake and all-cause and cardiovascular disease (CVD) mortality in prospective cohort studies. A systematic search was conducted in electronic databases to identify studies published from January 1996 to December 2015 that satisfied inclusion/exclusion criteria. Risk ratios and 95% confidence intervals were extracted and analyzed using a random-effects model. Nonlinear dose-response analysis was modeled by using restricted cubic splines. The inclusion criteria were met by 22 prospective studies exploring various flavonoid and lignan classes. Compared with lower intake, high consumption of total flavonoids was associated with decreased risk of all-cause mortality (risk ratio = 0.74, 95% confidence intervals: 0.55, 0.99), while a 100-mg/day increment in intake led to a (linear) decreased risk of 6% and 4% of all-cause and CVD mortality, respectively. Among flavonoid classes, significant results were obtained for intakes of flavonols, flavones, flavanones, anthocyanidins, and proanthocyanidins. Only limited evidence was available on flavonoid classes and lignans and all-cause mortality. Findings from this meta-analysis indicated that dietary flavonoids are associated with decreased risk of all-cause and CVD mortality. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The class analysis of poverty: a response to Tony Novak.

PubMed

Wright, E O

1996-01-01

In responding to Tony Novak's criticisms of his earlier article "The Class Analysis of Poverty," the author makes four principle points. First, contrary to Novak's views, a class analysis to poverty should define poverty in terms of both income-poverty and asset-poverty. Second, while Novak is correct that the term "underclass" often has a pejorative meaning, it remains an important concept for identifying segments of the population that are deeply oppressed economically, but not exploited. Third, the concepts of class analysis must be elaborated at a variety of levels of abstraction, not simply the highest level of the pure "mode of production," as is implied by Novak's arguments. Finally, class analysis must acknowledge and conceptualize the specific forms of complexity of contemporary class structures, which is impossible if it restricts its class concepts to a simple polarized notion.

Longitudinal Effects of Early Childhood Maltreatment on Co-Occurring Substance Misuse and Mental Health Problems in Adulthood: The Role of Adolescent Alcohol Use and Depression

PubMed Central

Skinner, Martie L.; Hong, Seunghye; Herrenkohl, Todd I.; Brown, Eric C.; Lee, Jungeun Olivia; Jung, Hyunzee

2016-01-01

Objective: This study tested a developmental model in which subtypes of childhood maltreatment were hypothesized to have direct and indirect effects on co-occurring depression, anxiety, and substance misuse in adulthood. Indirect effects involved adolescent alcohol use and depression, which were included as mediators in the tested models. Method: This prospective longitudinal study (N = 332; 52.4% male) followed the participants from childhood (18 months to 6 years of age) to adulthood (31–41 years old, M = 36.21). Maltreatment subtypes included parent-reported physical and emotional abuse and child-reported sexual abuse. Adult outcomes included measures of substance misuse and mental health (i.e., depression and anxiety). Latent class analysis and structural equation models were used to identify classes of substance misuse and mental health co-occurrence and to test mediating effects of adolescent alcohol use and depression. Results: Three classes were identified: (a) low risk of substance misuse and low mental health symptoms, (b) moderate substance misuse risk and mild depression and anxiety, and (c) moderate substance misuse risk and moderate to high depression and anxiety. Structural models showed that effects of childhood sexual abuse were fully mediated by adolescent alcohol use and depression. Physical abuse increased adolescent depression but did not have direct or indirect effects on adult outcome classes. Emotional abuse had a direct effect on the adult classes. Conclusions: Children exposed to severe emotional abuse are at higher risk for comorbid substance misuse, depression, and anxiety into their mid-30s, after taking into account evidence of alcohol use and depression during adolescence. Sexual and physical abuse have more proximal effects on adolescent alcohol use and depression, which then influence the risk of adult problems. PMID:27172579

Information-theoretic metric as a tool to investigate nonclassical correlations

NASA Astrophysics Data System (ADS)

Rudolph, Alexander L.; Lamine, Brahim; Joyce, Michael; Vignolles, Hélène; Consiglio, David

2014-06-01

We report on a project to introduce interactive learning strategies (ILS) to physics classes at the Université Pierre et Marie Curie, one of the leading science universities in France. In Spring 2012, instructors in two large introductory classes, first-year, second-semester mechanics, and second-year introductory electricity and magnetism, enrolling approximately 500 and 250 students, respectively, introduced ILS into some, but not all, of the sections of each class. The specific ILS utilized were think-pair-share questions and Peer Instruction in the main lecture classrooms, and University of Washington Tutorials for Introductory Physics in recitation sections. Pre- and postinstruction assessments [Force Concept Inventory (FCI) and Conceptual Survey of Electricity and Magnetism (CSEM), respectively] were given, along with a series of demographic questions. Since not all lecture or recitation sections in these classes used ILS, we were able to compare the results of the FCI and CSEM between interactive and noninteractive classes taught simultaneously with the same curriculum. We also analyzed final exam results, as well as the results of student and instructor attitude surveys between classes. In our analysis, we argue that multiple linear regression modeling is superior to other common analysis tools, including normalized gain. Our results show that ILS are effective at improving student learning by all measures used: research-validated concept inventories and final exam scores, on both conceptual and traditional problem-solving questions. Multiple linear regression analysis reveals that interactivity in the classroom is a significant predictor of student learning, showing a similar or stronger relationship with student learning than such ascribed characteristics as parents’ education, and achieved characteristics such as grade point average and hours studied per week. Analysis of student and instructor attitudes shows that both groups believe that ILS improve student learning in the physics classroom and increase student engagement and motivation. All of the instructors who used ILS in this study plan to continue their use.