An innovation resistance factor model

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Siti Salwa Mohd Ishak

2016-09-01

Full Text Available The process and implementation strategy of information technology in construction is generally considered through the limiting prism of theoretical contexts generated from innovation diffusion and acceptance. This research argues that more attention should be given to understanding the positive effects of resistance. The study develops a theoretical framing for the Integrated Resistance Factor Model (IRFM. The framing uses a combination of diffusion of innovation theory, technology acceptance model and social network perspective. The model is tested to identify the most significant resistance factors using Partial Least Square (PLS technique. All constructs proposed in the model are found to be significant, valid and consistent with the theoretical framework. IRFM is shown to be an effective and appropriate model of user resistance factors. The most critical factors to influence technology resistance in the online project information management system (OPIMS context are: support from leaders and peers, complexity of the technology, compatibility with key work practices; and pre-trial of the technology before it is actually deployed. The study provides a new model for further research in technology innovation specific to the construction industry.

Assessing vulnerability to drought: identifying underlying factors across Europe

Science.gov (United States)

Urquijo, Julia; Gonzalez Tánago, Itziar; Ballesteros, Mario; De Stefano, Lucia

2015-04-01

Drought is considered one of the most severe and damaging natural hazards in terms of people and sectors affected and associated losses. Drought is a normal and recurrent climatic phenomenon that occurs worldwide, although its spatial and temporal characteristics vary significantly among climates. In the case of Europe, in the last thirty years, the region has suffered several drought events that have caused estimated economic damages over a €100 billion and have affected almost 20% of its territory and population. In recent years, there has been a growing awareness among experts and authorities of the need to shift from a reactive crisis approach to a drought risk management approach, as well as of the importance of designing and implementing policies, strategies and plans at country and river basin levels to deal with drought. The identification of whom and what is vulnerable to drought is a central aspect of drought risk mitigation and planning and several authors agree that societal vulnerability often determines drought risk more than the actual precipitation shortfalls. The final aim of a drought vulnerability assessment is to identify the underlying sources of drought impact, in order to develop policy options that help to enhance coping capacity and therefore to prevent drought impact. This study identifies and maps factors underlying vulnerability to drought across Europe. The identification of factors influencing vulnerability starts from the analysis of past drought impacts in four European socioeconomic sectors. This analysis, along with an extensive literature review, led to the selection of vulnerability factors that are both relevant and adequate for the European context. Adopting the IPCC model, vulnerability factors were grouped to describe exposure, sensitivity and adaptive capacity. The aggregation of these components has resulted in the mapping of vulnerability to drought across Europe at NUTS02 level. Final results have been compared with

Dimensional Model for Estimating Factors influencing Childhood Obesity: Path Analysis Based Modeling

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Maryam Kheirollahpour

2014-01-01

Full Text Available The main objective of this study is to identify and develop a comprehensive model which estimates and evaluates the overall relations among the factors that lead to weight gain in children by using structural equation modeling. The proposed models in this study explore the connection among the socioeconomic status of the family, parental feeding practice, and physical activity. Six structural models were tested to identify the direct and indirect relationship between the socioeconomic status and parental feeding practice general level of physical activity, and weight status of children. Finally, a comprehensive model was devised to show how these factors relate to each other as well as to the body mass index (BMI of the children simultaneously. Concerning the methodology of the current study, confirmatory factor analysis (CFA was applied to reveal the hidden (secondary effect of socioeconomic factors on feeding practice and ultimately on the weight status of the children and also to determine the degree of model fit. The comprehensive structural model tested in this study suggested that there are significant direct and indirect relationships among variables of interest. Moreover, the results suggest that parental feeding practice and physical activity are mediators in the structural model.

Identify and rank key factors influencing the adoption of cloud computing for a healthy Electronics

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Javad Shukuhy

2015-02-01

Full Text Available Cloud computing as a new technology with Internet infrastructure and new approaches can be significant benefits in providing medical services electronically. Aplying this technology in E-Health requires consideration of various factors. The main objective of this study is to identify and rank the factors influencing the adoption of e-health cloud. Based on the Technology-Organization-Environment (TOE framework and Human-Organization-Technology fit (HOT-fit model, 16 sub-factors were identified in four major factors. With survey of 60 experts, academics and experts in health information technology and with the help of fuzzy analytic hierarchy process had ranked these sub-factors and factors. In the literature, considering newness this study, no internal or external study, have not alluded these number of criteria. The results show that when deciding to adopt cloud computing in E-Health, respectively, must be considered technological, human, organizational and environmental factors.

Identifiability in stochastic models

CERN Document Server

1992-01-01

The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.

Identifying Some Risk Factors for the Time to Death of the Elderly Using the Semi-Parametric Blended Model of Survival Analysis With Competing Risks

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Samane Hajiabbasi

2018-01-01

Conclusion In single-variable fitting, age, history of myocardial infarction, history of stroke, and kidney problems were identified to have significant effects on the time to death of the elderly. Based on one-variable semi-parametric competing risk mixture fitted models, more significant risk factors for the time to death of elderly was identified when compared with a fitted multivariate mode to the data. This implies that the role of some independent variables can be explained by other independent variables.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

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Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Identifying Critical Factors Influencing the Rents of Public Rental Housing Delivery by PPPs: The Case of Nanjing

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Jingfeng Yuan

2017-02-01

Full Text Available The occupancy rate of Public Rental Housing (PRH in China is relatively low due to the unreasonable rents. At the same time, the development of PRH using Public Private Partnerships (PPPs increases the complexity of the rents. Therefore, the critical factors influencing the rents of PRH delivery by PPPs should be identified. Based on the comprehensive literature, this article identified a conceptual model for the factors influencing the rents of PRH delivery by PPPs in China, composed of 14 factors grouped in three factor packages, and discussed the relationships among three factor packages. A survey based on Nanjing was conducted to assess the relative significance of 14 factors. According to the results, six critical factors were identified: construction costs, household income, floor area and structure, transportation, market rents in the same district and public facilities. In addition, the proposed conceptual model had a good fit. The results also supported two hypothetical relationships among three factor packages: (1 the increase of the affordability of the target tenants had a positive effect on the increase of profits of private sectors; and (2 the increase of the affordability of the target tenants had a positive effect on the increase of level of the characteristics of PRH units. For future research, six critical factors and the relationships among three factor packages can be used to determine the reasonable rents for PRH delivery by PPPs in China.

Identifying community healthcare supports for the elderly and the factors affecting their aging care model preference: evidence from three districts of Beijing

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Tianyang Liu

2016-11-01

Full Text Available Abstract Background The Chinese tradition of filial piety, which prioritized family-based care for the elderly, is transitioning and elders can no longer necessarily rely on their children. The purpose of this study was to identify community support for the elderly, and analyze the factors that affect which model of old-age care elderly people dwelling in communities prefer. Methods We used the database “Health and Social Support of Elderly Population in Community”. Questionnaires were issued in 2013, covering 3 districts in Beijing. A group of 1036 people over 60 years in age were included in the study. The respondents’ profile variables were organized in Andersen’s Model and community healthcare resource factors were added. A multinomial logistic model was applied to analyze the factors associated with the desired aging care models. Results Cohabiting with children and relying on care from family was still the primary desired aging care model for seniors (78 %, followed by living in institutions (14.8 % and living at home independently while relying on community resources (7.2 %. The regression result indicated that predisposing, enabling and community factors were significantly associated with the aging care model preference. Specifically, compared with those who preferred to cohabit with children, those having higher education, fewer available family and friend helpers, and shorter distance to healthcare center were more likely to prefer to live independently and rely on community support. And compared with choosing to live in institutions, those having fewer available family and friend helpers and those living alone were more likely to prefer to live independently and rely on community. Need factors (health and disability condition were not significantly associated with desired aging care models, indicating that desired aging care models were passive choices resulted from the balancing of family and social caring resources

A multi-factor GIS method to identify optimal geographic locations for electric vehicle (EV) charging stations

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Zhang, Yongqin; Iman, Kory

2018-05-01

Fuel-based transportation is one of the major contributors to poor air quality in the United States. Electric Vehicle (EV) is potentially the cleanest transportation technology to our environment. This research developed a spatial suitability model to identify optimal geographic locations for installing EV charging stations for travelling public. The model takes into account a variety of positive and negative factors to identify prime locations for installing EV charging stations in Wasatch Front, Utah, where automobile emission causes severe air pollution due to atmospheric inversion condition near the valley floor. A walkable factor grid was created to store index scores from input factor layers to determine prime locations. 27 input factors including land use, demographics, employment centers etc. were analyzed. Each factor layer was analyzed to produce a summary statistic table to determine the site suitability. Potential locations that exhibit high EV charging usage were identified and scored. A hot spot map was created to demonstrate high, moderate, and low suitability areas for installing EV charging stations. A spatially well distributed EV charging system was then developed, aiming to reduce "range anxiety" from traveling public. This spatial methodology addresses the complex problem of locating and establishing a robust EV charging station infrastructure for decision makers to build a clean transportation infrastructure, and eventually improve environment pollution.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

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Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Identifiability of PBPK Models with Applications to ...

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Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss different types of identifiability that occur in PBPK models and give reasons why they occur. We particularly focus on how the mathematical structure of a PBPK model and lack of appropriate data can lead to statistical models in which it is impossible to estimate at least some parameters precisely. Methods are reviewed which can determine whether a purely linear PBPK model is globally identifiable. We propose a theorem which determines when identifiability at a set of finite and specific values of the mathematical PBPK model (global discrete identifiability) implies identifiability of the statistical model. However, we are unable to establish conditions that imply global discrete identifiability, and conclude that the only safe approach to analysis of PBPK models involves Bayesian analysis with truncated priors. Finally, computational issues regarding posterior simulations of PBPK models are discussed. The methodology is very general and can be applied to numerous PBPK models which can be expressed as linear time-invariant systems. A real data set of a PBPK model for exposure to dimethyl arsinic acid (DMA(V)) is presented to illustrate the proposed methodology. We consider statistical analy

Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

Science.gov (United States)

Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

2017-12-01

Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

Application of positive matrix factorization to identify potential sources of PAHs in soil of Dalian, China

International Nuclear Information System (INIS)

Wang Degao; Tian Fulin; Yang Meng; Liu Chenlin; Li Yifan

2009-01-01

Soil derived sources of polycyclic aromatic hydrocarbons (PAHs) in the region of Dalian, China were investigated using positive matrix factorization (PMF). Three factors were separated based on PMF for the statistical investigation of the datasets both in summer and winter. These factors were dominated by the pattern of single sources or groups of similar sources, showing seasonal and regional variations. The main sources of PAHs in Dalian soil in summer were the emissions from coal combustion average (46%), diesel engine (30%), and gasoline engine (24%). In winter, the main sources were the emissions from coal-fired boiler (72%), traffic average (20%), and gasoline engine (8%). These factors with strong seasonality indicated that coal combustion in winter and traffic exhaust in summer dominated the sources of PAHs in soil. These results suggested that PMF model was a proper approach to identify the sources of PAHs in soil. - PMF model is a proper approach to identify potential sources of PAHs in soil based on the PAH profiles measured in the field and those published in the literature.

Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals.

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Dicke, Theresa; Marsh, Herbert W; Riley, Philip; Parker, Philip D; Guo, Jiesi; Horwood, Marcus

2018-01-01

School principals world-wide report high levels of strain and attrition resulting in a shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that reflect principals' occupational wellbeing. For this purpose, we used the Copenhagen Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering multiple psychosocial factors identified by leading occupational stress theories. We evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant, and convergent validity using latent structural equation modeling in a large sample of Australian school principals ( N = 2,049). Results reveal that confirmatory factor analysis produced marginally acceptable model fit. A novel approach we call set exploratory structural equation modeling (set-ESEM), where cross-loadings were only allowed within a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM. Further multitrait-multimethod models based on the set-ESEM confirm the importance of a principal's psychosocial risk factors; Stressors and depression were related to demands and ill-being, while confidence and autonomy were related to wellbeing. We also show that working in the private sector was beneficial for showing a low psychosocial risk, while other demographics have little effects. Finally, we identify five latent risk profiles (high risk to no risk) of school principals based on all psychosocial factors. Overall the research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors.

Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

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Ling-Yun Dai

2017-01-01

Full Text Available Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, L2,1-norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes.

Comparison of Transcription Factor Binding Site Models

KAUST Repository

Bhuyan, Sharifulislam

2012-05-01

Modeling of transcription factor binding sites (TFBSs) and TFBS prediction on genomic sequences are important steps to elucidate transcription regulatory mechanism. Dependency of transcription regulation on a great number of factors such as chemical specificity, molecular structure, genomic and epigenetic characteristics, long distance interaction, makes this a challenging problem. Different experimental procedures generate evidence that DNA-binding domains of transcription factors show considerable DNA sequence specificity. Probabilistic modeling of TFBSs has been moderately successful in identifying patterns from a family of sequences. In this study, we compare performances of different probabilistic models and try to estimate their efficacy over experimental TFBSs data. We build a pipeline to calculate sensitivity and specificity from aligned TFBS sequences for several probabilistic models, such as Markov chains, hidden Markov models, Bayesian networks. Our work, containing relevant statistics and evaluation for the models, can help researchers to choose the most appropriate model for the problem at hand.

Structural Identifiability of Dynamic Systems Biology Models.

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Villaverde, Alejandro F; Barreiro, Antonio; Papachristodoulou, Antonis

2016-10-01

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.

Identifying Ghanaian Pre-Service Teachers' Readiness for Computer Use: A Technology Acceptance Model Approach

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Gyamfi, Stephen Adu

2016-01-01

This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. Data from 380 usable questionnaires were tested against the research model. Utilising the extended technology acceptance model (TAM) as a research framework, the study found that: pre-service teachers'…

Imaging-Based Screen Identifies Laminin 411 as a Physiologically Relevant Niche Factor with Importance for i-Hep Applications

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John Ong

2018-03-01

Full Text Available Summary: Use of hepatocytes derived from induced pluripotent stem cells (i-Heps is limited by their functional differences in comparison with primary cells. Extracellular niche factors likely play a critical role in bridging this gap. Using image-based characterization (high content analysis; HCA of freshly isolated hepatocytes from 17 human donors, we devised and validated an algorithm (Hepatocyte Likeness Index; HLI for comparing the hepatic properties of cells against a physiological gold standard. The HLI was then applied in a targeted screen of extracellular niche factors to identify substrates driving i-Heps closer to the standard. Laminin 411, the top hit, was validated in two additional induced pluripotent stem cell (iPSC lines, primary tissue, and an in vitro model of α1-antitrypsin deficiency. Cumulatively, these data provide a reference method to control and screen for i-Hep differentiation, identify Laminin 411 as a key niche protein, and underscore the importance of combining substrates, soluble factors, and HCA when developing iPSC applications. : Rashid and colleagues demonstrate the utility of a high-throughput imaging platform for identification of physiologically relevant extracellular niche factors to advance i-Heps closer to their primary tissue counterparts. The extracellular matrix (ECM protein screen identified Laminin 411 as an important niche factor facilitating i-Hep-based disease modeling in vitro. Keywords: iPS hepatocytes, extracellular niche, image-based screening, disease modeling, laminin

Identifiable risk factors in hepatitis b and c

International Nuclear Information System (INIS)

Rehman, F.U.; Pervez, A.; Rafiq, A.

2011-01-01

Background: Both hepatitis B and C are common infections affecting masses and are leading causes of Chronic Liver Disease in Pakistan as well as worldwide. In majority of cases both viral diseases spread by factors that are preventable. The present study is conducted to determine the identifiable risk factors in patients admitted with Chronic Hepatitis B and C. Methods: An observational study was carried out for a period of 6 months. All age groups and both sexes were included. The patients were interviewed and the identifiable risk factors were looked for. The standard methods for detection of Hepatitis B and C were used. Results: One-hundred and ten patients were studied from January to July 2009. Sixty-five patients had Hepatitis C, 35 had Hepatitis B, and 10 had both Hepatitis B and C. Ninety-three patients had a history of injections and transfusions etc., and 38 had surgical scars. Tattoos were present in 42 patients and nose and/or ear piercing marks were present in 28 patients. The number of risk factors increased in co-infection. Conclusion: There is a role of unhygienic health delivery practices, lack of awareness and resources for standard screening protocol for spread of Hepatitis B and C. (author)

Lung cancer and risk factors: how to identify phenotypic markers?

International Nuclear Information System (INIS)

Clement-Duchene, Christelle

2009-01-01

Lung cancer is the leading cause of death in the world. Most lung cancer are diagnosed at an advanced stage (IIIB and IV), with a poor prognosis. The main risk factors are well known like active smoking, and occupational exposure (asbestos), but 10 a 20% occur in never smokers. In this population, various studies have been conducted in order to identify possible risk factors, and although many have been identified, none seem to explain more than a small percentage of the cases. According to the histological types, adenocarcinoma is now the more frequent type, and its association with the main risk factors (tobacco exposure, asbestos exposure) is still studied. The tumoral location is associated with the exposure to the risk factors. Finally, the survival seems to be different between gender, and between smokers, and never smokers. All these characteristics are perhaps associated with different pathways of carcinogenesis. In this context, we have analyzed a cohort of 1493 patients with lung cancer in order to identify phenotypic markers, and to understand the mechanisms of the lung carcinogenesis. (author) [fr

Identifying patients with therapy-resistant depression by using factor analysis

DEFF Research Database (Denmark)

Andreasson, K; Liest, V; Lunde, M

2010-01-01

with transcranial pulsed electromagnetic fields (T-PEMF)], in which the relative effect as percentage of improvement during the treatment period was analysed. RESULTS: We identified 2 major factors, the first of which was a general factor. The second was a dual factor consisting of a depression subscale comprising...

Identifying influential factors of business process performance using dependency analysis

Science.gov (United States)

Wetzstein, Branimir; Leitner, Philipp; Rosenberg, Florian; Dustdar, Schahram; Leymann, Frank

2011-02-01

We present a comprehensive framework for identifying influential factors of business process performance. In particular, our approach combines monitoring of process events and Quality of Service (QoS) measurements with dependency analysis to effectively identify influential factors. The framework uses data mining techniques to construct tree structures to represent dependencies of a key performance indicator (KPI) on process and QoS metrics. These dependency trees allow business analysts to determine how process KPIs depend on lower-level process metrics and QoS characteristics of the IT infrastructure. The structure of the dependencies enables a drill-down analysis of single factors of influence to gain a deeper knowledge why certain KPI targets are not met.

Identifying risk factors for victimization among male prisoners in Taiwan.

Science.gov (United States)

Kuo, Shih-Ya; Cuvelier, Steven J; Huang, Yung-Shun

2014-02-01

This study identified risk factors for prison victimization in Taiwan with an application of Western literature and assessed the extent of its applicability in an Eastern context. The sample was drawn from four male prisons located in Northern, Central, Southern, and Eastern Taiwan; a total of 1,181 valid surveys were collected. The results generally support the major findings of the extant Western studies. Crowding, however, was not significantly associated with the risk of victimization in any of the statistical models, which might be related to the different experiences and living conditions in the free community between Taiwanese and American inmates. This study generated clear policy implications, which may reduce prison victimization and engender a greater sense of well-being in the prison environment.

Tombusvirus-yeast interactions identify conserved cell-intrinsic viral restriction factors

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Zsuzsanna eSasvari

2014-08-01

Full Text Available To combat viral infections, plants possess innate and adaptive immune pathways, such as RNA silencing, R gene and recessive gene-mediated resistance mechanisms. However, it is likely that additional cell-intrinsic restriction factors (CIRF are also involved in limiting plant virus replication. This review discusses novel CIRFs with antiviral functions, many of them RNA-binding proteins or affecting the RNA binding activities of viral replication proteins. The CIRFs against tombusviruses have been identified in yeast (Saccharomyces cerevisiae, which is developed as an advanced model organism. Grouping of the identified CIRFs based on their known cellular functions and subcellular localization in yeast reveals that TBSV replication is limited by a wide variety of host gene functions. Yeast proteins with the highest connectivity in the network map include the well-characterized Xrn1p 5’-3’ exoribonuclease, Act1p actin protein and Cse4p centromere protein. The protein network map also reveals an important interplay between the pro-viral Hsp70 cellular chaperone and the antiviral co-chaperones, and possibly key roles for the ribosomal or ribosome-associated factors. We discuss the antiviral functions of selected CIRFs, such as the RNA binding nucleolin, ribonucleases, WW-domain proteins, single- and multi-domain cyclophilins, TPR-domain co-chaperones and cellular ion pumps. These restriction factors frequently target the RNA-binding region in the viral replication proteins, thus interfering with the recruitment of the viral RNA for replication and the assembly of the membrane-bound viral replicase. Although many of the characterized CIRFs act directly against TBSV, we propose that the TPR-domain co-chaperones function as guardians of the cellular Hsp70 chaperone system, which is subverted efficiently by TBSV for viral replicase assembly in the absence of the TPR-domain co-chaperones.

Identifying influential factors on integrated marketing planning using information technology

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Karim Hamdi

2014-07-01

Full Text Available This paper presents an empirical investigation to identify important factors influencing integrated marketing planning using information technology. The proposed study designs a questionnaire for measuring integrated marketing planning, which consists of three categories of structural factors, behavioral factors and background factors. There are 40 questions associated with the proposed study in Likert scale. Cronbach alphas have been calculated for structural factors, behavioral factors and background factors as 0.89, 0.86 and 0.83, respectively. Using some statistical test, the study has confirmed the effects of three factors on integrated marketing. In addition, the implementation of Freedman test has revealed that structural factors were the most important factor followed by background factors and behavioral factors.

Identifiability Results for Several Classes of Linear Compartment Models.

Science.gov (United States)

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

Identifying factors affecting about outsourcing in paraclinical services

African Journals Online (AJOL)

Objective: Outsourcing refers to the transfer of services or functions to an outsider supplier, which controls them through a contract or cooperative. The main problem of senior managers in health organizations is determining the services which should be outsourced. The present study seeks to identify the factors that affect ...

Identifying risk factors for first-episode neck pain: A systematic review.

Science.gov (United States)

Kim, Rebecca; Wiest, Colin; Clark, Kelly; Cook, Chad; Horn, Maggie

2018-02-01

Neck pain affects 15.1% of the United States' general population every 3 months, and ranks fourth in global disability. Because of the tendency for neck pain to become a chronic issue, it is important to identify risk factors that could encourage prevention and early diagnosis. The purpose of this systematic review was to identify risk factors for a first episode of neck pain. Three databases were searched with key words such as "neck pain" and "first incidence." Risk factors from the resulting articles were reported as either a physical or psychosocial risk factor and ranked by the strength of their odds/risk/hazard ratio: empowering leadership, high perceived social climate, leisure physical activity, and cervical extensor endurance. Most risk factors found for neck pain were related to psychosocial characteristics, rather than physical characteristics. A number of these risk factors were mediating factors, suggesting that a prevention-based program may be useful in modifying the existence of the risk factors before the occurrence of neck pain. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying the effects of parameter uncertainty on the reliability of riverbank stability modelling

Science.gov (United States)

Samadi, A.; Amiri-Tokaldany, E.; Darby, S. E.

2009-05-01

Bank retreat is a key process in fluvial dynamics affecting a wide range of physical, ecological and socioeconomic issues in the fluvial environment. To predict the undesirable effects of bank retreat and to inform effective measures to prevent it, a wide range of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of driving and resisting forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the bank profile (bank height and angle), the geotechnical properties of the bank materials, as well as the hydrological status of the riverbanks. In this paper we evaluate the extent to which uncertainties in the parameterization of these controlling factors feed through to influence the reliability of the resulting bank stability estimate. This is achieved by employing a simple model of riverbank stability with respect to planar failure (which is the most common type of bank stability model) in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. These identified parameter value ranges are compared to empirically derived parameter uncertainties to determine whether they are likely to confound the reliability of the resulting bank stability calculations. Our results show that parameter uncertainties are typically high enough that the likelihood of generating unreliable predictions is typically very high (> ˜ 80% for predictions requiring a precision of < ± 15%). Because parameter uncertainties are derived primarily from the natural variability of the parameters, rather than measurement errors, much more careful attention should be paid to field sampling strategies, such that the parameter uncertainties and consequent prediction unreliabilities can be quantified more

Identifying best existing practice for characterization modeling in life cycle impact assessment

DEFF Research Database (Denmark)

Hauschild, Michael Zwicky; Goedkoop, Mark; Guinée, Jeroen

2013-01-01

Purpose: Life cycle impact assessment (LCIA) is a field of active development. The last decade has seen prolific publication of new impact assessment methods covering many different impact categories and providing characterization factors that often deviate from each other for the same substance...... and impact. The LCA standard ISO 14044 is rather general and unspecific in its requirements and offers little help to the LCA practitioner who needs to make a choice. With the aim to identify the best among existing characterization models and provide recommendations to the LCA practitioner, a study...... was performed for the Joint Research Centre of the European Commission (JRC). Methods Existing LCIA methods were collected and their individual characterization models identified at both midpoint and endpoint levels and supplemented with other environmental models of potential use for LCIA. No new developments...

Staphylococcus aureus virulence factors identified by using a high-throughput Caenorhabditis elegans-killing model.

Science.gov (United States)

Begun, Jakob; Sifri, Costi D; Goldman, Samuel; Calderwood, Stephen B; Ausubel, Frederick M

2005-02-01

Staphylococcus aureus is an important human pathogen that is also able to kill the model nematode Caenorhabditis elegans. We constructed a 2,950-member Tn917 transposon insertion library in S. aureus strain NCTC 8325. Twenty-one of these insertions exhibited attenuated C. elegans killing, and of these, 12 contained insertions in different genes or chromosomal locations. Ten of these 12 insertions showed attenuated killing phenotypes when transduced into two different S. aureus strains, and 5 of the 10 mutants correspond to genes that have not been previously identified in signature-tagged mutagenesis studies. These latter five mutants were tested in a murine renal abscess model, and one mutant harboring an insertion in nagD exhibited attenuated virulence. Interestingly, Tn917 was shown to have a very strong bias for insertions near the terminus of DNA replication.

Identifying risk factors associated with smear positivity of pulmonary tuberculosis in Kazakhstan.

Directory of Open Access Journals (Sweden)

Sabrina Hermosilla

Full Text Available Sputum smear-positive tuberculosis (TB patients have a high risk of transmission and are of great epidemiological and infection control significance. Little is known about the smear-positive populations in high TB burden regions, such as Kazakhstan. The objective of this study is to characterize the smear-positive population in Kazakhstan and identify associated modifiable risk factors.Data on incident TB cases' (identified between April 2012 and March 2014 socio-demographic, risk behavior, and comorbidity characteristics were collected in four regions of Kazakhstan through structured survey and medical record review. We used multivariable logistic regression to determine factors associated with smear positivity.Of the total sample, 193 (34.3% of the 562 study participants tested smear-positive. In the final adjusted multivariable logistic regression model, sex (adjusted odds ratio (aOR = 2.0, 95% CI:1.3-3.1, p < 0.01, incarceration (aOR = 3.6, 95% CI:1.2-11.1, p = 0.03, alcohol dependence (aOR = 2.6, 95% CI:1.2-5.7, p = 0.02, diabetes (aOR = 5.0, 95% CI:2.4-10.7, p < 0.01, and physician access (aOR = 2.7, 95% CI:1.3-5.5p < 0.01 were associated with smear-positivity.Incarceration, alcohol dependence, diabetes, and physician access are associated with smear positivity among incident TB cases in Kazakhstan. To stem the TB epidemic, screening, treatment and prevention policies should address these factors.

Exploiting intrinsic fluctuations to identify model parameters.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven; Pahle, Jürgen

2015-04-01

Parameterisation of kinetic models plays a central role in computational systems biology. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Model parameters can be structurally non-identifiable because of functional relationships. Noise in measured data is usually considered to be a nuisance for parameter estimation. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non-identifiable. The authors present a method to identify model parameters that are structurally non-identifiable in a deterministic framework. The method takes time course recordings of biochemical systems in steady state or transient state as input. Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. Although the system's behaviour cannot be distinguished on this manifold in a deterministic framework it might be distinguishable in a stochastic modelling framework. Their method exploits this by using an objective function that includes a measure for fluctuations in particle numbers. They show on three example models, immigration-death, gene expression and Epo-EpoReceptor interaction, that this resolves the non-identifiability even in the case of measurement noise with known amplitude. The method is applied to partially observed recordings of biochemical systems with measurement noise. It is simple to implement and it is usually very fast to compute. This optimisation can be realised in a classical or Bayesian fashion.

Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes

Science.gov (United States)

Jia, Pengfei; Maloney, Tim

2015-01-01

Predictive modelling is used to identify students at risk of failing their first-year courses and not returning to university in the second year. Our aim is twofold. Firstly, we want to understand the factors that lead to poor first-year experiences at university. Secondly, we want to develop simple, low-cost tools that would allow universities to…

Coherence method of identifying signal noise model

International Nuclear Information System (INIS)

Vavrin, J.

1981-01-01

The noise analysis method is discussed in identifying perturbance models and their parameters by a stochastic analysis of the noise model of variables measured on a reactor. The analysis of correlations is made in the frequency region using coherence analysis methods. In identifying an actual specific perturbance, its model should be determined and recognized in a compound model of the perturbance system using the results of observation. The determination of the optimum estimate of the perturbance system model is based on estimates of related spectral densities which are determined from the spectral density matrix of the measured variables. Partial and multiple coherence, partial transfers, the power spectral densities of the input and output variables of the noise model are determined from the related spectral densities. The possibilities of applying the coherence identification methods were tested on a simple case of a simulated stochastic system. Good agreement was found of the initial analytic frequency filters and the transfers identified. (B.S.)

Identifying factors affecting destination choice of medical tourists: a ...

African Journals Online (AJOL)

medical tourism”, has emerged as a new source of competitive advantage all over the world. The present study seeks to identify the factors that affect destination choice of medical tourists. Methods: We systematically searched relevant databases ...

Using dynamic walking models to identify factors that contribute to increased risk of falling in older adults.

Science.gov (United States)

Roos, Paulien E; Dingwell, Jonathan B

2013-10-01

Falls are common in older adults. The most common cause of falls is tripping while walking. Simulation studies demonstrated that older adults may be restricted by lower limb strength and movement speed to regain balance after a trip. This review examines how modeling approaches can be used to determine how different measures predict actual fall risk and what some of the causal mechanisms of fall risk are. Although increased gait variability predicts increased fall risk experimentally, it is not clear which variability measures could best be used, or what magnitude of change corresponded with increased fall risk. With a simulation study we showed that the increase in fall risk with a certain increase in gait variability was greatly influenced by the initial level of variability. Gait variability can therefore not easily be used to predict fall risk. We therefore explored other measures that may be related to fall risk and investigated the relationship between stability measures such as Floquet multipliers and local divergence exponents and actual fall risk in a dynamic walking model. We demonstrated that short-term local divergence exponents were a good early predictor for fall risk. Neuronal noise increases with age. It has however not been fully understood if increased neuronal noise would cause an increased fall risk. With our dynamic walking model we showed that increased neuronal noise caused increased fall risk. Although people who are at increased risk of falling reduce their walking speed it had been questioned whether this slower speed would actually cause a reduced fall risk. With our model we demonstrated that a reduced walking speed caused a reduction in fall risk. This may be due to the decreased kinematic variability as a result of the reduced signal-dependent noise of the smaller muscle forces that are required for slower. These insights may be used in the development of fall prevention programs in order to better identify those at increased risk of

Using Dynamic Walking Models to Identify Factors that Contribute to Increased Risk of Falling in Older Adults

Science.gov (United States)

Roos, Paulien E.; Dingwell, Jonathan B.

2013-01-01

Falls are common in older adults. The most common cause of falls is tripping while walking. Simulation studies demonstrated that older adults may be restricted by lower limb strength and movement speed to regain balance after a trip. This review examines how modeling approaches can be used to determine how different measures predict actual fall risk and what some of the causal mechanisms of fall risk are. Although increased gait variability predicts increased fall risk experimentally, it is not clear which variability measures could best be used, or what magnitude of change corresponded with increased fall risk. With a simulation study we showed that the increase in fall risk with a certain increase in gait variability was greatly influenced by the initial level of variability. Gait variability can therefore not easily be used to predict fall risk. We therefore explored other measures that may be related to fall risk and investigated the relationship between stability measures such as Floquet multipliers and local divergence exponents and actual fall risk in a dynamic walking model. We demonstrated that short-term local divergence exponents were a good early predictor for fall risk. Neuronal noise increases with age. It has however not been fully understood if increased neuronal noise would cause an increased fall risk. With our dynamic walking model we showed that increased neuronal noise caused increased fall risk. Although people who are at increased risk of falling reduce their walking speed it had been questioned whether this slower speed would actually cause a reduced fall risk. With our model we demonstrated that a reduced walking speed caused a reduction in fall risk. This may be due to the decreased kinematic variability as a result of the reduced signal-dependent noise of the smaller muscle forces that are required for slower. These insights may be used in the development of fall prevention programs in order to better identify those at increased risk of

Modelling impulsive factors for electronics and restaurant coupons’ e-store display

Science.gov (United States)

Ariningsih, P. K.; Nainggolan, M.; Sandy, I. A.

2018-04-01

In many times, the increment of e-store visitors does not followed by sales increment. Most purchases through e-commerce are impulsive buying, however only small amount of study is available to understand impulsive factors of e-store display. This paper suggests a preliminary concept on understanding the impulsive factors in Electronics and Restaurant Coupons e-store display, which are two among few popular group products sold through e-commerce. By conducting literature study and survey, 31 attributes were identified as impulsive factors in electronics e-store display and 20 attributes were identified as impulsive factors for restaurant coupon e-store. The attributes were then grouped into comprehensive impulsive factors by factor analysis. Each group of impulsive attributes were generated into 3 factors. Accessibility Factors and Trust Factors appeared for each group products. The other factors are Internal Factors for electronics e-store and Marketing factors for restaurant coupons e-store. Structural Equation Model of the impulsive factors was developed for each type of e-store, which stated the covariance between Trust Factors and Accessibility Factors. Based on preliminary model, Internal Factor and Trust Factor are influencing impulsive buying in electronics store. Special factor for electronics e-store is Internal Factor, while for restaurant coupons e-store is Marketing Factor.

Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments

International Nuclear Information System (INIS)

Su, Laisuo; Zhang, Jianbo; Wang, Caijuan; Zhang, Yakun; Li, Zhe; Song, Yang; Jin, Ting; Ma, Zhao

2016-01-01

Highlights: • The effect of seven principal factors on the aging behavior of lithium ion cells is studied. • Orthogonal design of experiments is used to reduce the experiment units. • Capacity fades linearly during the initial 10% capacity fading period. • Statistical methods are used to compare the significance of each principal factor. • A multi-factor statistical model is developed to predict the aging rate of cells. - Abstract: The aging rate under cycling conditions for lithium-ion cells is affected by many factors. Seven principal factors are systematically examined using orthogonal design of experiments, and statistical analysis was used to identify the order of principal factors in terms of strength in causing capacity fade. These seven principal factors are: the charge and discharge currents (i_1, i_2) during the constant current regime, the charge and discharge cut-off voltages (V_1, V_2) and the corresponding durations (t_1, t_2) during the constant voltage regime, and the ambient temperature (T). An orthogonal array with 18 test units was selected for the experiments. The test results show that (1) during the initial 10% capacity fading period, the capacity faded linearly with Wh-throughput for all the test conditions; (2) after the initial period, certain cycling conditions exacerbated aging rates, while the others remain the same. The statistical results show that: (1) except for t_1, the other six principal factors significantly affect the aging rate; (2) the strength of the principal factors was ranked as: i_1 > V_1 > T > t_2 > V_2 > i_2 > t_1. Finally, a multi-factor statistical aging model is developed to predict the aging rate, and the accuracy of the model is validated.

Structural identifiability analysis of a cardiovascular system model.

Science.gov (United States)

Pironet, Antoine; Dauby, Pierre C; Chase, J Geoffrey; Docherty, Paul D; Revie, James A; Desaive, Thomas

2016-05-01

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

[Lake eutrophication modeling in considering climatic factors change: a review].

Science.gov (United States)

Su, Jie-Qiong; Wang, Xuan; Yang, Zhi-Feng

2012-11-01

Climatic factors are considered as the key factors affecting the trophic status and its process in most lakes. Under the background of global climate change, to incorporate the variations of climatic factors into lake eutrophication models could provide solid technical support for the analysis of the trophic evolution trend of lake and the decision-making of lake environment management. This paper analyzed the effects of climatic factors such as air temperature, precipitation, sunlight, and atmosphere on lake eutrophication, and summarized the research results about the lake eutrophication modeling in considering in considering climatic factors change, including the modeling based on statistical analysis, ecological dynamic analysis, system analysis, and intelligent algorithm. The prospective approaches to improve the accuracy of lake eutrophication modeling with the consideration of climatic factors change were put forward, including 1) to strengthen the analysis of the mechanisms related to the effects of climatic factors change on lake trophic status, 2) to identify the appropriate simulation models to generate several scenarios under proper temporal and spatial scales and resolutions, and 3) to integrate the climatic factors change simulation, hydrodynamic model, ecological simulation, and intelligent algorithm into a general modeling system to achieve an accurate prediction of lake eutrophication under climatic change.

Factors Models of Scrum Adoption in the Software Development Process: A Systematic Literature Review

Directory of Open Access Journals (Sweden)

Marilyn Sihuay

2018-05-01

Full Text Available (Background The adoption of Agile Software Development (ASD, in particular Scrum, has grown significantly since its introduction in 2001. However, in Lima, many ASDs implementations have been not suitable (uncompleted or inconsistent, thus losing benefits obtainable by this approach and the critical success factors in this context are unknown. (Objective To analyze factors models used in the evaluation of the adoption of ASDs, as these factors models can contribute to explaining the success or failure of these adoptions. (Method In this study we used a systematic literature review. (Result Ten models have been identified; their similarities and differences are presented. (Conclusion Each model identified consider different factors, however some of them are shared by five of these models, such as team member attributes, engaging customer, customer collaboration, experience and work environment.

Genome-wide screen of Pseudomonas aeruginosa In Saccharomyces cerevisiae identifies new virulence factors

Directory of Open Access Journals (Sweden)

Rafat eZrieq

2015-11-01

Full Text Available Pseudomonas aeruginosa is a human opportunistic pathogen that causes mortality in cystic fibrosis and immunocompromised patients. While many virulence factors of this pathogen have already been identified, several remain to be discovered. In this respect we set an unprecedented genome-wide screen of a P. aeruginosa expression library based on a yeast growth phenotype. 51 candidates were selected in a three-round screening process. The robustness of the screen was validated by the selection of three well known secreted proteins including one demonstrated virulence factor, the protease LepA. Further in silico sorting of the 51 candidates highlighted three potential new Pseudomonas effector candidates (Pec. By testing the cytotoxicity of wild type P. aeruginosa vs pec mutants towards macrophages and the virulence in the Caenorhabditis elegans model, we demonstrated that the three selected Pecs are novel virulence factors of P. aeruginosa. Additional cellular localization experiments in the host revealed specific localization for Pec1 and Pec2 that could inform about their respective functions.

Evaluating predictive models for solar energy growth in the US states and identifying the key drivers

Science.gov (United States)

Chakraborty, Joheen; Banerji, Sugata

2018-03-01

Driven by a desire to control climate change and reduce the dependence on fossil fuels, governments around the world are increasing the adoption of renewable energy sources. However, among the US states, we observe a wide disparity in renewable penetration. In this study, we have identified and cleaned over a dozen datasets representing solar energy penetration in each US state, and the potentially relevant socioeconomic and other factors that may be driving the growth in solar. We have applied a number of predictive modeling approaches - including machine learning and regression - on these datasets over a 17-year period and evaluated the relative performance of the models. Our goals were: (1) identify the most important factors that are driving the growth in solar, (2) choose the most effective predictive modeling technique for solar growth, and (3) develop a model for predicting next year’s solar growth using this year’s data. We obtained very promising results with random forests (about 90% efficacy) and varying degrees of success with support vector machines and regression techniques (linear, polynomial, ridge). We also identified states with solar growth slower than expected and representing a potential for stronger growth in future.

Identifying the factors underlying discontinuation of triptans.

Science.gov (United States)

Wells, Rebecca E; Markowitz, Shira Y; Baron, Eric P; Hentz, Joseph G; Kalidas, Kavita; Mathew, Paul G; Halker, Rashmi; Dodick, David W; Schwedt, Todd J

2014-02-01

To identify factors associated with triptan discontinuation among migraine patients. It is unclear why many migraine patients who are prescribed triptans discontinue this treatment. This study investigated correlates of triptan discontinuation with a focus on potentially modifiable factors to improve compliance. This multicenter cross-sectional survey (n = 276) was performed at US tertiary care headache clinics. Headache fellows who were members of the American Headache Society Headache Fellows Research Consortium recruited episodic and chronic migraine patients who were current triptan users (use within prior 3 months and for ≥1 year) or past triptan users (no use within 6 months; prior use within 2 years). Univariate analyses were first completed to compare current triptan users to past users for: migraine characteristics, other migraine treatments, triptan education, triptan efficacy, triptan side effects, type of prescribing provider, Migraine Disability Assessment (MIDAS) scores and Beck Depression Inventory (BDI) scores. Then, a multivariable logistic regression model was selected from all possible combinations of predictor variables to determine the factors that best correlated with triptan discontinuation. Compared with those still using triptans (n = 207), those who had discontinued use (n = 69) had higher rates of medication overuse (30 vs. 18%, P = .04) and were more likely to have ever used opioids for migraine treatment (57 vs. 38%, P = .006) as well as higher MIDAS (mean 63 vs. 37, P = .001) and BDI scores (mean 10.4 vs. 7.4, P = .009). Compared with discontinued users, current triptan users were more likely to have had their triptan prescribed by a specialist (neurologist, headache specialist, or pain specialist) (74 vs. 54%, P = .002) and were more likely to report headache resolution (53 vs. 14%, P  24 (2.6, [1.5, 4.6]), BDI >4 (2.5, [1.4, 4.5]), and a history of ever using opioids for migraine therapy (2.2, [1

Talent identification model for sprinter using discriminant factor

Science.gov (United States)

Kusnanik, N. W.; Hariyanto, A.; Herdyanto, Y.; Satia, A.

2018-01-01

The main purpose of this study was to identify young talented sprinter using discriminant factor. The research was conducted in 3 steps including item pool, screening of item pool, and trial of instruments at the small and big size of samples. 315 male elementary school students participated in this study with mean age of 11-13 years old. Data were collected by measuring anthropometry (standing height, sitting height, body mass, and leg length); testing physical fitness (40m sprint for speed, shuttle run for agility, standing broad jump for power, multistage fitness test for endurance). Data were analyzed using discriminant factor. The result of this study found that there were 5 items that selected as an instrument to identify young talented sprinter: sitting height, body mass, leg length, sprint 40m, and multistage fitness test. Model of Discriminant for talent identification in sprinter was D = -24,497 + (0,155 sitting height) + (0,080 body mass) + (0,148 leg length) + (-1,225 Sprint 40m) + (0,563 MFT). The conclusion of this study: instrument tests that have been selected and discriminant model that have been found can be applied to identify young talented as a sprinter.

Identifying at-risk profiles and protective factors for problem gambling: A longitudinal study across adolescence and early adulthood.

Science.gov (United States)

Allami, Youssef; Vitaro, Frank; Brendgen, Mara; Carbonneau, René; Tremblay, Richard E

2018-05-01

Past studies have identified various risk and protective factors for problem gambling (PG). However, no study has examined the interplay between these factors using a combination of person-centered and variable-centered approaches embedded within a longitudinal design. The present study aimed to (a) identify distinct profiles in early adolescence based on a set of risk factors commonly associated with PG (impulsivity, depression, anxiety, drug-alcohol use, aggressiveness, and antisociality), (b) explore the difference in reported gambling problems between these profiles during midadolescence and early adulthood, and (c) identify family- and peer-related variables that could operate as protective or compensatory factors in this context. Two samples were used: (a) a population sample (N = 1,033) living in low socioeconomic-status neighborhoods and (b) a population sample (N = 3,017) representative of students attending Quebec schools. Latent profile analyses were conducted to identify at-risk profiles based on individual risk factors measured at age 12 years. Negative binomial regression models were estimated to compare profiles in terms of their reported gambling problems at ages 16 and 23. Finally, family- and peer-related variables measured at age 14 were included to test their protective or compensatory role with respect to the link between at-risk profiles and gambling problems. Four profiles were identified: well-adjusted, internalizing, externalizing, and comorbid. Compared to the well-adjusted profile, the externalizing and comorbid profiles reported more gambling problems at ages 16 and 23, but the internalizing profile did not differ significantly. Various protective and compensatory factors emerged for each profile at both time points. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Identify the Important Decision Factors of Online Shopping Adoption in Indonesia

Directory of Open Access Journals (Sweden)

Lailatul HIJRAH

2017-12-01

Full Text Available The objective of this study is to identify factors encouraging a consumer to engage in online shopping activities. The expected contribution of this study is for online entrepreneurs, in order to develop the most suitable business strategy, so that it will be clearly identified and sorted out which factors are the most important and the main motivation of Indonesian consumers to shop via online by using responses from respondents who usually shop online and offline in 3 cities in Indonesia, Jakarta, Surabaya and Samarinda. The research instruments were developed by conducting FGDs on relevant groups, either academics, online shopping activists, suppliers and courier businessmen in Jakarta, Surabaya and Samarinda Cities in effort to extract any information that encourages consumers to online shopping. After conducting FGD, the researcher produced 48 items proposed for factor analysis and after extracted to form eleven constructs, some items were removed because they had less loading factors. The eleven constructs or dimensions are trust, risk, consumer factors, website factors, price, service quality, convenience, subjective norm, product guarantee, variety of products and lifestyle. The implications of this study provide valuable insights about consumer decisions to online shopping or not online shopping.

Identifying Nonprovider Factors Affecting Pediatric Emergency Medicine Provider Efficiency.

Science.gov (United States)

Saleh, Fareed; Breslin, Kristen; Mullan, Paul C; Tillett, Zachary; Chamberlain, James M

2017-10-31

The aim of this study was to create a multivariable model of standardized relative value units per hour by adjusting for nonprovider factors that influence efficiency. We obtained productivity data based on billing records measured in emergency relative value units for (1) both evaluation and management of visits and (2) procedures for 16 pediatric emergency medicine providers with more than 750 hours worked per year. Eligible shifts were in an urban, academic pediatric emergency department (ED) with 2 sites: a tertiary care main campus and a satellite community site. We used multivariable linear regression to adjust for the impact of shift and pediatric ED characteristics on individual-provider efficiency and then removed variables from the model with minimal effect on productivity. There were 2998 eligible shifts for the 16 providers during a 3-year period. The resulting model included 4 variables when looking at both ED sites combined. These variables include the following: (1) number of procedures billed by provider, (2) season of the year, (3) shift start time, and (4) day of week. Results were improved when we separately modeled each ED location. A 3-variable model using procedures billed by provider, shift start time, and season explained 23% of the variation in provider efficiency at the academic ED site. A 3-variable model using procedures billed by provider, patient arrivals per hour, and shift start time explained 45% of the variation in provider efficiency at the satellite ED site. Several nonprovider factors affect provider efficiency. These factors should be considered when designing productivity-based incentives.

Identifying the bleeding trauma patient: predictive factors for massive transfusion in an Australasian trauma population.

Science.gov (United States)

Hsu, Jeremy Ming; Hitos, Kerry; Fletcher, John P

2013-09-01

Military and civilian data would suggest that hemostatic resuscitation results in improved outcomes for exsanguinating patients. However, identification of those patients who are at risk of significant hemorrhage is not clearly defined. We attempted to identify factors that would predict the need for massive transfusion (MT) in an Australasian trauma population, by comparing those trauma patients who did receive massive transfusion with those who did not. Between 1985 and 2010, 1,686 trauma patients receiving at least 1 U of packed red blood cells were identified from our prospectively maintained trauma registry. Demographic, physiologic, laboratory, injury, and outcome variables were reviewed. Univariate analysis determined significant factors between those who received MT and those who did not. A predictive multivariate logistic regression model with backward conditional stepwise elimination was used for MT risk. Statistical analysis was performed using SPSS PASW. MT patients had a higher pulse rate, lower Glasgow Coma Scale (GCS) score, lower systolic blood pressure, lower hemoglobin level, higher Injury Severity Score (ISS), higher international normalized ratio (INR), and longer stay. Initial logistic regression identified base deficit (BD), INR, and hemoperitoneum at laparotomy as independent predictive variables. After assigning cutoff points of BD being greater than 5 and an INR of 1.5 or greater, a further model was created. A BD greater than 5 and either INR of 1.5 or greater or hemoperitoneum was associated with 51 times increase in MT risk (odds ratio, 51.6; 95% confidence interval, 24.9-95.8). The area under the receiver operating characteristic curve for the model was 0.859. From this study, a combination of BD, INR, and hemoperitoneum has demonstrated good predictability for MT. This tool may assist in the determination of those patients who might benefit from hemostatic resuscitation. Prognostic study, level III.

Use of clinical risk factors to identify postmenopausal women with vertebral fractures.

Science.gov (United States)

Tobias, J H; Hutchinson, A P; Hunt, L P; McCloskey, E V; Stone, M D; Martin, J C; Thompson, P W; Palferman, T G; Bhalla, A K

2007-01-01

Previous studies have been unable to identify risk factors for prevalent vertebral fractures (VF), which are suitable for use in selection strategies intended to target high-risk sub-groups for diagnostic assessment. However, these studies generally consisted of large epidemiology surveys based on questionnaires and were only able to evaluate a limited number of risk factors. Here, we investigated whether a stronger relationship exists with prevalent VF when conventional risk factors are combined with additional information obtained from detailed one-to-one assessment. Women aged 65-75 registered at four geographically distinct GP practices were invited to participate (n=1,518), of whom 540 attended for assessment as follows: a questionnaire asking about risk factors for osteoporosis such as height loss compared to age 25 and history of non-vertebral fracture (NVF), the get-up-and-go test, Margolis back pain score, measurement of wall-tragus and rib-pelvis distances, and BMD as measured by the distal forearm BMD. A lateral thoraco-lumbar spine X-ray was obtained, which was subsequently scored for the presence of significant vertebral deformities. Of the 509 subjects who underwent spinal radiographs, 37 (7.3%) were found to have one or more VF. Following logistic regression analysis, the four most predictive clinical risk factors for prevalent VF were: height loss (P=0.006), past NVF (P=0.004), history of back pain (P=0.075) and age (P=0.05). BMD was also significantly associated with prevalent VF (P=0.002), but its inclusion did not affect associations with other variables. Factors elicited from detailed one-to-one assessment were not related to the risk of one or more prevalent VFs. The area under ROC curves derived from these regressions, which suggested that models for prevalent VF had modest predictive accuracy, were as follows: 0.68 (BMD), 0.74 (four clinical risk factors above) and 0.78 (clinical risk factors + BMD). Analyses were repeated in relation to the

Identifying the Best-Fitting Factor Structure of the Experience of Close Relations

DEFF Research Database (Denmark)

Esbjørn, Barbara Hoff; Breinholst, Sonja; Niclasen, Janni

2015-01-01

. The present study used a Danish sample with the purpose of addressing limitations in previous studies, such as the lack of diversity in cultural back- ground, restricted sample characteristics, and poorly fitting structure models. Participants consisted of 253 parents of children between the ages of 7 and 12...... years, 53% being moth- ers. The parents completed the paper version of the questionnaire. Confirmatory Factor Analyses were carried out to determine whether theoretically and empirically established models including one and two factors would also provide adequate fits in a Danish sample. A previous...... study using the original ECR suggested that Scandinavian samples may best be described using a five-factor solution. Our results indicated that the one- and two-factor models of the ECR-R did not fit the data well. Exploratory Factor Analysis revealed a five- factor model. Our study provides evidence...

Identifying risk factors that contribute to acute mountain sickness ...

African Journals Online (AJOL)

This study is a questionnaire-based study conducted in London and at Everest Base Camp, in which 116 lowlanders were invited to participate and fill in a questionnaire to identify potential risk factors in their history that may have contributed to development of or protection against AMS. Results. A total of 89 lowlanders ...

Identifying and assessing the factors affecting skill gap in digital marketing in communication industry companies

Directory of Open Access Journals (Sweden)

Fereshteh Ghotbifar

2017-03-01

Full Text Available As far as new communication channels are concerned, there have been extensive developments in communications and marketing in digital era. Today, therefore, companies try to take advantage of digital marketing channels to provide suitable services to customers to improve their satisfaction level. However, this study aimed to identify and assess factors affecting skill gap in digital marketing. This was descriptive correlation study. The population consisted of experts in communications industry to identify most important skill gaps in digital marketing and factors affecting them; also, managers and specialists of these companies were investigated to determine the role of identified factors in reducing skills gaps. Using localized questionnaire and interviewing with ten experts who were selected by Delphi snowball method, the skill gaps in marketing and factors affecting them were identified. Also, a researcher made questionnaire with 32 questions was distributed among 226 employees to investigate the identified factors role in reducing skills gap in digital marketing. The results showed that from four identified factors, the components including operational strategic factors and environmental factors had direct and positive impact on creating skill gap in digital marketing of studied companies. The environmental factors such as social and cultural conditions, religion, technology, and economy had more proactive impact on skills gap in digital marketing. Also, the results showed that among skills gaps in digital marketing of studied companies, the skills (Principles of Communication and (Predicting Future had the highest and lowest gaps, respectively.

Risk factors for atherosclerosis - can they be used to identify the ...

African Journals Online (AJOL)

Risk factors are often used in preventive care programmes to identify the patient at particular risk for developing atherosclerosis. Risk factors for atherosclerosis have also been shown to be linked to the presence of the disease at a given time, a fact that may be helpful when screening for additional atherosclerotic disease in ...

Identifiability of parameters and behaviour of the MCMC chains: a case study using the reaction norm model

DEFF Research Database (Denmark)

Shariati, M M; Korsgaard, I R; Sorensen, D

2009-01-01

model with unknown covariates (RNUC) is a model in which unknown environmental effects can be inferred jointly with the remaining parameters. The problem of identifiability of parameters at the level of the likelihood and the associated behaviour of MCMC chains were discussed using the RNUC...... as fixed and there are other fixed factors in the model, the contrasts involving environmental effects, the variance of environmental sensitivities (genetic slopes) and the residual variance are the only identifiable parameters. These different identifiability scenarios were generated by changing...... as an example. It was shown theoretically that when environmental effects (covariates) are considered as random effects, estimable functions of the fixed effects, (co)variance components and genetic effects are identifiable as well as the environmental effects. When the environmental effects are treated...

Global identifiability of linear compartmental models--a computer algebra algorithm.

Science.gov (United States)

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Model-based identification and use of task complexity factors of human integrated systems

International Nuclear Information System (INIS)

Ham, Dong-Han; Park, Jinkyun; Jung, Wondea

2012-01-01

Task complexity is one of the conceptual constructs that are critical to explain and predict human performance in human integrated systems. A basic approach to evaluating the complexity of tasks is to identify task complexity factors and measure them. Although a great deal of task complexity factors have been studied, there is still a lack of conceptual frameworks for identifying and organizing them analytically, which can be generally used irrespective of the types of domains and tasks. This study proposes a model-based approach to identifying and using task complexity factors, which has two facets—the design aspects of a task and complexity dimensions. Three levels of design abstraction, which are functional, behavioral, and structural aspects of a task, characterize the design aspect of a task. The behavioral aspect is further classified into five cognitive processing activity types. The complexity dimensions explain a task complexity from different perspectives, which are size, variety, and order/organization. Twenty-one task complexity factors are identified by the combination of the attributes of each facet. Identification and evaluation of task complexity factors based on this model is believed to give insights for improving the design quality of tasks. This model for complexity factors can also be used as a referential framework for allocating tasks and designing information aids. The proposed approach is applied to procedure-based tasks of nuclear power plants (NPPs) as a case study to demonstrate its use. Last, we compare the proposed approach with other studies and then suggest some future research directions.

Identifying work ability promoting factors for home care aides and assistant nurses

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Larsson Agneta

2012-01-01

Full Text Available Abstract Background In workplace health promotion, all potential resources needs to be taken into consideration, not only factors relating to the absence of injury and the physical health of the workers, but also psychological aspects. A dynamic balance between the resources of the individual employees and the demands of work is an important prerequisite. In the home care services, there is a noticeable trend towards increased psychosocial strain on employees at work. There are a high frequency of work-related musculoskeletal disorders and injuries, and a low prevalence of sustainable work ability. The aim of this research was to identify factors promoting work ability and self-efficacy in care aides and assistant nurses within home care services. Methods This study is based on cross-sectional data collected in a municipality in northern Sweden. Care aides (n = 58 and assistant nurses (n = 79 replied to a self-administered questionnaire (response rate 46%. Hierarchical multiple regression analyses were performed to assess the influence of several independent variables on self-efficacy (model 1 and work ability (model 2 for care aides and assistant nurses separately. Results Perceptions of personal safety, self-efficacy and musculoskeletal wellbeing contributed to work ability for assistant nurses (R2adj of 0.36, p 2adj of 0.29, p = 0.001. Self-efficacy was associated with the safety climate and the physical demands of the job in both professions (R2adj of 0.24, p = 0.003 for care aides, and also by sex and age for the assistant nurses (R2adj of 0.31, p Conclusions The intermediate factors contributed differently to work ability in the two professions. Self-efficacy, personal safety and musculoskeletal wellbeing were important for the assistant nurses, while the work ability of the care aides was associated with the safety climate, but also with the non-changeable factors age and seniority. All these factors are important to acknowledge in

Modeling secondary accidents identified by traffic shock waves.

Science.gov (United States)

Junhua, Wang; Boya, Liu; Lanfang, Zhang; Ragland, David R

2016-02-01

The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

International Nuclear Information System (INIS)

Thomassen, Mads; Tan, Qihua; Kruse, Torben A

2008-01-01

Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets. We have analyzed 8 publicly available gene expression data sets. A global approach, 'gene set enrichment analysis' as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets. The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis. By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

Science.gov (United States)

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Identifying a key physical factor sensitive to the performance of Madden-Julian oscillation simulation in climate models

Science.gov (United States)

Kim, Go-Un; Seo, Kyong-Hwan

2018-01-01

A key physical factor in regulating the performance of Madden-Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization's Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.

Identifying factors influencing contraceptive use in Bangladesh: evidence from BDHS 2014 data.

Science.gov (United States)

Hossain, M B; Khan, M H R; Ababneh, F; Shaw, J E H

2018-01-30

Birth control is the conscious control of the birth rate by methods which temporarily prevent conception by interfering with the normal process of ovulation, fertilization, and implantation. High contraceptive prevalence rate is always expected for controlling births for those countries that are experiencing high population growth rate. The factors that influence contraceptive prevalence are also important to know for policy implication purposes in Bangladesh. This study aims to explore the socio-economic, demographic and others key factors that influence the use of contraception in Bangladesh. The contraception data are extracted from the 2014 Bangladesh Demographic and Health Survey (BDHS) data which were collected by using a two stage stratified random sampling technique that is a source of nested variability. The nested sources of variability must be incorporated in the model using random effects in order to model the actual parameter effects on contraceptive prevalence. A mixed effect logistic regression model has been implemented for the binary contraceptive data, where parameters are estimated through generalized estimating equation by assuming exchangeable correlation structure to explore and identify the factors that truly affect the use of contraception in Bangladesh. The prevalence of contraception use by currently married 15-49 years aged women or their husbands is 62.4%. Our study finds that administrative division, place of residence, religion, number of household members, woman's age, occupation, body mass index, breastfeeding practice, husband's education, wish for children, living status with wife, sexual activity in past year, women amenorrheic status, abstaining status, number of children born in last five years and total children ever died were significantly associated with contraception use in Bangladesh. The odds of women experiencing the outcome of interest are not independent due to the nested structure of the data. As a result, a mixed

Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

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Lahiru Iddamalgoda

2016-08-01

Full Text Available Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification and scoring based prioritization methods for determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI methods in conjunction with the K nearest neighbors’ could be used in accurately categorizing the genetic factors in disease causation

Sensitized mutagenesis screen in Factor V Leiden mice identifies thrombosis suppressor loci.

Science.gov (United States)

Westrick, Randal J; Tomberg, Kärt; Siebert, Amy E; Zhu, Guojing; Winn, Mary E; Dobies, Sarah L; Manning, Sara L; Brake, Marisa A; Cleuren, Audrey C; Hobbs, Linzi M; Mishack, Lena M; Johnston, Alexander J; Kotnik, Emilee; Siemieniak, David R; Xu, Jishu; Li, Jun Z; Saunders, Thomas L; Ginsburg, David

2017-09-05

Factor V Leiden ( F5 L ) is a common genetic risk factor for venous thromboembolism in humans. We conducted a sensitized N -ethyl- N -nitrosourea (ENU) mutagenesis screen for dominant thrombosuppressor genes based on perinatal lethal thrombosis in mice homozygous for F5 L ( F5 L/L ) and haploinsufficient for tissue factor pathway inhibitor ( Tfpi +/- ). F8 deficiency enhanced the survival of F5 L/L Tfpi +/- mice, demonstrating that F5 L/L Tfpi +/- lethality is genetically suppressible. ENU-mutagenized F5 L/L males and F5 L/+ Tfpi +/- females were crossed to generate 6,729 progeny, with 98 F5 L/L Tfpi +/- offspring surviving until weaning. Sixteen lines, referred to as "modifier of Factor 5 Leiden ( MF5L1-16 )," exhibited transmission of a putative thrombosuppressor to subsequent generations. Linkage analysis in MF5L6 identified a chromosome 3 locus containing the tissue factor gene ( F3 ). Although no ENU-induced F3 mutation was identified, haploinsufficiency for F3 ( F3 +/- ) suppressed F5 L/L Tfpi +/- lethality. Whole-exome sequencing in MF5L12 identified an Actr2 gene point mutation (p.R258G) as the sole candidate. Inheritance of this variant is associated with suppression of F5 L/L Tfpi +/- lethality ( P = 1.7 × 10 -6 ), suggesting that Actr2 p.R258G is thrombosuppressive. CRISPR/Cas9 experiments to generate an independent Actr2 knockin/knockout demonstrated that Actr2 haploinsufficiency is lethal, supporting a hypomorphic or gain-of-function mechanism of action for Actr2 p.R258G Our findings identify F8 and the Tfpi/F3 axis as key regulators in determining thrombosis balance in the setting of F5 L and also suggest a role for Actr2 in this process.

Unidimensional factor models imply weaker partial correlations than zero-order correlations.

Science.gov (United States)

van Bork, Riet; Grasman, Raoul P P P; Waldorp, Lourens J

2018-06-01

In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

Identifying the customer satisfaction factors in furniture market

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Majid Azizi

2017-05-01

Full Text Available Purpose – the purpose of this research is to identify the influential factors on customer satisfaction in the Iranian furniture market in order to get acquainted with the fundamental items for planning future sales programs with the purposes of extolling competitive advantages. Design/methodology/approach – A commixture of 6 items and 31 factors were educed from interviewing with 20 experts in furniture designing and manufacturing industry. The collected data from customer need indexes in previous research were also used. Findings – results showed that such factors as economic factors weighting 0.32, product specifications weighting 0.21 and credibility weighting 0.19 were the most important indexes and price weighting 0.195, fame weighting 0.131, quality, durability and resistance weighting 0.116, paying conditions weighting 0.095, designing and decorating in virtual softwares before ordering weighting 0.074, updatedness weighting 0.064 and interaction approach with the weight of 0.42 were the most considerable influential sub-indexes on the satisfaction of the Iranian furniture market customers. Research limitations/implications – by the enhancement of competition throughout the world markets and the inevitable presence of Iran in it, the market activists’ concentration should shift towards paying comprehensive attention to desires and needs of furniture market customers. Practical implications – some important issues on planning suitable manufacturing and marketing programs in furniture market are introduce so that the activists be aware of considering the growing knowledge and awareness of end-users which increases the pressure on the manufacturer side. There are also some solutions in terms of internal and external organizational factors with regard to the complex nature of competitive environment in furniture market. Originality/value – the paper provides an examination of effective factors on customer satisfaction with a

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

Science.gov (United States)

Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

Identifying Predictive Factors for Incident Reports in Patients Receiving Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Elnahal, Shereef M., E-mail: selnaha1@jhmi.edu [Department of Radiation Oncology and Molecular Radiation Sciences, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland (United States); Blackford, Amanda [Department of Oncology Biostatistics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland (United States); Smith, Koren; Souranis, Annette N.; Briner, Valerie; McNutt, Todd R.; DeWeese, Theodore L.; Wright, Jean L.; Terezakis, Stephanie A. [Department of Radiation Oncology and Molecular Radiation Sciences, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland (United States)

2016-04-01

Purpose: To describe radiation therapy cases during which voluntary incident reporting occurred; and identify patient- or treatment-specific factors that place patients at higher risk for incidents. Methods and Materials: We used our institution's incident learning system to build a database of patients with incident reports filed between January 2011 and December 2013. Patient- and treatment-specific data were reviewed for all patients with reported incidents, which were classified by step in the process and root cause. A control group of patients without events was generated for comparison. Summary statistics, likelihood ratios, and mixed-effect logistic regression models were used for group comparisons. Results: The incident and control groups comprised 794 and 499 patients, respectively. Common root causes included documentation errors (26.5%), communication (22.5%), technical treatment planning (37.5%), and technical treatment delivery (13.5%). Incidents were more frequently reported in minors (age <18 years) than in adult patients (37.7% vs 0.4%, P<.001). Patients with head and neck (16% vs 8%, P<.001) and breast (20% vs 15%, P=.03) primaries more frequently had incidents, whereas brain (18% vs 24%, P=.008) primaries were less frequent. Larger tumors (17% vs 10% had T4 lesions, P=.02), and cases on protocol (9% vs 5%, P=.005) or with intensity modulated radiation therapy/image guided intensity modulated radiation therapy (52% vs 43%, P=.001) were more likely to have incidents. Conclusions: We found several treatment- and patient-specific variables associated with incidents. These factors should be considered by treatment teams at the time of peer review to identify patients at higher risk. Larger datasets are required to recommend changes in care process standards, to minimize safety risks.

Identifying Predictive Factors for Incident Reports in Patients Receiving Radiation Therapy

International Nuclear Information System (INIS)

Elnahal, Shereef M.; Blackford, Amanda; Smith, Koren; Souranis, Annette N.; Briner, Valerie; McNutt, Todd R.; DeWeese, Theodore L.; Wright, Jean L.; Terezakis, Stephanie A.

2016-01-01

Purpose: To describe radiation therapy cases during which voluntary incident reporting occurred; and identify patient- or treatment-specific factors that place patients at higher risk for incidents. Methods and Materials: We used our institution's incident learning system to build a database of patients with incident reports filed between January 2011 and December 2013. Patient- and treatment-specific data were reviewed for all patients with reported incidents, which were classified by step in the process and root cause. A control group of patients without events was generated for comparison. Summary statistics, likelihood ratios, and mixed-effect logistic regression models were used for group comparisons. Results: The incident and control groups comprised 794 and 499 patients, respectively. Common root causes included documentation errors (26.5%), communication (22.5%), technical treatment planning (37.5%), and technical treatment delivery (13.5%). Incidents were more frequently reported in minors (age <18 years) than in adult patients (37.7% vs 0.4%, P<.001). Patients with head and neck (16% vs 8%, P<.001) and breast (20% vs 15%, P=.03) primaries more frequently had incidents, whereas brain (18% vs 24%, P=.008) primaries were less frequent. Larger tumors (17% vs 10% had T4 lesions, P=.02), and cases on protocol (9% vs 5%, P=.005) or with intensity modulated radiation therapy/image guided intensity modulated radiation therapy (52% vs 43%, P=.001) were more likely to have incidents. Conclusions: We found several treatment- and patient-specific variables associated with incidents. These factors should be considered by treatment teams at the time of peer review to identify patients at higher risk. Larger datasets are required to recommend changes in care process standards, to minimize safety risks.

Identifying important motivational factors for professionals in Greek hospitals

Science.gov (United States)

Kontodimopoulos, Nick; Paleologou, Victoria; Niakas, Dimitris

2009-01-01

Background The purpose of this study was to identify important motivational factors according to the views of health-care professionals in Greek hospitals and particularly to determine if these might differ in the public and private sectors. Methods A previously developed -and validated- instrument addressing four work-related motivators (job attributes, remuneration, co-workers and achievements) was used. Three categories of health care professionals, doctors (N = 354), nurses (N = 581) and office workers (N = 418), working in public and private hospitals, participated and motivation was compared across socio-demographic and occupational variables. Results The range of reported motivational factors was mixed and Maslow's conclusions that lower level motivational factors must be met before ascending to the next level were not confirmed. The highest ranked motivator for the entire sample, and by professional subgroup, was achievements (P motivators were similar, and only one significant difference was observed, namely between doctors and nurses in respect to co-workers (P motivated by all factors significantly more than their public-hospital counterparts. Conclusion The results are in agreement with the literature which focuses attention to management approaches employing both monetary and non-monetary incentives to motivate health care workers. This study showed that intrinsic factors are particularly important and should become a target for effective employee motivation. PMID:19754968

Depression in Intimate Partner Violence Victims in Slovenia: A Crippling Pattern of Factors Identified in Family Practice Attendees

Directory of Open Access Journals (Sweden)

Nena Kopčavar Guček

2018-01-01

Full Text Available This multi-centre cross-sectional study explored associations between prevalence of depression and exposure to intimate partner violence (IPV at any time in patients’ adult life in 471 participants of a previous IPV study. In 2016, 174 interviews were performed, using the Short Form Domestic Violence Exposure Questionnaire, the Zung Scale and questions about behavioural patterns of exposure to IPV. Family doctors reviewed patients’ medical charts for period from 2012 to 2016, using the Domestic Violence Exposure Medical Chart Check List, for conditions which persisted for at least three years. Depression was found to be associated with any exposure to IPV in adult life and was more likely to affect women. In multivariable logistic regression modelling, factors associated with self-rated depression were identified (p < 0.05. Exposure to emotional and physical violence was identified as a risk factor in the first model, explaining 23% of the variance. The second model explained 66% of the variance; past divorce, dysfunctional family relationships and a history of incapacity to work increased the likelihood of depression in patients. Family doctors should consider IPV exposure when detecting depression, since lifetime IPV exposure was found to be 40.4% and 36.9% of depressed revealed it.

Depression in Intimate Partner Violence Victims in Slovenia: A Crippling Pattern of Factors Identified in Family Practice Attendees.

Science.gov (United States)

Guček, Nena Kopčavar; Selič, Polona

2018-01-26

This multi-centre cross-sectional study explored associations between prevalence of depression and exposure to intimate partner violence (IPV) at any time in patients' adult life in 471 participants of a previous IPV study. In 2016, 174 interviews were performed, using the Short Form Domestic Violence Exposure Questionnaire, the Zung Scale and questions about behavioural patterns of exposure to IPV. Family doctors reviewed patients' medical charts for period from 2012 to 2016, using the Domestic Violence Exposure Medical Chart Check List, for conditions which persisted for at least three years. Depression was found to be associated with any exposure to IPV in adult life and was more likely to affect women. In multivariable logistic regression modelling, factors associated with self-rated depression were identified (p < 0.05). Exposure to emotional and physical violence was identified as a risk factor in the first model, explaining 23% of the variance. The second model explained 66% of the variance; past divorce, dysfunctional family relationships and a history of incapacity to work increased the likelihood of depression in patients. Family doctors should consider IPV exposure when detecting depression, since lifetime IPV exposure was found to be 40.4% and 36.9% of depressed revealed it.

The Promise of Virtual Teams: Identifying Key Factors in Effectiveness and Failure

Science.gov (United States)

Horwitz, Frank M.; Bravington, Desmond; Silvis, Ulrik

2006-01-01

Purpose: The aim of the investigation is to identify enabling and disenabling factors in the development and operation of virtual teams; to evaluate the importance of factors such as team development, cross-cultural variables, leadership, communication and social cohesion as contributors to virtual team effectiveness. Design/methodology/approach:…

Factor Analysis of Drawings: Application to College Student Models of the Greenhouse Effect

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Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

2015-01-01

Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance,…

BENCHMARKING - PRACTICAL TOOLS IDENTIFY KEY SUCCESS FACTORS

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Olga Ju. Malinina

2016-01-01

Full Text Available The article gives a practical example of the application of benchmarking techniques. The object of study selected fashion store Company «HLB & M Hennes & Mauritz», located in the shopping center «Gallery», Krasnodar. Hennes & Mauritz. The purpose of this article is to identify the best ways to develop a fashionable brand clothing store Hennes & Mauritz on the basis of benchmarking techniques. On the basis of conducted market research is a comparative analysis of the data from different perspectives. The result of the author’s study is a generalization of the ndings, the development of the key success factors that will allow to plan a successful trading activities in the future, based on the best experience of competitors.

Identifying key hospital service quality factors in online health communities.

Science.gov (United States)

Jung, Yuchul; Hur, Cinyoung; Jung, Dain; Kim, Minki

2015-04-07

The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. We defined social media-based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media-based key quality factors for hospitals. To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and

What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing.

Science.gov (United States)

Ko, Minsam; Yeo, Jaeryong; Lee, Juyeong; Lee, Uichin; Jang, Young Jae

2016-01-01

Sports fans are able to watch games from many locations using TV services while interacting with other fans online. In this paper, we identify the factors that affect sports viewers' online interactions. Using a large-scale dataset of more than 25 million chat messages from a popular social TV site for baseball, we extract various game-related factors, and investigate the relationships between these factors and fans' interactions using a series of multiple regression analyses. As a result, we identify several factors that are significantly related to viewer interactions. In addition, we determine that the influence of these factors varies according to the user group; i.e., active vs. less active users, and loyal vs. non-loyal users.

Identifying and ranking the factors affecting entrepreneurial marketing to facilitate exports

Directory of Open Access Journals (Sweden)

Mehdi Habibzadeh

2016-04-01

Full Text Available Small and medium enterprises (SMEs are believed the most important components of today’s businesses and they can boost the growth of economy. This paper presents an empirical investigation to identify and rank important factors influencing on entrepreneurial marketing to facilitate exports of SMEs. The study designs a questionnaire in Likert scale and distributes it among 387 randomly selected entrepreneurs who act as managers of some SMEs in city of Tehran, Iran. Cronbach alpha is calculated as 0.873, which is well above the acceptable level. Using principle component analysis, the study has determined four factors including competitive intelligence, competitive advantage, external factors and internal factors to facilitate the export of SMEs.

Identifying Human Factors Issues in Aircraft Maintenance Operations

Science.gov (United States)

Veinott, Elizabeth S.; Kanki, Barbara G.; Shafto, Michael G. (Technical Monitor)

1995-01-01

Maintenance operations incidents submitted to the Aviation Safety Reporting System (ASRS) between 1986-1992 were systematically analyzed in order to identify issues relevant to human factors and crew coordination. This exploratory analysis involved 95 ASRS reports which represented a wide range of maintenance incidents. The reports were coded and analyzed according to the type of error (e.g, wrong part, procedural error, non-procedural error), contributing factors (e.g., individual, within-team, cross-team, procedure, tools), result of the error (e.g., aircraft damage or not) as well as the operational impact (e.g., aircraft flown to destination, air return, delay at gate). The main findings indicate that procedural errors were most common (48.4%) and that individual and team actions contributed to the errors in more than 50% of the cases. As for operational results, most errors were either corrected after landing at the destination (51.6%) or required the flight crew to stop enroute (29.5%). Interactions among these variables are also discussed. This analysis is a first step toward developing a taxonomy of crew coordination problems in maintenance. By understanding what variables are important and how they are interrelated, we may develop intervention strategies that are better tailored to the human factor issues involved.

Applying total interpretive structural modeling to study factors affecting construction labour productivity

Directory of Open Access Journals (Sweden)

Sayali Shrikrishna Sandbhor

2014-03-01

Full Text Available Construction sector has always been dependent on manpower. Most of the activities carried out on any construction site are labour intensive. Since productivity of any project depends directly on productivity of labour, it is a prime responsibility of the employer to enhance labour productivity. Measures to improve the same depend on analysis of positive and negative factors affecting productivity. Major attention should be given to factors that decrease the productivity of labour. Factor analysis thus is an integral part of any study aiming to improve productivity.  Interpretive structural modeling is a methodology for identifying and summarizing relationships among factors which define an issue or problem. It provides a means to arrange the factors in an order as per their complexity. This study attempts to use the latest version of interpretive structural modeling i.e. total interpretive structural modeling to analyze factors negatively affecting construction labour productivity. It establishes interpretive relationship among these factors facilitating improvement in the overall productivity of construction site.

Collapsing Factors in Multitrait-Multimethod Models: Examining Consequences of a Mismatch Between Measurement Design and Model

Directory of Open Access Journals (Sweden)

Christian eGeiser

2015-08-01

Full Text Available Models of confirmatory factor analysis (CFA are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific factor shows non-significant loading or variance estimates. Eid et al. (2008 distinguished between MTMM measurement designs with interchangeable (randomly selected versus structurally different (fixed methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor and latent state-trait models.

What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing.

Directory of Open Access Journals (Sweden)

Minsam Ko

Full Text Available Sports fans are able to watch games from many locations using TV services while interacting with other fans online. In this paper, we identify the factors that affect sports viewers' online interactions. Using a large-scale dataset of more than 25 million chat messages from a popular social TV site for baseball, we extract various game-related factors, and investigate the relationships between these factors and fans' interactions using a series of multiple regression analyses. As a result, we identify several factors that are significantly related to viewer interactions. In addition, we determine that the influence of these factors varies according to the user group; i.e., active vs. less active users, and loyal vs. non-loyal users.

What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing

Science.gov (United States)

Yeo, Jaeryong; Lee, Juyeong

2016-01-01

Sports fans are able to watch games from many locations using TV services while interacting with other fans online. In this paper, we identify the factors that affect sports viewers’ online interactions. Using a large-scale dataset of more than 25 million chat messages from a popular social TV site for baseball, we extract various game-related factors, and investigate the relationships between these factors and fans’ interactions using a series of multiple regression analyses. As a result, we identify several factors that are significantly related to viewer interactions. In addition, we determine that the influence of these factors varies according to the user group; i.e., active vs. less active users, and loyal vs. non-loyal users. PMID:26849568

Using a Systematic Approach to Identifying Organizational Factors in Root Cause Analysis

International Nuclear Information System (INIS)

Gallogly, Kay Wilde

2011-01-01

This presentation set the scene for the second discussion session. In her presentation, the author observed that: - Investigators do not see the connection between the analysis tools available and the identification of HOF. Most investigators use the tools in a cursory manner and so do not derive the full benefits of the tools. Some tools are used for presentation purposes as opposed to being used for analytical purposes e.g. event and causal factors charts. In some cases, the report will indicate that specific analytical tools were used in the investigation but the analysis is not in the body of the report. - Some investigators are documenting HOF causes but do not recognize them as such. This indicates a lack of understanding of HOF. - Others investigators focus on technical issues because of their own comfort level. - The culture of the Organisation will affect the depth of the investigation and therefore the use of the analytical tools to pursue HOF issues. - The author contends that if analysis tools are applied systematically to gather factually based data, then HOF issues can be identified. The use of factual information (without judgement and subjectivity) is important to maintain the credibility of the investigation especially when HOF issues are identified. - Systematic use of tools assists in better communication of the issues to foster greater understanding and acceptance by senior management. - Barrier Analysis, Change Analysis, and TWIN (Task Demands, Work Environment, Individual Capabilities, and Human Nature) all offer the opportunity to identify HOF issues if the analyst pursues this line of investigation. It was illustrated that many elements of the TWIN Error Precursors are themselves Organisational in nature. - The TWIN model applied to the Anatomy of an Event will help to distinguish those which are Organisational issues (Latent Organisational Weaknesses, Error Precursors and Flawed Defences) and those which are human factors (Active Errors

Extracting the Evaluations of Stereotypes: Bi-factor Model of the Stereotype Content Structure

Directory of Open Access Journals (Sweden)

Pablo Sayans-Jiménez

2017-10-01

Full Text Available Stereotype dimensions—competence, morality and sociability—are fundamental to studying the perception of other groups. These dimensions have shown moderate/high positive correlations with each other that do not reflect the theoretical expectations. The explanation for this (e.g., halo effect undervalues the utility of the shared variance identified. In contrast, in this work we propose that this common variance could represent the global evaluation of the perceived group. Bi-factor models are proposed to improve the internal structure and to take advantage of the information representing the shared variance among dimensions. Bi-factor models were compared with first order models and other alternative models in three large samples (300–309 participants. The relationships among the global and specific bi-factor dimensions with a global evaluation dimension (measured through a semantic differential were estimated. The results support the use of bi-factor models rather than first order models (and other alternative models. Bi-factor models also show a greater utility to directly and more easily explore the stereotype content including its evaluative content.

Extracting the Evaluations of Stereotypes: Bi-factor Model of the Stereotype Content Structure.

Science.gov (United States)

Sayans-Jiménez, Pablo; Cuadrado, Isabel; Rojas, Antonio J; Barrada, Juan R

2017-01-01

Stereotype dimensions-competence, morality and sociability-are fundamental to studying the perception of other groups. These dimensions have shown moderate/high positive correlations with each other that do not reflect the theoretical expectations. The explanation for this (e.g., halo effect) undervalues the utility of the shared variance identified. In contrast, in this work we propose that this common variance could represent the global evaluation of the perceived group. Bi-factor models are proposed to improve the internal structure and to take advantage of the information representing the shared variance among dimensions. Bi-factor models were compared with first order models and other alternative models in three large samples (300-309 participants). The relationships among the global and specific bi-factor dimensions with a global evaluation dimension (measured through a semantic differential) were estimated. The results support the use of bi-factor models rather than first order models (and other alternative models). Bi-factor models also show a greater utility to directly and more easily explore the stereotype content including its evaluative content.

Skewed factor models using selection mechanisms

KAUST Repository

Kim, Hyoung-Moon

2015-12-21

Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

Skewed factor models using selection mechanisms

KAUST Repository

Kim, Hyoung-Moon; Maadooliat, Mehdi; Arellano-Valle, Reinaldo B.; Genton, Marc G.

2015-01-01

Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

Science.gov (United States)

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Three novel approaches to structural identifiability analysis in mixed-effects models.

Science.gov (United States)

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2016-05-06

Structural identifiability is a concept that considers whether the structure of a model together with a set of input-output relations uniquely determines the model parameters. In the mathematical modelling of biological systems, structural identifiability is an important concept since biological interpretations are typically made from the parameter estimates. For a system defined by ordinary differential equations, several methods have been developed to analyse whether the model is structurally identifiable or otherwise. Another well-used modelling framework, which is particularly useful when the experimental data are sparsely sampled and the population variance is of interest, is mixed-effects modelling. However, established identifiability analysis techniques for ordinary differential equations are not directly applicable to such models. In this paper, we present and apply three different methods that can be used to study structural identifiability in mixed-effects models. The first method, called the repeated measurement approach, is based on applying a set of previously established statistical theorems. The second method, called the augmented system approach, is based on augmenting the mixed-effects model to an extended state-space form. The third method, called the Laplace transform mixed-effects extension, is based on considering the moment invariants of the systems transfer function as functions of random variables. To illustrate, compare and contrast the application of the three methods, they are applied to a set of mixed-effects models. Three structural identifiability analysis methods applicable to mixed-effects models have been presented in this paper. As method development of structural identifiability techniques for mixed-effects models has been given very little attention, despite mixed-effects models being widely used, the methods presented in this paper provides a way of handling structural identifiability in mixed-effects models previously not

Model endophenotype for bipolar disorder: Qualitative Analysis, etiological factors, and research areas

Directory of Open Access Journals (Sweden)

Naraiana de Oliveira Tavares

2014-12-01

Full Text Available The aim of this study is to present an updated view of the writings on the endophenotype model for bipolar disorder using analytical methodologies. A review and analysis of networks was performed through descriptors and keywords that characterize the composition of the endophenotype model as a model of health. Information was collected from between 1992 and 2014, and the main thematic areas covered in the articles were identified. We discuss the results and question their cohesion, emphasizing the need to strengthen and identify the points of connection between etiological factors and characteristics that make up the model of endophenotypes for bipolar disorder.

Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

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S. Pereira

2012-04-01

Full Text Available The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km² located in the Northern Portugal.

In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use and was compared with a model developed using all seven landslide predisposing factors.

Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model

Directory of Open Access Journals (Sweden)

Hong Xue

2018-01-01

Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to

Modeling Ability Differentiation in the Second-Order Factor Model

Science.gov (United States)

Molenaar, Dylan; Dolan, Conor V.; van der Maas, Han L. J.

2011-01-01

In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid-Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals,…

IDENTIFYING MOTIVATION FACTOR INVOLVEMENT OF SARAWAK MALAY WOMEN ENTREPRENEUR

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Masyantie Mohamad

2016-03-01

Full Text Available Sarawak multilayered cake among Sarawak product signature famous among the local as well as international tourist visiting Sarawak. In fact, Sarawak Malay women entrepreneurs have become very necessary players in the entrepreneurial field specifically in this cottage industries from the early introduction of this business, they have facing various problem in this businesses. Thus, this research aims to build an understanding of motivational factor that encourage Sarawak Malay women entrepreneurial experiences especially in multilayered cake businesses. Using qualitative methods, this research aims to identify the entrepreneurial motivations factors; with regards to start-up motivation by Sarawak Malay women. The finding shows that the motivations that influence Malay women within Kuching, Sarawak areas to start and grow their business are involve self-driven and context driven that motivate them involve in multilayered cakes businesses.

Global analysis of WRKY transcription factor superfamily in Setaria identifies potential candidates involved in abiotic stress signaling

OpenAIRE

Muthamilarasan, Mehanathan; Bonthala, Venkata S.; Khandelwal, Rohit; Jaishankar, Jananee; Shweta, Shweta; Nawaz, Kashif; Prasad, Manoj

2015-01-01

Transcription factors (TFs) are major players in stress signalling and constitute an integral part of signalling networks. Among the major TFs, WRKY proteins play pivotal roles in regulation of transcriptional reprogramming associated with stress responses. In view of this, genome- and transcriptome-wide identification of WRKY TF family was performed in the C4 model plants, Setaria italica (SiWRKY) and S. viridis (SvWRKY), respectively. The study identified 105 SiWRKY and 44 SvWRKY proteins t...

Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

Directory of Open Access Journals (Sweden)

Anke Hüls

2017-05-01

Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

KAUST Repository

Ting, Chee-Ming

2017-12-06

We consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.

Change management in Iranian hospitals: social factors model

Directory of Open Access Journals (Sweden)

B. Delgoshaei

2012-02-01

Full Text Available Background: Continuous change in the complex health care environments is a major challenge for administrative managers. This study aimed to design a change model to facilitate change implementation in the Iranian hospitals. Methods: This is a descriptive and comparative study. The data were collected through library search and in-depth interview with 15 hospital managers. Nine well-established change theories developed by Lewin, Action Research, Kotter, Ackerman- Anderson and Anderson, Prosci , Kilman, Beer, Continuum, and Gelicher were compared. Common denominators of the theories were identified and tabulated. Experienced hospital managers’ suggestions about social factors were acquired. The initial model was designed and validated using the Delphi Technique. Results: The majority of the selected change models emphasize the significance of social factors in change implementation such as effective communication, organizational climate and culture, and leadership. The results from the interviews indicate that low readiness to change, lack of confidence (or trust for change, and autocratic leadership style ,and poor communication could hinder the change process. Conclusion: Based on the model developed in the study, effective communication, readiness of employees, and a contingency leadership/management combined could lead to successful implementation of change in the hospital.

Robust global identifiability theory using potentials--Application to compartmental models.

Science.gov (United States)

Wongvanich, N; Hann, C E; Sirisena, H R

2015-04-01

This paper presents a global practical identifiability theory for analyzing and identifying linear and nonlinear compartmental models. The compartmental system is prolonged onto the potential jet space to formulate a set of input-output equations that are integrals in terms of the measured data, which allows for robust identification of parameters without requiring any simulation of the model differential equations. Two classes of linear and non-linear compartmental models are considered. The theory is first applied to analyze the linear nitrous oxide (N2O) uptake model. The fitting accuracy of the identified models from differential jet space and potential jet space identifiability theories is compared with a realistic noise level of 3% which is derived from sensor noise data in the literature. The potential jet space approach gave a match that was well within the coefficient of variation. The differential jet space formulation was unstable and not suitable for parameter identification. The proposed theory is then applied to a nonlinear immunological model for mastitis in cows. In addition, the model formulation is extended to include an iterative method which allows initial conditions to be accurately identified. With up to 10% noise, the potential jet space theory predicts the normalized population concentration infected with pathogens, to within 9% of the true curve. Copyright © 2015 Elsevier Inc. All rights reserved.

Challenges of implementing collaborative models of decision making with trans-identified patients.

Science.gov (United States)

Dewey, Jodie M

2015-10-01

Factors health providers face during the doctor-patient encounter both impede and assist the development of collaborative models of treatment. I investigated decision making among medical and therapeutic professionals who work with trans-identified patients to understand factors that might impede or facilitate the adoption of the collaborative decision-making model in their clinical work. Following a grounded theory approach, I collected and analysed data from semi-structured interviews with 10 U.S. physicians and 10 U.S. mental health professionals. Doctors and therapists often desire collaboration with their patients but experience dilemmas in treating the trans-identified patients. Dilemmas include lack of formal education, little to no institutional support and inconsistent understanding and application of the main documents used by professionals treating trans-patients. Providers face considerable risk in providing unconventional treatments due to the lack of institutional and academic support relating to the treatment for trans-people, and the varied interpretation and application of the diagnostic and treatment documents used in treating trans-people. To address this risk, the relationship with the patient becomes crucial. However, trust, a component required for collaboration, is thwarted when the patients feel obliged to present in ways aligned with these documents in order to receive desired treatments. When trust cannot be established, medical and mental health providers can and do delay or deny treatments, resulting in the imbalance of power between patient and provider. The documents created to assist in treatment actually thwart professional desire to work collaboratively with patients. © 2013 John Wiley & Sons Ltd.

A Note on the Identifiability of Generalized Linear Mixed Models

DEFF Research Database (Denmark)

Labouriau, Rodrigo

2014-01-01

I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization...

High-throughput, signature-tagged mutagenic approach to identify novel virulence factors of Yersinia pestis CO92 in a mouse model of infection.

Science.gov (United States)

Ponnusamy, Duraisamy; Fitts, Eric C; Sha, Jian; Erova, Tatiana E; Kozlova, Elena V; Kirtley, Michelle L; Tiner, Bethany L; Andersson, Jourdan A; Chopra, Ashok K

2015-05-01

The identification of new virulence factors in Yersinia pestis and understanding their molecular mechanisms during an infection process are necessary in designing a better vaccine or to formulate an appropriate therapeutic intervention. By using a high-throughput, signature-tagged mutagenic approach, we created 5,088 mutants of Y. pestis strain CO92 and screened them in a mouse model of pneumonic plague at a dose equivalent to 5 50% lethal doses (LD50) of wild-type (WT) CO92. From this screen, we obtained 118 clones showing impairment in disseminating to the spleen, based on hybridization of input versus output DNA from mutant pools with 53 unique signature tags. In the subsequent screen, 20/118 mutants exhibited attenuation at 8 LD50 when tested in a mouse model of bubonic plague, with infection by 10/20 of the aforementioned mutants resulting in 40% or higher survival rates at an infectious dose of 40 LD50. Upon sequencing, six of the attenuated mutants were found to carry interruptions in genes encoding hypothetical proteins or proteins with putative functions. Mutants with in-frame deletion mutations of two of the genes identified from the screen, namely, rbsA, which codes for a putative sugar transport system ATP-binding protein, and vasK, a component of the type VI secretion system, were also found to exhibit some attenuation at 11 or 12 LD50 in a mouse model of pneumonic plague. Likewise, among the remaining 18 signature-tagged mutants, 9 were also attenuated (40 to 100%) at 12 LD50 in a pneumonic plague mouse model. Previously, we found that deleting genes encoding Braun lipoprotein (Lpp) and acyltransferase (MsbB), the latter of which modifies lipopolysaccharide function, reduced the virulence of Y. pestis CO92 in mouse models of bubonic and pneumonic plague. Deletion of rbsA and vasK genes from either the Δlpp single or the Δlpp ΔmsbB double mutant augmented the attenuation to provide 90 to 100% survivability to mice in a pneumonic plague model at 20

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

Science.gov (United States)

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Structural equation modeling analysis of factors influencing architects' trust in project design teams

Institute of Scientific and Technical Information of China (English)

DING Zhi-kun; NG Fung-fai; WANG Jia-yuan

2009-01-01

This paper describes a structural equation modeling (SEM) analysis of factors influencing architects' trust in project design teams. We undertook a survey of architects, during which we distributed 193 questionnaires in 29 A-level architectural We used Amos 6.0 for SEM to identify significant personal construct based factors affecting interpersonal trust. The results show that only social interaction between architects significantly affects their interpersonal trust. The explained variance of trust is not very high in the model. Therefore, future research should add more factors into the current model. The practical implication is that team managers should promote the social interactions between team members such that the interpersonal trust level between team members can be improved.

The Relationship between Race and Students' Identified Career Role Models and Perceived Role Model Influence

Science.gov (United States)

Karunanayake, Danesh; Nauta, Margaret M.

2004-01-01

The authors examined whether college students' race was related to the modal race of their identified career role models, the number of identified career role models, and their perceived influence from such models. Consistent with A. Bandura's (1977, 1986) social learning theory, students tended to have role models whose race was the same as…

Identifying the necessary and sufficient number of risk factors for predicting academic failure.

Science.gov (United States)

Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

2012-03-01

Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities across Categories

Science.gov (United States)

Duvvuri, Sri Devi; Gruca, Thomas S.

2010-01-01

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors

Directory of Open Access Journals (Sweden)

Theophilus O. Ogunyemi

2012-01-01

Full Text Available Longitudinal data for studying urinary incontinence (UI risk factors are rare. Data from one study, the hallmark Medical, Epidemiological, and Social Aspects of Aging (MESA, have been analyzed in the past; however, repeated measures analyses that are crucial for analyzing longitudinal data have not been applied. We tested a novel application of statistical methods to identify UI risk factors in older women. MESA data were collected at baseline and yearly from a sample of 1955 men and women in the community. Only women responding to the 762 baseline and 559 follow-up questions at one year in each respective survey were examined. To test their utility in mining large data sets, and as a preliminary step to creating a predictive index for developing UI, logistic regression, generalized estimating equations (GEEs, and proportional hazard regression (PHREG methods were used on the existing MESA data. The GEE and PHREG combination identified 15 significant risk factors associated with developing UI out of which six of them, namely, urinary frequency, urgency, any urine loss, urine loss after emptying, subject’s anticipation, and doctor’s proactivity, are found most highly significant by both methods. These six factors are potential candidates for constructing a future UI predictive index.

A method to identify dependencies between organizational factors using statistical independence test

International Nuclear Information System (INIS)

Kim, Y.; Chung, C.H.; Kim, C.; Jae, M.; Jung, J.H.

2004-01-01

A considerable number of studies on organizational factors in nuclear power plants have been made especially in recent years, most of which have assumed organizational factors to be independent. However, since organizational factors characterize the organization in terms of safety and efficiency etc. and there would be some factors that have close relations between them. Therefore, from whatever point of view, if we want to identify the characteristics of an organization, the dependence relationships should be considered to get an accurate result. In this study the organization of a reference nuclear power plant in Korea was analyzed for the trip cases of that plant using 20 organizational factors that Jacobs and Haber had suggested: 1) coordination of work, 2) formalization, 3) organizational knowledge, 4) roles and responsibilities, 5) external communication, 6) inter-department communications, 7) intra-departmental communications, 8) organizational culture, 9) ownership, 10) safety culture, 11) time urgency, 12) centralization, 13) goal prioritization, 14) organizational learning, 15) problem identification, 16) resource allocation, 17) performance evaluation, 18) personnel selection, 19) technical knowledge, and 20) training. By utilizing the results of the analysis, a method to identify the dependence relationships between organizational factors is presented. The statistical independence test for the analysis result of the trip cases is adopted to reveal dependencies. This method is geared to the needs to utilize many kinds of data that has been obtained as the operating years of nuclear power plants increase, and more reliable dependence relations may be obtained by using these abundant data

Extending existing structural identifiability analysis methods to mixed-effects models.

Science.gov (United States)

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2018-01-01

The concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice. Copyright © 2017 Elsevier Inc. All rights reserved.

An exploratory study to identify critical factors of innovation culture in organizations

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Hamed Asgari

2013-07-01

Full Text Available During the past two decades, there has been a growing trend on knowledge-based organizations. Innovation, on the other hand, plays essential role on building competitive business units. In this paper, we present an exploratory study to identify critical factors of innovation culture in organizations. We detect important factors influencing innovation culture in construction industry based on the implementation of factor analysis. The proposed study designs a questionnaire and distributes it among 400 experts who are involved in construction industry. Cronbach alpha has been calculated as 0.779, which validates the overall questionnaire. The results of factor analysis have indicated that six factors of building cultural infrastructures, education, organizational vision, established culture, strategic culture and flexible culture are the most important items influencing innovation culture.

Spatial scale effects in environmental risk-factor modelling for diseases

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Ram K. Raghavan

2013-05-01

Full Text Available Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD and Kansas Gap Analysis Program (KS GAP around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

Using sensitivity analysis to identify key factors for the propagation of a plant epidemic.

Science.gov (United States)

Rimbaud, Loup; Bruchou, Claude; Dallot, Sylvie; Pleydell, David R J; Jacquot, Emmanuel; Soubeyrand, Samuel; Thébaud, Gaël

2018-01-01

Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus , in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.

Testing job typologies and identifying at-risk subpopulations using factor mixture models

NARCIS (Netherlands)

Keller, A. C.; Igic, Ivana; Meier, Laurenz L.; Semmer, N. K.; Schaubroeck, J.; Brunner, Beatrice; Elfering, Achim

2017-01-01

Research in occupational health psychology has tended to focus on the effects of single job characteristics or various job characteristics combined into 1 factor. However, such a variable-centered approach does not account for the clustering of job attributes among groups of employees. We addressed

Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer

DEFF Research Database (Denmark)

Pearce, Celeste Leigh; Rossing, Mary Anne; Lee, Alice W

2013-01-01

There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied....

Factors associated with adoption of health information technology: a conceptual model based on a systematic review.

Science.gov (United States)

Kruse, Clemens Scott; DeShazo, Jonathan; Kim, Forest; Fulton, Lawrence

2014-05-23

The Health Information Technology for Economic and Clinical Health Act (HITECH) allocated $19.2 billion to incentivize adoption of the electronic health record (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the Center for Medicare and Medicaid Services (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR. The purpose of this systematic review is to identify a full-spectrum of both internal organizational and external environmental factors associated with the adoption of health information technology (HIT), specifically the EHR. The result is a conceptual model that is commensurate with the complexity of with the health care sector. We performed a systematic literature search in PubMed (restricted to English), EBSCO Host, and Google Scholar for both empirical studies and theory-based writing from 1993-2013 that demonstrated association between influential factors and three modes of HIT: EHR, electronic medical record (EMR), and computerized provider order entry (CPOE). We also looked at published books on organizational theories. We made notes and noted trends on adoption factors. These factors were grouped as adoption factors associated with various versions of EHR adoption. The resulting conceptual model summarizes the diversity of independent variables (IVs) and dependent variables (DVs) used in articles, editorials, books, as

Identify and Classify Critical Success Factor of Agile Software Development Methodology Using Mind Map

OpenAIRE

Tasneem Abd El Hameed; Mahmoud Abd EL Latif; Sherif Kholief

2016-01-01

Selecting the right method, right personnel and right practices, and applying them adequately, determine the success of software development. In this paper, a qualitative study is carried out among the critical factors of success from previous studies. The factors of success match with their relative principles to illustrate the most valuable factor for agile approach success, this paper also prove that the twelve principles poorly identified for few factors resulting from qualitative and qua...

A measurement model of perinatal stressors: identifying risk for postnatal emotional distress in mothers of high-risk infants.

Science.gov (United States)

DeMier, R L; Hynan, M T; Hatfield, R F; Varner, M W; Harris, H B; Manniello, R L

2000-01-01

A measurement model of perinatal stressors was first evaluated for reliability and then used to identify risk factors for postnatal emotional distress in high-risk mothers. In Study 1, six measures (gestational age of the baby, birthweight, length of the baby's hospitalization, a postnatal complications rating for the infant, and Apgar scores at 1 and 5 min) were obtained from chart reviews of preterm births at two different hospitals. Confirmatory factor analyses revealed that the six measures could be accounted for by three factors: (a) Infant Maturity, (b) Apgar Ratings, and (c) Complications. In Study 2, a modified measurement model indicated that Infant Maturity and Complications were significant predictors of postnatal emotional distress in an additional sample of mothers. This measurement model may also be useful in predicting (a) other measures of psychological distress in parents, and (b) measures of cognitive and motor development in infants.

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Science.gov (United States)

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

Rheumatoid arthritis: identifying and characterising polymorphisms using rat models

Science.gov (United States)

2016-01-01

ABSTRACT Rheumatoid arthritis is a chronic inflammatory joint disorder characterised by erosive inflammation of the articular cartilage and by destruction of the synovial joints. It is regulated by both genetic and environmental factors, and, currently, there is no preventative treatment or cure for this disease. Genome-wide association studies have identified ∼100 new loci associated with rheumatoid arthritis, in addition to the already known locus within the major histocompatibility complex II region. However, together, these loci account for only a modest fraction of the genetic variance associated with this disease and very little is known about the pathogenic roles of most of the risk loci identified. Here, we discuss how rat models of rheumatoid arthritis are being used to detect quantitative trait loci that regulate different arthritic traits by genetic linkage analysis and to positionally clone the underlying causative genes using congenic strains. By isolating specific loci on a fixed genetic background, congenic strains overcome the challenges of genetic heterogeneity and environmental interactions associated with human studies. Most importantly, congenic strains allow functional experimental studies be performed to investigate the pathological consequences of natural genetic polymorphisms, as illustrated by the discovery of several major disease genes that contribute to arthritis in rats. We discuss how these advances have provided new biological insights into arthritis in humans. PMID:27736747

The Factors Influencing Satisfaction with Public City Transport: A Structural Equation Modelling Approach

Directory of Open Access Journals (Sweden)

Pawlasova Pavlina

2015-12-01

Full Text Available Satisfaction is one of the key factors which influences customer loyalty. We assume that the satisfied customer will be willing to use the ssame service provider again. The overall passengers´ satisfaction with public city transport may be affected by the overall service quality. Frequency, punctuality, cleanliness in the vehicle, proximity, speed, fare, accessibility and safety of transport, information and other factors can influence passengers´ satisfaction. The aim of this paper is to quantify factors and identify the most important factors influencing customer satisfaction with public city transport within conditions of the Czech Republic. Two methods of analysis are applied in order to fulfil the aim. The method of factor analysis and the method Varimax were used in order to categorize variables according to their mutual relations. The method of structural equation modelling was used to evaluate the factors and validate the model. Then, the optimal model was found. The logistic parameters, including service continuity and frequency, and service, including information rate, station proximity and vehicle cleanliness, are the factors influencing passengers´ satisfaction on a large scale.

Evaluation factors for verification and validation of low-level waste disposal site models

International Nuclear Information System (INIS)

Moran, M.S.; Mezga, L.J.

1982-01-01

The purpose of this paper is to identify general evaluation factors to be used to verify and validate LLW disposal site performance models in order to assess their site-specific applicability and to determine their accuracy and sensitivity. It is intended that the information contained in this paper be employed by model users involved with LLW site performance model verification and validation. It should not be construed as providing protocols, but rather as providing a framework for the preparation of specific protocols or procedures. A brief description of each evaluation factor is provided. The factors have been categorized according to recommended use during either the model verification or the model validation process. The general responsibilities of the developer and user are provided. In many cases it is difficult to separate the responsibilities of the developer and user, but the user is ultimately accountable for both verification and validation processes. 4 refs

Impact of identifying factors which trigger bothersome tinnitus on the treatment outcome in tinnitus retraining therapy.

Science.gov (United States)

Molini, Egisto; Faralli, Mario; Calzolaro, Lucia; Ricci, Giampietro

2014-01-01

The aim of this work was to ascertain any differences in the effectiveness of rehabilitation therapy in relation to the presence or absence of a known negative reinforcement responsible for the tinnitus-related pathology. Between 1 January 2001 and 31 December 2008, we recruited 294 subjects suffering from incapacitating tinnitus and/or hyperacusis. The patients underwent tinnitus retraining therapy (TRT) according to the methods described by Jastreboff and Hazell [Tinnitus Retraining Therapy: Implementing the Neurophysiological Model. Cambridge, Cambridge University Press, 2004, pp 121-133]. We clinically assessed the presence or absence of known phenomena of associative learning, regarding the presence of adverse events temporally correlated with tinnitus and the treatment outcome. The separate analysis of the 2 subgroups shows a statistically significant difference in the improvement rate between the group with a known triggering factor and the group without a triggering factor, with a preponderance of the former with a 91% improvement rate versus approximately 56% for the latter. In our study, the inability to identify factors triggering bothersome tinnitus negatively affected the treatment outcome in TRT. © 2014 S. Karger AG, Basel.

Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

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Gorka Ruiz de Garibay

2018-05-01

Full Text Available Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patient-derived xenografts (PDXs, we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2 is associated with this process. A triple-negative BRCA1-mutated PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance.

Using a Delphi Method to Identify Human Factors Contributing to Nursing Errors.

Science.gov (United States)

Roth, Cheryl; Brewer, Melanie; Wieck, K Lynn

2017-07-01

The purpose of this study was to identify human factors associated with nursing errors. Using a Delphi technique, this study used feedback from a panel of nurse experts (n = 25) on an initial qualitative survey questionnaire followed by summarizing the results with feedback and confirmation. Synthesized factors regarding causes of errors were incorporated into a quantitative Likert-type scale, and the original expert panel participants were queried a second time to validate responses. The list identified 24 items as most common causes of nursing errors, including swamping and errors made by others that nurses are expected to recognize and fix. The responses provided a consensus top 10 errors list based on means with heavy workload and fatigue at the top of the list. The use of the Delphi survey established consensus and developed a platform upon which future study of nursing errors can evolve as a link to future solutions. This list of human factors in nursing errors should serve to stimulate dialogue among nurses about how to prevent errors and improve outcomes. Human and system failures have been the subject of an abundance of research, yet nursing errors continue to occur. © 2016 Wiley Periodicals, Inc.

Identifying risk factors for PTSD in women seeking medical help after rape

OpenAIRE

Möller, Anna Tiihonen; Bäckström, Torbjörn; Söndergaard, Hans Peter; Helström, Lotti

2014-01-01

Objectives: Rape has been found to be the trauma most commonly associated with Posttraumatic Stress Disorder (PTSD) among women. It is therefore important to be able to identify those women at greatest risk of developing PTSD. The aims of the present study were to analyze the PTSD prevalence six months after sexual assaults and identify the major risk factors for developing PTSD. Methods: Participants were 317 female victims of rape who sought help at the Emergency Clinic for Raped Women at S...

Identify User’s Satisfaction from Platform Using Behavior

Directory of Open Access Journals (Sweden)

Kuo L.H.

2016-01-01

Full Text Available The purpose of this study was to verify a model of user’s satisfaction of an e-learning environment based upon platform using behaviors. This is a non-experimental study. The data was collected from system management logs and users satisfaction survey after learning service. Total 314 users were invited in this study. First, theory model was identified. Second, the satisfaction survey results were prepared. Third, the behavior data of each survey subject were prepared. A CFA procedure were conducted to verify whether the data fits into the model. The model fit is positive with χ2 = 2.06, p-value=.151, df= 1, RMSEA=.058. The proposed two-factor theory model with simple structure fit the data. The love e-learning and satisfaction of e-learning factors are significantly supporting the hypothesis of a relationship between the factors. The findings suggested that identifying satisfaction from behavior is possible.

Factor Analysis of Drawings: Application to college student models of the greenhouse effect

Science.gov (United States)

Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

2015-09-01

Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance, suggesting that 4 archetype models of the greenhouse effect dominate thinking within this population. Factor scores, indicating the extent to which each student's drawing aligned with representative models, were compared to performance on conceptual understanding and attitudes measures, demographics, and non-cognitive features of drawings. Student drawings were also compared to drawings made by scientists to ascertain the extent to which models reflect more sophisticated and accurate models. Results indicate that student and scientist drawings share some similarities, most notably the presence of some features of the most sophisticated non-scientific model held among the study population. Prior knowledge, prior attitudes, gender, and non-cognitive components are also predictive of an individual student's model. This work presents a new technique for analyzing drawings, with general implications for the use of drawings in investigating student conceptions.

INNOVATIVE PRACTICES IN TOURISM. APOSSIBLE MODEL BY FOSTERING SHADOW FACTORS

Directory of Open Access Journals (Sweden)

Ada Mirela TOMESCU

2015-08-01

Full Text Available The paper is the result of an empirical research, a study that includes a theoretical framework. The data used to test our hypotheses come from 60 small tourism firms from Bihor County, Romania. The research conducted has revealed that actions focusing on innovation must be based on a solid analysis, supported by the knowledge and the understanding of the contextual factors (environment, culture as a mental programming, values also based on the organizational factors (the management commitment, systemic perspective, learning and practice of experimentation, rapid transfer of knowledge within the organization. For the purpose of this work, the contextual factors that are exogenous represent the shadow factors. The studies performed in three European projects implemented in tourism SMEs of Bihor County have allowed us to advance the idea that contextual and organizational factors, that are identified as the source of innovation are based on rationality, which is enlarged by affectivity and imagination. The identified correlations may be considered, in our opinion an element of novelty and originality. Finally, the purpose of this paper is to provide a possible model, based on the idea of building an innovative firm, the one that has learned how to determine their own employees to be innovative. O03, L2, L26

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

Science.gov (United States)

Strakova, Eva; Zikova, Alice; Vohradsky, Jiri

2014-01-01

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

A Numerical Procedure for Model Identifiability Analysis Applied to Enzyme Kinetics

DEFF Research Database (Denmark)

Daele, Timothy, Van; Van Hoey, Stijn; Gernaey, Krist

2015-01-01

The proper calibration of models describing enzyme kinetics can be quite challenging. In the literature, different procedures are available to calibrate these enzymatic models in an efficient way. However, in most cases the model structure is already decided on prior to the actual calibration...... and Pronzato (1997) and which can be easily set up for any type of model. In this paper the proposed approach is applied to the forward reaction rate of the enzyme kinetics proposed by Shin and Kim(1998). Structural identifiability analysis showed that no local structural model problems were occurring......) identifiability problems. By using the presented approach it is possible to detect potential identifiability problems and avoid pointless calibration (and experimental!) effort....

Genome-wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition.

Science.gov (United States)

Knowles, Emma E M; Carless, Melanie A; de Almeida, Marcio A A; Curran, Joanne E; McKay, D Reese; Sprooten, Emma; Dyer, Thomas D; Göring, Harald H; Olvera, Rene; Fox, Peter; Almasy, Laura; Duggirala, Ravi; Kent, Jack W; Blangero, John; Glahn, David C

2014-01-01

It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis. © 2013 Wiley Periodicals, Inc.

The Importance of Business Model Factors for Cloud Computing Adoption: Role of Previous Experiences

Directory of Open Access Journals (Sweden)

Bogataj Habjan Kristina

2017-08-01

Full Text Available Background and Purpose: Bringing several opportunities for more effective and efficient IT governance and service exploitation, cloud computing is expected to impact the European and global economies significantly. Market data show that despite many advantages and promised benefits the adoption of cloud computing is not as fast and widespread as foreseen. This situation shows the need for further exploration of the potentials of cloud computing and its implementation on the market. The purpose of this research was to identify individual business model factors with the highest impact on cloud computing adoption. In addition, the aim was to identify the differences in opinion regarding the importance of business model factors on cloud computing adoption according to companies’ previous experiences with cloud computing services.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

Science.gov (United States)

Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

PWSCC Growth Assessment Model Considering Stress Triaxiality Factor for Primary Alloy 600 Components

Directory of Open Access Journals (Sweden)

Jong-Sung Kim

2016-08-01

Full Text Available We propose a primary water stress corrosion cracking (PWSCC initiation model of Alloy 600 that considers the stress triaxiality factor to apply to finite element analysis. We investigated the correlation between stress triaxiality effects and PWSCC growth behavior in cold-worked Alloy 600 stream generator tubes, and identified an additional stress triaxiality factor that can be added to Garud's PWSCC initiation model. By applying the proposed PWSCC initiation model considering the stress triaxiality factor, PWSCC growth simulations based on the macroscopic phenomenological damage mechanics approach were carried out on the PWSCC growth tests of various cold-worked Alloy 600 steam generator tubes and compact tension specimens. As a result, PWSCC growth behavior results from the finite element prediction are in good agreement with the experimental results.

Model Correction Factor Method

DEFF Research Database (Denmark)

Christensen, Claus; Randrup-Thomsen, Søren; Morsing Johannesen, Johannes

1997-01-01

The model correction factor method is proposed as an alternative to traditional polynomial based response surface techniques in structural reliability considering a computationally time consuming limit state procedure as a 'black box'. The class of polynomial functions is replaced by a limit...... of the model correction factor method, is that in simpler form not using gradient information on the original limit state function or only using this information once, a drastic reduction of the number of limit state evaluation is obtained together with good approximations on the reliability. Methods...

Shell model and spectroscopic factors

International Nuclear Information System (INIS)

Poves, P.

2007-01-01

In these lectures, I introduce the notion of spectroscopic factor in the shell model context. A brief review is given of the present status of the large scale applications of the Interacting Shell Model. The spectroscopic factors and the spectroscopic strength are discussed for nuclei in the vicinity of magic closures and for deformed nuclei. (author)

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

Science.gov (United States)

Golay, Philippe; Reverte, Isabelle; Rossier, Jérôme; Favez, Nicolas; Lecerf, Thierry

2013-06-01

The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

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Meitinger, T; Meindl, A; Bork, P; Rost, B; Sander, C; Haasemann, M; Murken, J

1993-12-01

The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

Risk factor screening to identify women requiring oral glucose tolerance testing to diagnose gestational diabetes: A systematic review and meta-analysis and analysis of two pregnancy cohorts.

Directory of Open Access Journals (Sweden)

Diane Farrar

Full Text Available Easily identifiable risk factors including: obesity and ethnicity at high risk of diabetes are commonly used to indicate which women should be offered the oral glucose tolerance test (OGTT to diagnose gestational diabetes (GDM. Evidence regarding these risk factors is limited however. We conducted a systematic review (SR and meta-analysis and individual participant data (IPD analysis to evaluate the performance of risk factors in identifying women with GDM.We searched MEDLINE, Medline in Process, Embase, Maternity and Infant Care and the Cochrane Central Register of Controlled Trials (CENTRAL up to August 2016 and conducted additional reference checking. We included observational, cohort, case-control and cross-sectional studies reporting the performance characteristics of risk factors used to identify women at high risk of GDM. We had access to IPD from the Born in Bradford and Atlantic Diabetes in Pregnancy cohorts, all pregnant women in the two cohorts with data on risk factors and OGTT results were included.Twenty nine published studies with 211,698 women for the SR and a further 14,103 women from two birth cohorts (Born in Bradford and the Atlantic Diabetes in Pregnancy study for the IPD analysis were included. Six studies assessed the screening performance of guidelines; six examined combinations of risk factors; eight evaluated the number of risk factors and nine examined prediction models or scores. Meta-analysis using data from published studies suggests that irrespective of the method used, risk factors do not identify women with GDM well. Using IPD and combining risk factors to produce the highest sensitivities, results in low specificities (and so higher false positives. Strategies that use the risk factors of age (>25 or >30 and BMI (>25 or 30 perform as well as other strategies with additional risk factors included.Risk factor screening methods are poor predictors of which pregnant women will be diagnosed with GDM. A simple

An effective automatic procedure for testing parameter identifiability of HIV/AIDS models.

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Saccomani, Maria Pia

2011-08-01

Realistic HIV models tend to be rather complex and many recent models proposed in the literature could not yet be analyzed by traditional identifiability testing techniques. In this paper, we check a priori global identifiability of some of these nonlinear HIV models taken from the recent literature, by using a differential algebra algorithm based on previous work of the author. The algorithm is implemented in a software tool, called DAISY (Differential Algebra for Identifiability of SYstems), which has been recently released (DAISY is freely available on the web site http://www.dei.unipd.it/~pia/ ). The software can be used to automatically check global identifiability of (linear and) nonlinear models described by polynomial or rational differential equations, thus providing a general and reliable tool to test global identifiability of several HIV models proposed in the literature. It can be used by researchers with a minimum of mathematical background.

An Application Of Receptor Modeling To Identify Airborne Particulate ...

African Journals Online (AJOL)

An Application Of Receptor Modeling To Identify Airborne Particulate Sources In Lagos, Nigeria. FS Olise, OK Owoade, HB Olaniyi. Abstract. There have been no clear demarcations between industrial and residential areas of Lagos with focus on industry as the major source. There is need to identify potential source types in ...

(Reinforcing) Factors Influencing a Physical Education Teacher's Use of the Direct Instruction Model Teaching Games

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Jayantilal, Kumar; O'Leary, Nick

2017-01-01

The purpose of this study was to explore how a physical education (PE) teacher employed the direct instruction model (DIM) teaching games in a United Kingdom secondary school. The research sought to identify how the teacher utilised the DIM and those factors that influenced his use of the model. Occupational socialization was used to identify the…

Identifying the principal driving factors of water ecosystem dependence and the corresponding indicator species in a pilot City, China

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Zhao, C. S.; Shao, N. F.; Yang, S. T.; Xiang, H.; Lou, H. Z.; Sun, Y.; Yang, Z. Y.; Zhang, Y.; Yu, X. Y.; Zhang, C. B.; Yu, Q.

2018-01-01

The world's aquatic ecosystems yield numerous vital services, which are essential to human existence but have deteriorated seriously in recent years. By studying the mechanisms of interaction between ecosystems and habitat processes, the constraining factors can be identified, and this knowledge can be used to improve the success rate of ecological restoration initiatives. At present, there is insufficient data on the link between hydrological, water quality factors and the changes in the structure of aquatic communities to allow any meaningful study of driving factors of aquatic ecosystems. In this study, the typical monitoring stations were selected by fuzzy clustering analysis based on the spatial and temporal distribution characteristics of water ecology in Jinan City, the first pilot city for the construction of civilized aquatic ecosystems in China. The dominant species identification model was used to identify the dominant species of the aquatic community. The driving effect of hydrological and water quality factors on dominant species was analyzed by Canonical Correspondence Analysis. Then, the principal factors of aquatic ecosystem dependence were selected. The results showed that there were 10 typical monitoring stations out of 59 monitoring sites, which were representative of aquatic ecosystems, 9 dominant fish species, and 20 dominant invertebrate species. The selection of factors for aquatic ecosystem dependence in Jinan were highly influenced by its regional conditions. Chemical environmental parameters influence the temporal and spatial variation of invertebrate much more than that of fish in Jinan City. However, the methodologies coupling typical monitoring stations selection, dominant species determination and driving factors identification were certified to be a cost-effective way, which can provide in-deep theoretical and technical directions for the restoration of aquatic ecosystems elsewhere.

Identifying risk factors of highly pathogenic avian influenza (H5N1 subtype) in Indonesia.

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Loth, Leo; Gilbert, Marius; Wu, Jianmei; Czarnecki, Christina; Hidayat, Muhammad; Xiao, Xiangming

2011-10-01

Highly pathogenic avian influenza (HPAI), subtype H5N1, was first officially reported in Indonesia in 2004. Since then the disease has spread and is now endemic in large parts of the country. This study investigated the statistical relationship between a set of risk factors and the presence or absence of HPAI in Indonesia during 2006 and 2007. HPAI was evaluated through participatory disease surveillance (PDS) in backyard village chickens (the study population), and risk factors included descriptors of people and poultry distribution (separating chickens, ducks and production sectors), poultry movement patterns and agro-ecological conditions. The study showed that the risk factors "elevation", "human population density" and "rice cropping" were significant in accounting for the spatial variation of the PDS-defined HPAI cases. These findings were consistent with earlier studies in Thailand and Vietnam. In addition "commercial poultry population", and two indicators of market locations and transport; "human settlements" and "road length", were identified as significant risk factors in the models. In contrast to several previous studies carried out in Southeast Asia, domestic backyard ducks were not found to be a significant risk factor in Indonesia. The study used surrogate estimates of market locations and marketing chains and further work should focus on the actual location of the live bird markets, and on the flow of live poultry and poultry products between them, so that patterns of possible transmission, and regions of particular risk could be better inferred. Copyright © 2011 Elsevier B.V. All rights reserved.

Patient and carer identified factors which contribute to safety incidents in primary care: a qualitative study.

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Hernan, Andrea L; Giles, Sally J; Fuller, Jeffrey; Johnson, Julie K; Walker, Christine; Dunbar, James A

2015-09-01

Patients can have an important role in reducing harm in primary-care settings. Learning from patient experience and feedback could improve patient safety. Evidence that captures patients' views of the various contributory factors to creating safe primary care is largely absent. The aim of this study was to address this evidence gap. Four focus groups and eight semistructured interviews were conducted with 34 patients and carers from south-east Australia. Participants were asked to describe their experiences of primary care. Audio recordings were transcribed verbatim and specific factors that contribute to safety incidents were identified in the analysis using the Yorkshire Contributory Factors Framework (YCFF). Other factors emerging from the data were also ascertained and added to the analytical framework. Thirteen factors that contribute to safety incidents in primary care were ascertained. Five unique factors for the primary-care setting were discovered in conjunction with eight factors present in the YCFF from hospital settings. The five unique primary care contributing factors to safety incidents represented a range of levels within the primary-care system from local working conditions to the upstream organisational level and the external policy context. The 13 factors included communication, access, patient factors, external policy context, dignity and respect, primary-secondary interface, continuity of care, task performance, task characteristics, time in the consultation, safety culture, team factors and the physical environment. Patient and carer feedback of this type could help primary-care professionals better understand and identify potential safety concerns and make appropriate service improvements. The comprehensive range of factors identified provides the groundwork for developing tools that systematically capture the multiple contributory factors to patient safety. Published by the BMJ Publishing Group Limited. For permission to use (where not

Risk factors identified for owner-reported feline obesity at around one year of age: Dry diet and indoor lifestyle.

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Rowe, Elizabeth; Browne, William; Casey, Rachel; Gruffydd-Jones, Tim; Murray, Jane

2015-10-01

Obesity is considered the second most common health problem in pet cats in developed countries. Previous studies investigating risk factors for feline obesity have been cross-sectional, where reverse causality cannot be ruled out. This study is the first to use prospective data from a large scale longitudinal study of pet cats ('Bristol Cats') to identify early-life risk factors for feline overweight/obesity at around one year of age. Data analysed were collected via three owner-completed questionnaires (for cats aged 2-4 months, 6.5-7 months and 12.5-13 months) completed between May 2010 and August 2013. Owner-reported body condition scores (BCS) of cats at age 12.5-13 months, using the 5-point system, were categorised into a dichotomous variable: overweight/obese (BCS 4-5) and not overweight (BCS 1-3) and used as the dependent variable. Cat breed, neuter status, outdoor access, type of diet, frequency of wet and dry food fed and frequency of treats fed were analysed as potential risk factors. Of the 966 cats for which data were available, 7.0% were reported by their owners to be overweight/obese at 12.5-13 months of age. Descriptive data on type of diet fed at different cat ages suggest that a dry diet is the most popular choice for UK domestic cats. Significant potential explanatory variables from univariable logistic regression models were included in multivariable logistic regression models built using stepwise forward-selection. To account for potential hierarchical clustering of data due to multi-cat households these were extended to two-level random intercept models. Models were compared using Wald test p- values. Clustering had no impact on the analysis. The final multivariable logistic regression model identified two risk factors that were independently associated with an increased risk of feline obesity developing at 12.5-13 months of age: restricted or no outdoor access and feeding dry food as the only or major (>50%) type of food in the diet at age 12

Structural identifiability of cyclic graphical models of biological networks with latent variables.

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Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

Zero-Inflated Poisson Modeling of Fall Risk Factors in Community-Dwelling Older Adults.

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Jung, Dukyoo; Kang, Younhee; Kim, Mi Young; Ma, Rye-Won; Bhandari, Pratibha

2016-02-01

The aim of this study was to identify risk factors for falls among community-dwelling older adults. The study used a cross-sectional descriptive design. Self-report questionnaires were used to collect data from 658 community-dwelling older adults and were analyzed using logistic and zero-inflated Poisson (ZIP) regression. Perceived health status was a significant factor in the count model, and fall efficacy emerged as a significant predictor in the logistic models. The findings suggest that fall efficacy is important for predicting not only faller and nonfaller status but also fall counts in older adults who may or may not have experienced a previous fall. The fall predictors identified in this study--perceived health status and fall efficacy--indicate the need for fall-prevention programs tailored to address both the physical and psychological issues unique to older adults. © The Author(s) 2014.

A simple method for identifying parameter correlations in partially observed linear dynamic models.

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Li, Pu; Vu, Quoc Dong

2015-12-14

Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a

Identifying depression severity risk factors in persons with traumatic spinal cord injury.

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Williams, Ryan T; Wilson, Catherine S; Heinemann, Allen W; Lazowski, Linda E; Fann, Jesse R; Bombardier, Charles H

2014-02-01

Examine the relationship between demographic characteristics, health-, and injury-related characteristics, and substance misuse across multiple levels of depression severity. 204 persons with traumatic spinal cord injury (SCI) volunteered as part of screening efforts for a randomized controlled trial of venlafaxine extended release for major depressive disorder (MDD). Instruments included the Patient Health Questionnaire-9 (PHQ-9) depression scale, the Alcohol Use Disorders Identification Test (AUDIT), and the Substance Abuse in Vocational Rehabilitation-Screener (SAVR-S), which contains 3 subscales: drug misuse, alcohol misuse, and a subtle items scale. Each of the SAVR-S subscales contributes to an overall substance use disorder (SUD) outcome. Three proportional odds models were specified, varying the substance misuse measure included in each model. 44% individuals had no depression symptoms, 31% had mild symptoms, 16% had moderate symptoms, 6% had moderately severe symptoms, and 3% had severe depression symptoms. Alcohol misuse, as indicated by the AUDIT and the SAVR-S drug misuse subscale scores were significant predictors of depression symptom severity. The SAVR-S substance use disorder (SUD) screening outcome was the most predictive variable. Level of education was only significantly predictive of depression severity in the model using the AUDIT alcohol misuse indicator. Likely SUD as measured by the SAVR-S was most predictive of depression symptom severity in this sample of persons with traumatic SCI. Drug and alcohol screening are important for identifying individuals at risk for depression, but screening for both may be optimal. Further research is needed on risk and protective factors for depression, including psychosocial characteristics. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Models of Anaylzing the Influence of Factors on Forming Profit Rate

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Klara S. Jakovčević

2014-04-01

Full Text Available The analysis in this paper is focused on identifying the impact of individual factors on the elements of the profit rate. The primary aim of this work is a methodological overview of solutions for understanding the full content of the profit rate as a cause of economic quality as well as indicators of the results of reproduction. Application of model analysis of profit rate factors was performed in an enterprise from Serbia that manufactures construction materials from baked clay. The aim is of application is to test the range in determining elements and factors of economic success of the enterprise, and quantification of changes in its assumptions. The results are useful guideline for the management to take organizational measures to increase the economic success of the enterprise. This means eliminating the negative, emphasizing the positive impact of objectively, and organizational factors to make higher economic success. Based on empirical research, it could be concluded that the proposed quantitative models of analyzing the dynamics of enterprise business quality could be applied in practice.

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

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Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p falls (OR = 0.25 with p factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis.

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Song, J; Zhao, H; Pan, C; Li, C; Liu, J; Pan, Y

2017-09-15

Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting. Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters. Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions.

APPLICATION OF MULTIPLE LOGISTIC REGRESSION, BAYESIAN LOGISTIC AND CLASSIFICATION TREE TO IDENTIFY THE SIGNIFICANT FACTORS INFLUENCING CRASH SEVERITY

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MILAD TAZIK

2017-11-01

Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

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Muñoz-Tamayo, R; Puillet, L; Daniel, J B; Sauvant, D; Martin, O; Taghipoor, M; Blavy, P

2018-04-01

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published

Structural identifiability of systems biology models: a critical comparison of methods.

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Oana-Teodora Chis

Full Text Available Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.

Unitary input DEA model to identify beef cattle production systems typologies

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Eliane Gonçalves Gomes

2012-08-01

Full Text Available The cow-calf beef production sector in Brazil has a wide variety of operating systems. This suggests the identification and the characterization of homogeneous regions of production, with consequent implementation of actions to achieve its sustainability. In this paper we attempted to measure the performance of 21 livestock modal production systems, in their cow-calf phase. We measured the performance of these systems, considering husbandry and production variables. The proposed approach is based on data envelopment analysis (DEA. We used unitary input DEA model, with apparent input orientation, together with the efficiency measurements generated by the inverted DEA frontier. We identified five modal production systems typologies, using the isoefficiency layers approach. The results showed that the knowledge and the processes management are the most important factors for improving the efficiency of beef cattle production systems.

Identifiability in N-mixture models: a large-scale screening test with bird data.

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Kéry, Marc

2018-02-01

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.

Identifying models of HIV care and treatment service delivery in Tanzania, Uganda, and Zambia using cluster analysis and Delphi survey.

Science.gov (United States)

Tsui, Sharon; Denison, Julie A; Kennedy, Caitlin E; Chang, Larry W; Koole, Olivier; Torpey, Kwasi; Van Praag, Eric; Farley, Jason; Ford, Nathan; Stuart, Leine; Wabwire-Mangen, Fred

2017-12-06

Organization of HIV care and treatment services, including clinic staffing and services, may shape clinical and financial outcomes, yet there has been little attempt to describe different models of HIV care in sub-Saharan Africa (SSA). Information about the relative benefits and drawbacks of different models could inform the scale-up of antiretroviral therapy (ART) and associated services in resource-limited settings (RLS), especially in light of expanded client populations with country adoption of WHO's test and treat recommendation. We characterized task-shifting/task-sharing practices in 19 diverse ART clinics in Tanzania, Uganda, and Zambia and used cluster analysis to identify unique models of service provision. We ran descriptive statistics to explore how the clusters varied by environmental factors and programmatic characteristics. Finally, we employed the Delphi Method to make systematic use of expert opinions to ensure that the cluster variables were meaningful in the context of actual task-shifting of ART services in SSA. The cluster analysis identified three task-shifting/task-sharing models. The main differences across models were the availability of medical doctors, the scope of clinical responsibility assigned to nurses, and the use of lay health care workers. Patterns of healthcare staffing in HIV service delivery were associated with different environmental factors (e.g., health facility levels, urban vs. rural settings) and programme characteristics (e.g., community ART distribution or integrated tuberculosis treatment on-site). Understanding the relative advantages and disadvantages of different models of care can help national programmes adapt to increased client load, select optimal adherence strategies within decentralized models of care, and identify differentiated models of care for clients to meet the growing needs of long-term ART patients who require more complicated treatment management.

Linear factor copula models and their properties

KAUST Repository

Krupskii, Pavel; Genton, Marc G.

2018-01-01

We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

Linear factor copula models and their properties

KAUST Repository

Krupskii, Pavel

2018-04-25

We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

EFFECTIVE FACTORS AND MODEL SYSTEMS IN THE INDUSTRIAL PRODUCTION OF NISIN

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Ömer ŞİMŞEK

2007-01-01

Full Text Available Nisin is the first bacteriocin identified in Lactococcus lactis and belongs to type 1 lanthibiotic group. High nisin production in cultured media is related with the composition of fermentation medium, pH, produced nisin concentration and most importantly growth amount of cell. For industrial purpose, batch, fed-batch and continue fermentation systems were developed by regarding these factors. Maintaining efficient production of nisin having important potential at preservation of foods is important for both industrial production and using as starter culture. In this review the fermentation factors at nisin production were outlined and constructed model systems were compared.

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

Science.gov (United States)

Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; Barker, Daniel W.

2015-01-01

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.

Identified state-space prediction model for aero-optical wavefronts

Science.gov (United States)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

Using exploratory factor analysis of FFQ data to identify dietary patterns among Yup'ik people.

Science.gov (United States)

Ryman, Tove K; Austin, Melissa A; Hopkins, Scarlett; Philip, Jacques; O'Brien, Diane; Thummel, Kenneth; Boyer, Bert B

2014-03-01

An FFQ developed by the Center for Alaska Native Health Research for studies in Yup'ik people includes market foods and subsistence foods such as moose, seal, waterfowl and salmon that may be related to disease risk. Because the FFQ contains >100 food items, we sought to characterize dietary patterns more simply for use in ongoing pharmacogenomics studies. Exploratory factor analysis was used to derive a small number of 'factors' that explain a substantial amount of the variation in the Yup'ik diet. We estimated factor scores and measured associations with demographic characteristics and biomarkers. South-west Alaska, USA. Yup'ik people (n 358) aged ≥18 years. We identified three factors that each accounted for ≥10 % of the common variance: the first characterized by 'processed foods' (e.g. salty snacks, sweetened cereals); the second by 'fruits and vegetables' (e.g. fresh citrus, potato salad); and the third by 'subsistence foods' (seal or walrus soup, non-oily fish). Participants from coastal communities had higher values for the 'subsistence' factor, whereas participants from inland communities had higher values for the 'fruits and vegetables' factor. A biomarker of marine intake, δ 15N, was correlated with the 'subsistence' factor, whereas a biomarker of corn- and sugarcane-based market food intake, δ 13C, was correlated with 'processed foods'. The exploratory factor analysis identified three factors that appeared to reflect dietary patterns among Yup'ik based on associations with participant characteristics and biomarkers. These factors will be useful for chronic disease studies in this population.

Factor Analysis of Therapist-Identified Treatment Targets in Community-Based Children's Mental Health.

Science.gov (United States)

Love, Allison R; Okado, Izumi; Orimoto, Trina E; Mueller, Charles W

2018-01-01

The present study used exploratory and confirmatory factor analyses to identify underlying latent factors affecting variation in community therapists' endorsement of treatment targets. As part of a statewide practice management program, therapist completed monthly reports of treatment targets (up to 10 per month) for a sample of youth (n = 790) receiving intensive in-home therapy. Nearly 75 % of youth were diagnosed with multiple co-occurring disorders. Five factors emerged: Disinhibition, Societal Rules Evasion, Social Engagement Deficits, Emotional Distress, and Management of Biodevelopmental Outcomes. Using logistic regression, primary diagnosis predicted therapist selection of Disinhibition and Emotional Distress targets. Client age predicted endorsement of Societal Rules Evasion targets. Practice-to-research implications are discussed.

Identifying risk factors for PTSD in women seeking medical help after rape.

Science.gov (United States)

Tiihonen Möller, Anna; Bäckström, Torbjörn; Söndergaard, Hans Peter; Helström, Lotti

2014-01-01

Rape has been found to be the trauma most commonly associated with Posttraumatic Stress Disorder (PTSD) among women. It is therefore important to be able to identify those women at greatest risk of developing PTSD. The aims of the present study were to analyze the PTSD prevalence six months after sexual assaults and identify the major risk factors for developing PTSD. Participants were 317 female victims of rape who sought help at the Emergency Clinic for Raped Women at Stockholm South Hospital, Sweden. Baseline assessments of mental health were carried out and followed up after six months. Thirty-nine percent of the women had developed PTSD at the six month assessment, and 47% suffered from moderate or severe depression. The major risk factors for PTSD were having been sexually assaulted by more than one person, suffering from acute stress disorder (ASD) shortly after the assault, having been exposed to several acts during the assault, having been injured, having co-morbid depression, and having a history of more than two earlier traumas. Further, ASD on its own was found to be a poor predictor of PTSD because of the substantial ceiling effect after sexual assaults. Development of PTSD is common in the aftermath of sexual assaults. Increased risk of developing PTSD is caused by a combination of victim vulnerability and the extent of the dramatic nature of the current assault. By identifying those women at greatest risk of developing PTSD appropriate therapeutic resources can be directed.

Latent Fundamentals Arbitrage with a Mixed Effects Factor Model

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Andrei Salem Gonçalves

2012-09-01

Full Text Available We propose a single-factor mixed effects panel data model to create an arbitrage portfolio that identifies differences in firm-level latent fundamentals. Furthermore, we show that even though the characteristics that affect returns are unknown variables, it is possible to identify the strength of the combination of these latent fundamentals for each stock by following a simple approach using historical data. As a result, a trading strategy that bought the stocks with the best fundamentals (strong fundamentals portfolio and sold the stocks with the worst ones (weak fundamentals portfolio realized significant risk-adjusted returns in the U.S. market for the period between July 1986 and June 2008. To ensure robustness, we performed sub period and seasonal analyses and adjusted for trading costs and we found further empirical evidence that using a simple investment rule, that identified these latent fundamentals from the structure of past returns, can lead to profit.

Predicting survival function and identifying associated factors in patients with renal insufficiency in the metropolitan area of Maringá, Paraná State, Brazil.

Science.gov (United States)

Ramires, Thiago G; Nakamura, Luiz R; Righetto, Ana J; Ortega, Edwin M M; Cordeiro, Gauss M

2018-02-05

Renal insufficiency is a serious medical and public health problem worldwide. Recently, although many surveys have been developed to identify factors related to the lifetime of patients with renal insufficiency, controversial results from several studies suggest that researches should be conducted by region. Thus, in this study we aim to predict and identify factors associated with the lifetime of patients with chronic renal failure (CRF) in the metropolitan area of Maringá, Paraná State, Brazil, based on the generalized additive models for location, scale and shape (GAMLSS) framework. Data used in this study were collected from the Maringá Kidney Institute and comprehends 177 patients (classified with CRF and mostly being treated under the Brazilian Unified National Health System) enrolled in a hemodialysis program from 1978 up to 2010. By using this approach, we concluded that in other regions, gender, kidney transplant indicator, antibodies to hepatitis B and antibodies to hepatitis C are significant factors that affect the expected lifetime.

Predicting survival function and identifying associated factors in patients with renal insufficiency in the metropolitan area of Maringá, Paraná State, Brazil

Directory of Open Access Journals (Sweden)

Thiago G. Ramires

2018-02-01

Full Text Available Renal insufficiency is a serious medical and public health problem worldwide. Recently, although many surveys have been developed to identify factors related to the lifetime of patients with renal insufficiency, controversial results from several studies suggest that researches should be conducted by region. Thus, in this study we aim to predict and identify factors associated with the lifetime of patients with chronic renal failure (CRF in the metropolitan area of Maringá, Paraná State, Brazil, based on the generalized additive models for location, scale and shape (GAMLSS framework. Data used in this study were collected from the Maringá Kidney Institute and comprehends 177 patients (classified with CRF and mostly being treated under the Brazilian Unified National Health System enrolled in a hemodialysis program from 1978 up to 2010. By using this approach, we concluded that in other regions, gender, kidney transplant indicator, antibodies to hepatitis B and antibodies to hepatitis C are significant factors that affect the expected lifetime.

The Five-Factor Model: General Overview

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A A Vorobyeva

2011-12-01

Full Text Available The article describes the five-factor model (FFM, giving an overview of its history, basic dimensions, cross-cultural research conducted on the model and highlights some practical studies based on the FFM, including the studies on job performance, leader performance and daily social interactions. An overview of the recent five-factor theory is also provided. According to the theory, the five factors are encoded in human genes, therefore it is almost impossible to change the basic factors themselves, but a person's behavior might be changed due to characteristic adaptations which do not alter personality dimensions, only a person's behavior.

A survey of the influencing factors and models for resident's household waste management behavior

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The problem of household solid waste has been concerned and researched on by municipalities and researchers.At present, household solid waste has been changed to management problem from technical one. From the point view of management, the research on household solid waste is to study the factors which influence resident's behavior of managtng their waste. Based on the literature review, firstly, this paper summarizes those factors which have already been identified to have impact on resident's behavior of managing their waste. They are social-demographic variables,knowledge, environmental values, psychological factors, publicity and system design. Secondly, three typical models of the relationship between factors and behavior, which are factors determining task performance in waste management,conceptualization of waste management behavior and the theoretical model of repeated behavior on household waste management, are analyzed and the deficiencies of these models are also analyzed. Finally, according to the current situation in household waste management and the culture and resident's habits in China, this paper puts forward a research focus and suggestions about resident 's behavior of household solid waste management.

Pre-Analytical Parameters Affecting Vascular Endothelial Growth Factor Measurement in Plasma: Identifying Confounders.

Science.gov (United States)

Walz, Johanna M; Boehringer, Daniel; Deissler, Heidrun L; Faerber, Lothar; Goepfert, Jens C; Heiduschka, Peter; Kleeberger, Susannah M; Klettner, Alexa; Krohne, Tim U; Schneiderhan-Marra, Nicole; Ziemssen, Focke; Stahl, Andreas

2016-01-01

Vascular endothelial growth factor-A (VEGF-A) is intensively investigated in various medical fields. However, comparing VEGF-A measurements is difficult because sample acquisition and pre-analytic procedures differ between studies. We therefore investigated which variables act as confounders of VEGF-A measurements. Following a standardized protocol, blood was taken at three clinical sites from six healthy participants (one male and one female participant at each center) twice one week apart. The following pre-analytical parameters were varied in order to analyze their impact on VEGF-A measurements: analyzing center, anticoagulant (EDTA vs. PECT / CTAD), cannula (butterfly vs. neonatal), type of centrifuge (swing-out vs. fixed-angle), time before and after centrifugation, filling level (completely filled vs. half-filled tubes) and analyzing method (ELISA vs. multiplex bead array). Additionally, intrapersonal variations over time and sex differences were explored. Statistical analysis was performed using a linear regression model. The following parameters were identified as statistically significant independent confounders of VEGF-A measurements: analyzing center, anticoagulant, centrifuge, analyzing method and sex of the proband. The following parameters were no significant confounders in our data set: intrapersonal variation over one week, cannula, time before and after centrifugation and filling level of collection tubes. VEGF-A measurement results can be affected significantly by the identified pre-analytical parameters. We recommend the use of CTAD anticoagulant, a standardized type of centrifuge and one central laboratory using the same analyzing method for all samples.

Pre-Analytical Parameters Affecting Vascular Endothelial Growth Factor Measurement in Plasma: Identifying Confounders.

Directory of Open Access Journals (Sweden)

Johanna M Walz

Full Text Available Vascular endothelial growth factor-A (VEGF-A is intensively investigated in various medical fields. However, comparing VEGF-A measurements is difficult because sample acquisition and pre-analytic procedures differ between studies. We therefore investigated which variables act as confounders of VEGF-A measurements.Following a standardized protocol, blood was taken at three clinical sites from six healthy participants (one male and one female participant at each center twice one week apart. The following pre-analytical parameters were varied in order to analyze their impact on VEGF-A measurements: analyzing center, anticoagulant (EDTA vs. PECT / CTAD, cannula (butterfly vs. neonatal, type of centrifuge (swing-out vs. fixed-angle, time before and after centrifugation, filling level (completely filled vs. half-filled tubes and analyzing method (ELISA vs. multiplex bead array. Additionally, intrapersonal variations over time and sex differences were explored. Statistical analysis was performed using a linear regression model.The following parameters were identified as statistically significant independent confounders of VEGF-A measurements: analyzing center, anticoagulant, centrifuge, analyzing method and sex of the proband. The following parameters were no significant confounders in our data set: intrapersonal variation over one week, cannula, time before and after centrifugation and filling level of collection tubes.VEGF-A measurement results can be affected significantly by the identified pre-analytical parameters. We recommend the use of CTAD anticoagulant, a standardized type of centrifuge and one central laboratory using the same analyzing method for all samples.

Proteomic analysis identifies insulin-like growth factor-binding protein-related protein-1 as a podocyte product.

Science.gov (United States)

Matsumoto, Takayuki; Hess, Sonja; Kajiyama, Hiroshi; Sakairi, Toru; Saleem, Moin A; Mathieson, Peter W; Nojima, Yoshihisa; Kopp, Jeffrey B

2010-10-01

The podocyte secretory proteome may influence the phenotype of adjacent podocytes, endothelial cells, parietal epithelial cells, and tubular epithelial cells but has not been systematically characterized. We have initiated studies to characterize this proteome, with the goal of further understanding the podocyte cell biology. We cultured differentiated conditionally immortalized human podocytes and subjected the proteins in conditioned medium to mass spectrometry. At a false discovery rate of factor-binding protein-related protein-1 (IGFBP-rP1), was expressed in mRNA and protein of cultured podocytes. In addition, transforming growth factor-β1 stimulation increased IGFBP-rP1 in conditioned medium. We analyzed IGFBP-rP1 glomerular expression in a mouse model of human immunodeficiency virus-associated nephropathy. IGFBP-rP1 was absent from podocytes of normal mice and was expressed in podocytes and pseudocrescents of transgenic mice, where it was coexpressed with desmin, a podocyte injury marker. We conclude that IGFBP-rP1 may be a product of injured podocytes. Further analysis of the podocyte secretory proteome may identify biomarkers of podocyte injury.

Identifying nonproportional covariates in the Cox model

Czech Academy of Sciences Publication Activity Database

Kraus, David

2008-01-01

Roč. 37, č. 4 (2008), s. 617-625 ISSN 0361-0926 R&D Projects: GA AV ČR(CZ) IAA101120604; GA MŠk(CZ) 1M06047; GA ČR(CZ) GD201/05/H007 Institutional research plan: CEZ:AV0Z10750506 Keywords : Cox model * goodness of fit * proportional hazards assumption * time-varying coefficients Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.324, year: 2008

A preclinical model for identifying rats at risk of alcohol use disorder

KAUST Repository

Jadhav, Kshitij S.; Magistretti, Pierre J.; Halfon, Olivier; Augsburger, Marc; Boutrel, Benjamin

2017-01-01

Alcohol use is one of the world's leading causes of death and disease, although only a small proportion of individuals develop persistent alcohol use disorder (AUD). The identification of vulnerable individuals prior to their chronic intoxication remains of highest importance. We propose here to adapt current methodologies for identifying rats at risk of losing control over alcohol intake by modeling diagnostic criteria for AUD: Inability to abstain during a signaled period of reward unavailability, increased motivation assessed in a progressive effortful task and persistent alcohol intake despite aversive foot shocks. Factor analysis showed that these three addiction criteria loaded on one underlying construct indicating that they represent a latent construct of addiction trait. Further, not only vulnerable rats displayed higher ethanol consumption, and higher preference for ethanol over sweetened solutions, but they also exhibited pre-existing higher anxiety as compared to resilient rats. In conclusion, the present preclinical model confirms that development of an addiction trait not only requires prolonged exposure to alcohol, but also depends on endophenotype like anxiety that predispose a minority of individuals to lose control over alcohol consumption.

A preclinical model for identifying rats at risk of alcohol use disorder

KAUST Repository

Jadhav, Kshitij S.

2017-08-21

Alcohol use is one of the world\\'s leading causes of death and disease, although only a small proportion of individuals develop persistent alcohol use disorder (AUD). The identification of vulnerable individuals prior to their chronic intoxication remains of highest importance. We propose here to adapt current methodologies for identifying rats at risk of losing control over alcohol intake by modeling diagnostic criteria for AUD: Inability to abstain during a signaled period of reward unavailability, increased motivation assessed in a progressive effortful task and persistent alcohol intake despite aversive foot shocks. Factor analysis showed that these three addiction criteria loaded on one underlying construct indicating that they represent a latent construct of addiction trait. Further, not only vulnerable rats displayed higher ethanol consumption, and higher preference for ethanol over sweetened solutions, but they also exhibited pre-existing higher anxiety as compared to resilient rats. In conclusion, the present preclinical model confirms that development of an addiction trait not only requires prolonged exposure to alcohol, but also depends on endophenotype like anxiety that predispose a minority of individuals to lose control over alcohol consumption.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

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Wasserman Wyeth W

2011-03-01

Full Text Available Abstract Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs, microRNAs (miRNAs and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs. Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL. In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT, an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http

Overview of Building Information Modelling (BIM) adoption factors for construction organisations

Science.gov (United States)

Mohammad, W. N. S. Wan; Abdullah, M. R.; Ismail, S.; Takim, R.

2018-04-01

Improvement and innovation in building visualization, project coordination and communication are the major benefits generated by Building Information Modelling (BIM) for construction organisations. Thus, as many firms across the world would adopt BIM, however they do not know the clear direction in which path they are moving as there is no specific reference available for them to refer to. Hence, the paper seeks to identify the factors of BIM adoption from previous research. The methodology used in this paper is based on literature review from various sources such as conference articles and journals. Then, the findings were analysed using content analysis. The findings show that there are 24 factors found from literature that influence the adoption of BIM and four (4) factors such as vendor, organisational vision, knowledge, and implementation plan are among the least factors mentioned by previous researchers.

Experimental infections with Mycoplasma agalactiae identify key factors involved in host-colonization.

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Eric Baranowski

Full Text Available Mechanisms underlying pathogenic processes in mycoplasma infections are poorly understood, mainly because of limited sequence similarities with classical, bacterial virulence factors. Recently, large-scale transposon mutagenesis in the ruminant pathogen Mycoplasma agalactiae identified the NIF locus, including nifS and nifU, as essential for mycoplasma growth in cell culture, while dispensable in axenic media. To evaluate the importance of this locus in vivo, the infectivity of two knock-out mutants was tested upon experimental infection in the natural host. In this model, the parental PG2 strain was able to establish a systemic infection in lactating ewes, colonizing various body sites such as lymph nodes and the mammary gland, even when inoculated at low doses. In these PG2-infected ewes, we observed over the course of infection (i the development of a specific antibody response and (ii dynamic changes in expression of M. agalactiae surface variable proteins (Vpma, with multiple Vpma profiles co-existing in the same animal. In contrast and despite a sensitive model, none of the knock-out mutants were able to survive and colonize the host. The extreme avirulent phenotype of the two mutants was further supported by the absence of an IgG response in inoculated animals. The exact role of the NIF locus remains to be elucidated but these data demonstrate that it plays a key role in the infectious process of M. agalactiae and most likely of other pathogenic mycoplasma species as many carry closely related homologs.

Identifying factors associated with the discharge of male State patients from Weskoppies Hospital

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Riaan G. Prinsloo

2017-12-01

Full Text Available Background: Designated psychiatric facilities are responsible for the care, treatment and reintegration of State patients. The necessary long-term care places a considerable strain on health-care resources. Resource use should be optimised while managing the risks that patients pose to themselves and the community. Identifying unique factors associated with earlier discharge may decrease the length of stay. Factors associated with protracted inpatient care without discharge could identify patients who require early and urgent intervention. Aim: We identify socio-economic, demographic, psychiatric and charge-related factors associated with the discharge of male State patients. Methods: We reviewed the files of discharged and admitted forensic State patients at Weskoppies Psychiatric Hospital. Data were captured in an electronic recording sheet. The association between factors and the outcome measure (discharged vs. admitted was determined using chi-squared tests and Fischer’s exact tests. Results: Discharged State patients were associated with being a primary caregiver (p = 0.031 having good insight into illness (p = 0.025 or offence (p = 0.005 and having had multiple successful leaves of absences. A lack of substance abuse during admission (p = 0.027, an absence of a diagnosis of substance use disorder (p = 0.013 and the absence of verbal and physical aggression (p = 0.002 and p = 0.016 were associated with being discharged. Prolonged total length of stay (9–12 years, p = 0.031 and prolonged length of stay in open wards (6–9 years, p = 0.000 were associated with being discharged. A history of previous offences (p = 0.022, a diagnosis of substance use disorder (p = 0.023, recent substance abuse (p = 0.018 and a history of physical aggression since admission (p = 0.017 were associated with continued admission. Conclusion: Discharge of State patients is associated with an absence of substance abuse, lack of aggression

Identifying Factors Influencing the Establishment of a Health System Reform Plan in Iran's Public Hospitals

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Rasul Fani khiavi

2016-09-01

Full Text Available In today's world, health views have found a wider perspective in which non-medical expectations are particularly catered to. The health system reform plan seeks to improve society's health, decrease treatment costs, and increase patient satisfaction. This study investigated factors affecting the successful establishment of a health system reform plan. A mixed qualitative – quantitative approach was applied to conduct to explore influential factors associated with the establishment of a health system reform plan in Iran's public hospitals. The health systems and approaches to improving them in other countries have been studied. A Likert-based five-point questionnaire was the measurement instrument, and its content validity based on content validity ratio (CVR was 0.87. The construct validity, calculated using the factorial analysis and Kaiser Mayer Olkin (KMO techniques, was 0.964, which is a high level and suggests a correlation between the scale items. To complete the questionnaire, 185 experts, specialists, and executives of Iran’s health reform plan were selected using the Purposive Stratified Non Random Sampling and snowball methods. The data was then analyzed using exploratory factorial analysis and SPSS and LISREL software applications. The results of this research imply the existence of a pattern with a significant and direct relationship between the identified independent variables and the dependent variable of the establishment of a health system reform plan. The most important indices of establishing a health system reform plan, in the order of priority, were political support; suitable proportion and coverage of services presented in the society; management of resources; existence of necessary infrastructures; commitment of senior managers; constant planning, monitoring, and evaluation; and presentation of feedback to the plan's executives, intrasector/extrasector cooperation, and the plan’s guiding committee. Considering the

Bayesian inference for partially identified models exploring the limits of limited data

CERN Document Server

Gustafson, Paul

2015-01-01

Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Identified ModelsThe Road Ahead The Structure of Inference in Partially Identified Models Bayesian Inference The Structure of Posterior Distributions in PIMs Computational Strategies Strength of Bayesian Updating, Revisited Posterior MomentsCredible Intervals Evaluating the Worth of Inference Partial Identification versus Model Misspecification The Siren Call of Identification Comp

Personality-Related Problems and the Five-Factor Model of Personality

OpenAIRE

Boudreaux, Michael

2014-01-01

This research identifies a broad and inclusive set of personality-related problems and examines their empirical associations with both the high and low poles of the five-factor model of personality (FFM). McCrae, Widiger, and colleagues (e.g., McCrae, 1994; McCrae, Löckenhoff, & Costa, 2005; Widiger, Costa, & McCrae, 2002, 2012) have proposed that individuals with particular personality traits may be predisposed to particular kinds of problems in life, and suggested that the FFM serve as a ba...

Chou-Yang model and PHI form factor

Energy Technology Data Exchange (ETDEWEB)

Fazal-e-Aleem; Saleem, M.; Rafique, M.

1988-03-01

By using the deduced differential cross-section data for PHIp elastic scattering at 175 GeV/c in the Chou-Yang model, the PHI form factor has been computed and parametrized. Then in conjunction with the proton form factor this form factor is used in the pristine Chou-Yang model to obtain differential cross-section data at Fermilab energies. The theoretical results agree with the experimental measurements, endorsing the conjecture that the hadronic form factor of neutral particle is proportional to its magnetic form factor.

Beyond the first episode: candidate factors for a risk prediction model of schizophrenia.

Science.gov (United States)

Murphy, Brendan P

2010-01-01

Many early psychosis services are financially compromised and cannot offer a full tenure of care to all patients. To maintain viability of services it is important that those with schizophrenia are identified early to maximize long-term outcomes, as are those with better prognoses who can be discharged early. The duration of untreated psychosis remains the mainstay in determining those who will benefit from extended care, yet its ability to inform on prognosis is modest in both the short and medium term. There are a number of known or putative genetic and environmental risk factors that have the potential to improve prognostication, though a multivariate risk prediction model combining them with clinical characteristics has yet to be developed. Candidate risk factors for such a model are presented, with an emphasis on environmental risk factors. More work is needed to corroborate many putative factors and to determine which of the established factors are salient and which are merely proxy measures. Future research should help clarify how gene-environment and environment-environment interactions occur and whether risk factors are dose-dependent, or if they act additively or synergistically, or are redundant in the presence (or absence) of other factors.

Examining the Factors Affecting PDA Acceptance among Physicians: An Extended Technology Acceptance Model.

Science.gov (United States)

Basak, Ecem; Gumussoy, Cigdem Altin; Calisir, Fethi

2015-01-01

This study aims at identifying the factors affecting the intention to use personal digital assistant (PDA) technology among physicians in Turkey using an extended Technology Acceptance Model (TAM). A structural equation-modeling approach was used to identify the variables that significantly affect the intention to use PDA technology. The data were collected from 339 physicians in Turkey. Results indicated that 71% of the physicians' intention to use PDA technology is explained by perceived usefulness and perceived ease of use. On comparing both, the perceived ease of use has the strongest effect, whereas the effect of perceived enjoyment on behavioral intention to use is found to be insignificant. This study concludes with the recommendations for managers and possible future research.

Using Hierarchical Linear Modelling to Examine Factors Predicting English Language Students' Reading Achievement

Science.gov (United States)

Fung, Karen; ElAtia, Samira

2015-01-01

Using Hierarchical Linear Modelling (HLM), this study aimed to identify factors such as ESL/ELL/EAL status that would predict students' reading performance in an English language arts exam taken across Canada. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) along with the accompanying surveys for students and…

Identifying risk factors for PTSD in women seeking medical help after rape.

Directory of Open Access Journals (Sweden)

Anna Tiihonen Möller

Full Text Available Rape has been found to be the trauma most commonly associated with Posttraumatic Stress Disorder (PTSD among women. It is therefore important to be able to identify those women at greatest risk of developing PTSD. The aims of the present study were to analyze the PTSD prevalence six months after sexual assaults and identify the major risk factors for developing PTSD.Participants were 317 female victims of rape who sought help at the Emergency Clinic for Raped Women at Stockholm South Hospital, Sweden. Baseline assessments of mental health were carried out and followed up after six months.Thirty-nine percent of the women had developed PTSD at the six month assessment, and 47% suffered from moderate or severe depression. The major risk factors for PTSD were having been sexually assaulted by more than one person, suffering from acute stress disorder (ASD shortly after the assault, having been exposed to several acts during the assault, having been injured, having co-morbid depression, and having a history of more than two earlier traumas. Further, ASD on its own was found to be a poor predictor of PTSD because of the substantial ceiling effect after sexual assaults.Development of PTSD is common in the aftermath of sexual assaults. Increased risk of developing PTSD is caused by a combination of victim vulnerability and the extent of the dramatic nature of the current assault. By identifying those women at greatest risk of developing PTSD appropriate therapeutic resources can be directed.

Organisational Issues for E-Learning: Critical Success Factors as Identified by HE Practitioners

Science.gov (United States)

McPherson, Maggie; Nunes, Miguel Baptista

2006-01-01

Purpose: The purpose of this paper is to report on a research project that identified organisational critical success factors (CSFs) for e-learning implementation in higher education (HE). These CSFs can be used as a theoretical foundation upon which to base decision-making and strategic thinking about e-learning. Design/methodology/approach: The…

Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer.

Directory of Open Access Journals (Sweden)

Philip Stegmaier

Full Text Available The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification.

Calibration of Local Area Weather Radar-Identifying significant factors affecting the calibration

DEFF Research Database (Denmark)

Pedersen, Lisbeth; Jensen, Niels Einar; Madsen, Henrik

2010-01-01

A Local Area Weather Radar (LAWR) is an X-band weather radar developed to meet the needs of high resolution rainfall data for hydrological applications. The LAWR system and data processing methods are reviewed in the first part of this paper, while the second part of the paper focuses...... cases when the calibration is based on a factorized 3 parameter linear model instead of a single parameter linear model....

Factors Influencing Implementation of OHSAS 18001 in Indian Construction Organizations: Interpretive Structural Modeling Approach.

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Rajaprasad, Sunku Venkata Siva; Chalapathi, Pasupulati Venkata

2015-09-01

Construction activity has made considerable breakthroughs in the past two decades on the back of increases in development activities, government policies, and public demand. At the same time, occupational health and safety issues have become a major concern to construction organizations. The unsatisfactory safety performance of the construction industry has always been highlighted since the safety management system is neglected area and not implemented systematically in Indian construction organizations. Due to a lack of enforcement of the applicable legislation, most of the construction organizations are forced to opt for the implementation of Occupational Health Safety Assessment Series (OHSAS) 18001 to improve safety performance. In order to better understand factors influencing the implementation of OHSAS 18001, an interpretive structural modeling approach has been applied and the factors have been classified using matrice d'impacts croises-multiplication appliqué a un classement (MICMAC) analysis. The study proposes the underlying theoretical framework to identify factors and to help management of Indian construction organizations to understand the interaction among factors influencing in implementation of OHSAS 18001. Safety culture, continual improvement, morale of employees, and safety training have been identified as dependent variables. Safety performance, sustainable construction, and conducive working environment have been identified as linkage variables. Management commitment and safety policy have been identified as the driver variables. Management commitment has the maximum driving power and the most influential factor is safety policy, which states clearly the commitment of top management towards occupational safety and health.

Exploring Environmental Factors in Nursing Workplaces That Promote Psychological Resilience: Constructing a Unified Theoretical Model.

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Cusack, Lynette; Smith, Morgan; Hegney, Desley; Rees, Clare S; Breen, Lauren J; Witt, Regina R; Rogers, Cath; Williams, Allison; Cross, Wendy; Cheung, Kin

2016-01-01

Building nurses' resilience to complex and stressful practice environments is necessary to keep skilled nurses in the workplace and ensuring safe patient care. A unified theoretical framework titled Health Services Workplace Environmental Resilience Model (HSWERM), is presented to explain the environmental factors in the workplace that promote nurses' resilience. The framework builds on a previously-published theoretical model of individual resilience, which identified the key constructs of psychological resilience as self-efficacy, coping and mindfulness, but did not examine environmental factors in the workplace that promote nurses' resilience. This unified theoretical framework was developed using a literary synthesis drawing on data from international studies and literature reviews on the nursing workforce in hospitals. The most frequent workplace environmental factors were identified, extracted and clustered in alignment with key constructs for psychological resilience. Six major organizational concepts emerged that related to a positive resilience-building workplace and formed the foundation of the theoretical model. Three concepts related to nursing staff support (professional, practice, personal) and three related to nursing staff development (professional, practice, personal) within the workplace environment. The unified theoretical model incorporates these concepts within the workplace context, linking to the nurse, and then impacting on personal resilience and workplace outcomes, and its use has the potential to increase staff retention and quality of patient care.

A model of real estate and psychological factors in decision-making to buy real estate

Directory of Open Access Journals (Sweden)

Bojan Grum

2015-06-01

Full Text Available This article explores the psychological characteristics of potential real estate buyers connected with their decision to buy. Through a review of research, it reveals that most studies of psychological factors in the decision to buy real estate have a partial and dispersed orientation, and examine individual factors independently. It appears that the research area is lacking clearly defined models of psychological factors in the decision to buy real estate that would integrally and relationally explain the role of psychological characteristics of real estate buyers and their expectations in relation to a decision to buy. The article identifies two sets of psychological factors, motivational and emotional, determines their interaction with potential buyers’ expectations when deciding to purchase real estate and offers starting points for forming a model.

Identifying and Ranking the Effective Factors on Successful Implementation of Social Commerce in Iran, Using AHP Fuzzy

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Zahra Rahimi

2016-07-01

Full Text Available Social commerce has been introduced as a new approach to increase sales, number of customers and reduce marketing expenditures. This approach is a combination of business, communication between people, as well as communicative and informative technologies based on web 2.0 Its achievement originated from different factors relied on business, individuals, culture, and technology. These factors have been primarily identified on the basis of library researches and classified into six infrastructural groups including:  technical, economical and human resources, cultural, rules governing the countries, style of management, and business. Then, it identified priority of the factors by using the fuzzy analytic hierarchy process (AHP. Innovation of this research was to extract a comprehensive list of factors and to prioritize them based on specific conditions in Iran.

Factors affecting increased risk for substance use disorders following traumatic brain injury: What we can learn from animal models.

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Merkel, Steven F; Cannella, Lee Anne; Razmpour, Roshanak; Lutton, Evan; Raghupathi, Ramesh; Rawls, Scott M; Ramirez, Servio H

2017-06-01

Recent studies have helped identify multiple factors affecting increased risk for substance use disorders (SUDs) following traumatic brain injury (TBI). These factors include age at the time of injury, repetitive injury and TBI severity, neurocircuits, neurotransmitter systems, neuroinflammation, and sex differences. This review will address each of these factors by discussing 1) the clinical and preclinical data identifying patient populations at greatest risk for SUDs post-TBI, 2) TBI-related neuropathology in discrete brain regions heavily implicated in SUDs, and 3) the effects of TBI on molecular mechanisms that may drive substance abuse behavior, like dopaminergic and glutamatergic transmission or neuroimmune signaling in mesolimbic regions of the brain. Although these studies have laid the groundwork for identifying factors that affect risk of SUDs post-TBI, additional studies are required. Notably, preclinical models have been shown to recapitulate many of the behavioral, cellular, and neurochemical features of SUDs and TBI. Therefore, these models are well suited for answering important questions that remain in future investigations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.

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Kim, Seongho; Li, Lang

2014-02-01

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Identifying the connective strength between model parameters and performance criteria

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B. Guse

2017-11-01

Full Text Available In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE, Kling–Gupta efficiency (KGE and its three components (alpha, beta and r as well as RSR (the ratio of the root mean square error to the standard deviation for different segments of the flow duration curve (FDC are calculated. With a joint analysis of two regression tree (RT approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

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Hildebrandt, Tom; Epstein, Elizabeth E.; Sysko, Robyn; Bux, Donald A.

2017-01-01

Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs. PMID:28247423

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

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Villaverde, Alejandro F; Banga, Julio R

2017-11-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.

Identifying and prioritizing the factors effective in customer satisfaction using the TOPSIS method

Directory of Open Access Journals (Sweden)

H Forougozar

2014-01-01

Full Text Available Introduction: Customer satisfaction has been suggested as one of the interesting and challenging issues of management in the new millennium. In addition, oral and dental health and the quality of the services the health centers delivered to the patients directly affect the customer satisfaction. Therefore, the present study aimed to identify, investigate, and rank the factors affecting the customer satisfaction in the department of dentistry of Shiraz Farhangiyan health center. Method: The present descriptive study was conducted on the specialists and patients of the department of dentistry of Shiraz Farhangiyan health center. The validity of the questionnaire utilized in the study was confirmed by expert professors and its reliability was approved using the Cronbach’s alpha formula. Finally, the study data were analyzed in SPSS statistical software (v. 16, using inferential statistics. Results: All the hypotheses were confirmed by the results of the statistical analyses and quality, services, and expenditures revealed to affect the customer satisfaction in the department of dentistry of Shiraz Farhangiyan health center. Moreover, these factors were ranked using the TOPSIS method and the results showed quality and expenditures as the most and the least effective factors in customer satisfaction, respectively. Conclusion: Since restoring and arranging the organization based on the customer needs is among the main priorities of designing an organization, managers are suggested to take measures for organizational reformation based on the customers’ priorities. Of course, conducting such programs is of utmost importance in health and treatment environments, leading to provision of better services and facilitation of learning, education, and research. Thus, identifying the effective factors in customer satisfaction and ranking them are highly important.

Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

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Kese Pontes Freitas Alberton

2015-01-01

Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

A cross-sectional study of 329 farms in England to identify risk factors for ovine clinical mastitis.

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Cooper, S; Huntley, S J; Crump, R; Lovatt, F; Green, L E

2016-03-01

The aims of this study were to estimate the incidence rate of clinical mastitis (IRCM) and identify risk factors for clinical mastitis in suckler ewes to generate hypotheses for future study. A postal questionnaire was sent to 999 randomly selected English sheep farmers in 2010 to gather data on farmer reported IRCM and flock management practices for the calendar year 2009, of which 329 provided usable information. The mean IRCM per flock was 1.2/100 ewes/year (CI:1.10:1.35). The IRCM was 2.0, 0.9 and 1.3/100 ewes/year for flocks that lambed indoors, outdoors and a combination of both, respectively. Farmers ran a variety of managements before, during and after lambing that were not comparable within one model, therefore six mixed effects over-dispersed Poisson regression models were developed. Factors significantly associated with increased IRCM were increasing percentage of the flock with poor udder conformation, increasing mean number of lambs reared/ewe and when some or all ewes lambed in barns compared with outdoors (Model 1). For ewes housed in barns before lambing (Model 2), concrete, earth and other materials were associated with an increase in IRCM compared with hardcore floors (an aggregate of broken bricks and stones). For ewes in barns during lambing (Model 3), an increase in IRCM was associated with concrete compared with hardcore flooring and where bedding was stored covered outdoors or in a building compared with bedding stored outdoors uncovered. For ewes in barns after lambing (Model 4), increased IRCM was associated with earth compared with hardcore floors, and when fresh bedding was added once per week compared with at a frequency of ≤2 days or twice/week. The IRCM was lower for flocks where some or all ewes remained in the same fields before, during and after lambing compared with flocks that did not (Model 5). Where ewes and lambs were turned outdoors after lambing (Model 6), the IRCM increased as the age of the oldest lambs at turnout

Reformulation of Crop and Management Factor in ANSWERS Model

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Yayat Hidayat

2008-05-01

Full Text Available Crop and management factor value is significantly corelated with outputs of ANSWERS model especially on soil erosion. Using daily crop and management factors (daily C factors, the ANSWERS model performs well in predicting soil erosion which is showed by determination coeffient (R2 = 0.89, model efficiency (0.86, and average of percentage model deviations (24.1%. Whereas using USLE C factor (2 cropping systems, predicted is much higher than measured soil erosion (over estimate. Output of the model is not statisfy; it is represented by model coefficient (0.40 and average of percentage model deviations (63.6%.

Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow.

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Bai, Lu; Chan, Ching-Yao; Liu, Pan; Xu, Chengcheng

2017-10-03

Electric bikes (e-bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e-bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid-block bike lane. The types of 2-wheeled vehicles in the study included bicycle-style electric bicycles (BSEBs), scooter-style electric bicycles (SSEBs), and regular bicycles (RBs). Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2-wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear-end conflicts between e-bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid-block bike lanes. The frequency of rear-end conflicts between e-bikes and bikes increased with an increase in the operating speeds of e-bikes and the volume of e-bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e-bikes and bikes increased the frequency of rear-end conflicts between e-bikes and bikes in mid-block bike lanes. A 1% increase in the average operating speed of e-bikes would increase the expected number of rear-end conflicts between e-bikes and bikes by 1.48%. A 1% increase in the speed difference between e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%. The conflict frequency in mid-block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear-end conflicts included the operating speeds of e-bikes, the speed difference between e-bikes and regular bikes, the volume of e-bikes, the volume of bikes, and the width of bike lanes. The

Orthopedic Surgery among Patients with Rheumatoid Arthritis: A Population-based study to Identify Risk factors, Sex differences, and Time trends.

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Richter, Michael; Crowson, Cynthia S; Matteson, Eric L; Makol, Ashima

2017-12-20

To identify risk factors for large joint (LJS) versus small joint surgery (SJS) in rheumatoid arthritis (RA) and evaluate trends in surgery rates over time. A retrospective medical record review was performed of all orthopedic surgeries following first fulfillment of 1987 ACR criteria for adult-onset RA among residents of Olmsted County, Minnesota, USA in 1980-2013. Risk factors were examined using Cox models adjusted for age, sex and calendar year of RA incidence. Trends in incidence of joint surgeries were examined using Poisson regression models. A total of 1077 patients with RA (mean age 56 years, 69% female, 66% seropositive) were followed for a median of 10.7 years during which 112 (90 women) underwent at least one SJS and 204 (141 women) underwent at least one LJS. Risk factors included advanced age, rheumatoid factor and anti-CCP antibody positivity for both SJS and LJS, and BMI≥30 kg/m 2 for LJS. Risk factors for SJS and LJS at any time during follow-up included the presence of radiographic erosions, large joint swelling, and methotrexate use. SJS rates decreased by calendar year of incidence (hazard ratio 0.53; p=0.001), with significant decline in SJS after 1995. The cumulative incidence of SJS was higher in women than men (p=0.008). In recent years, there has been a significant decline in rates of SJS but not LJS in patients with RA. The incidence of SJS is higher among women. Traditional RA risk factors are strong predictors for SJS and LJS. Increasing age and obesity are predictive of LJS. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Model of separated form factors for unilamellar vesicles

International Nuclear Information System (INIS)

Kiselev, M.A.; Aksenov, V.L.; Lesieur, P.; Lombardo, D.; Kiselev, A.M.

2001-01-01

A new model of separated form factors is proposed for the evaluation of small-angle neutron scattering curves from large unilamellar vesicles. The validity of the model was checked via comparison with the model of a hollow sphere. The model of separated form factors and the hollow sphere model give a reasonable agreement in the evaluation of vesicle parameters

PENGUJIAN FAMA-FRENCH THREE-FACTOR MODEL DI INDONESIA

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Damar Hardianto

2017-03-01

Full Text Available This study empirically examined the Fama-French three factor model of stock returnsfor Indonesia over the period 2000-2004. We found evidence for pervasive market, size, andbook-to-market factors in Indonesian stock returns. We found that cross-sectional mean returnswere explained by exposures to these three factors, and not by the market factor alone. Theempirical results were reasonably consistent with the Fama-French three factor model.

Transcription factor expression uniquely identifies most postembryonic neuronal lineages in the Drosophila thoracic central nervous system.

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Lacin, Haluk; Zhu, Yi; Wilson, Beth A; Skeath, James B

2014-03-01

Most neurons of the adult Drosophila ventral nerve cord arise from a burst of neurogenesis during the third larval instar stage. Most of this growth occurs in thoracic neuromeres, which contain 25 individually identifiable postembryonic neuronal lineages. Initially, each lineage consists of two hemilineages--'A' (Notch(On)) and 'B' (Notch(Off))--that exhibit distinct axonal trajectories or fates. No reliable method presently exists to identify these lineages or hemilineages unambiguously other than labor-intensive lineage-tracing methods. By combining mosaic analysis with a repressible cell marker (MARCM) analysis with gene expression studies, we constructed a gene expression map that enables the rapid, unambiguous identification of 23 of the 25 postembryonic lineages based on the expression of 15 transcription factors. Pilot genetic studies reveal that these transcription factors regulate the specification and differentiation of postembryonic neurons: for example, Nkx6 is necessary and sufficient to direct axonal pathway selection in lineage 3. The gene expression map thus provides a descriptive foundation for the genetic and molecular dissection of adult-specific neurogenesis and identifies many transcription factors that are likely to regulate the development and differentiation of discrete subsets of postembryonic neurons.

Identify High-Quality Protein Structural Models by Enhanced K-Means.

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Wu, Hongjie; Li, Haiou; Jiang, Min; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K -means clustering ( SK -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that SK -means and K -means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK -means and K -means++ demonstrated substantial improvements relative to results from SPICKER and classical K -means.

Factor Copula Models for Replicated Spatial Data

KAUST Repository

Krupskii, Pavel

2016-12-19

We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

Factor Copula Models for Replicated Spatial Data

KAUST Repository

Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

2016-01-01

We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

Comparison of two model approaches in the Zambezi river basin with regard to model reliability and identifiability

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H. C. Winsemius

2006-01-01

Full Text Available Variations of water stocks in the upper Zambezi river basin have been determined by 2 different hydrological modelling approaches. The purpose was to provide preliminary terrestrial storage estimates in the upper Zambezi, which will be compared with estimates derived from the Gravity Recovery And Climate Experiment (GRACE in a future study. The first modelling approach is GIS-based, distributed and conceptual (STREAM. The second approach uses Lumped Elementary Watersheds identified and modelled conceptually (LEW. The STREAM model structure has been assessed using GLUE (Generalized Likelihood Uncertainty Estimation a posteriori to determine parameter identifiability. The LEW approach could, in addition, be tested for model structure, because computational efforts of LEW are low. Both models are threshold models, where the non-linear behaviour of the Zambezi river basin is explained by a combination of thresholds and linear reservoirs. The models were forced by time series of gauged and interpolated rainfall. Where available, runoff station data was used to calibrate the models. Ungauged watersheds were generally given the same parameter sets as their neighbouring calibrated watersheds. It appeared that the LEW model structure could be improved by applying GLUE iteratively. Eventually, it led to better identifiability of parameters and consequently a better model structure than the STREAM model. Hence, the final model structure obtained better represents the true hydrology. After calibration, both models show a comparable efficiency in representing discharge. However the LEW model shows a far greater storage amplitude than the STREAM model. This emphasizes the storage uncertainty related to hydrological modelling in data-scarce environments such as the Zambezi river basin. It underlines the need and potential for independent observations of terrestrial storage to enhance our understanding and modelling capacity of the hydrological processes. GRACE

Risk factors identified for certain lymphoma subtypes

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In a large international collaborative analysis of risk factors for non-Hodgkin lymphoma (NHL), scientists were able to quantify risk associated with medical history, lifestyle factors, family history of blood or lymph-borne cancers, and occupation for 11

Linking demand and supply factors in identifying cultural ecosystem services of urban green infrastructures

DEFF Research Database (Denmark)

Hegetschweiler, K. Tessa; de Vries, Sjerp; Arnberger, Arne

2017-01-01

and supply factors together. The aim was to provide an overview of this highly interdisciplinary research, to describe how these linkages are being made and to identify which factors significantly influence dependent variables such as levels of use, activities or health and well-being benefits. Commonly used......Urban green infrastructure provides a number of cultural ecosystem services that are greatly appreciated by the public. In order to benefit from these services, actual contact with the respective ecosystem is often required. Furthermore, the type of services offered depend on the physical...... characteristics of the ecosystem. We conducted a review of publications dealing with demand or social factors such as user needs, preferences and values as well as spatially explicit supply or physical factors such as amount of green space, (bio)diversity, recreational infrastructure, etc. and linking demand...

Assessment of slip factor models at off-design condition

International Nuclear Information System (INIS)

Yoon, Sung Ho; Baek, Je Hyun

2000-01-01

Slip factor is defined as an empirical factor being multiplied to theoretical energy transfer for the estimation of real work input of a centrifugal compressor. Researchers have tried to develop a simple empirical model, for a century, to predict a slip factor. However most these models were developed on the condition of design point assuming inviscid flow. So these models often fail to predict a correct slip factor at off-design condition. In this study, we summarized various slip factor models and compared these models with experimental and numerical data at off-design condition. As a result of this study, Wiesner's and Paeng and Chung's models are applicable for radial impeller, but all the models are not suitable for backswept impeller. Finally, the essential avenues for future study is discussed

Dietary patterns as identified by factor analysis and colorectal cancer among middle-aged Americans.

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Flood, Andrew; Rastogi, Tanuja; Wirfält, Elisabet; Mitrou, Panagiota N; Reedy, Jill; Subar, Amy F; Kipnis, Victor; Mouw, Traci; Hollenbeck, Albert R; Leitzmann, Michael; Schatzkin, Arthur

2008-07-01

Although diet has long been suspected as an etiological factor for colorectal cancer, studies of single foods and nutrients have provided inconsistent results. We used factor analysis methods to study associations between dietary patterns and colorectal cancer in middle-aged Americans. Diet was assessed among 293,615 men and 198,767 women in the National Institutes of Health-AARP Diet and Health Study. Principal components factor analysis identified 3 primary dietary patterns: a fruit and vegetables, a diet foods, and a red meat and potatoes pattern. State cancer registries identified 2151 incident cases of colorectal cancer in men and 959 in women between 1995 and 2000. Men with high scores on the fruit and vegetable pattern were at decreased risk [relative risk (RR) for quintile (Q) 5 versus Q1: 0.81; 95% CI: 0.70, 0.93; P for trend = 0.004]. Both men and women had a similar risk reduction with high scores on the diet food factor: men (RR: 0.82; 95% CI: 0.72, 0.94; P for trend = 0.001) and women (RR: 0.87; 95% CI: 0.71, 1.07; P for trend = 0.06). High scores on the red meat factor were associated with increased risk: men (RR: 1.17; 95% CI: 1.02, 1.35; P for trend = 0.14) and women (RR: 1.48; 95% CI: 1.20, 1.83; P for trend = 0.0002). These results suggest that dietary patterns characterized by a low frequency of meat and potato consumption and frequent consumption of fruit and vegetables and fat-reduced foods are consistent with a decreased risk of colorectal cancer.

Modelling human factor with Petri nets

International Nuclear Information System (INIS)

Bedreaga, Luminita; Constantinescu, Cristina; Guzun, Basarab

2007-01-01

The human contribution to risk and safety of nuclear power plant operation can be best understood, assessed and quantified using tools to evaluate human reliability. Human reliability analysis becomes an important part of every probabilistic safety assessment and it is used to demonstrate that nuclear power plants designed with different safety levels are prepared to cope with severe accidents. Human reliability analysis in context of probabilistic safety assessment consists in: identifying human-system interactions important to safety; quantifying probabilities appropriate with these interactions. Nowadays, the complex system functions can be modelled using special techniques centred either on states space adequate to system or on events appropriate to the system. Knowing that complex system model consists in evaluating the likelihood of success, in other words, in evaluating the possible value for that system being in some state, the inductive methods which are based on the system states can be applied also for human reliability modelling. Thus, switching to the system states taking into account the human interactions, the underlying basis of the Petri nets can be successfully applied and the likelihoods appropriate to these states can also derived. The paper presents the manner to assess the human reliability quantification using Petri nets approach. The example processed in the paper is from human reliability documentation without a detailed human factor analysis (qualitative). The obtained results by these two kinds of methods are in good agreement. (authors)

Structural Equation Model for Evaluating Factors Affecting Quality of Social Infrastructure Projects

Directory of Open Access Journals (Sweden)

Shahid Hussain

2018-05-01

Full Text Available The quality of the constructed social infrastructure project has been considered a necessary measure for the sustainability of projects. Studies on factors affecting project quality have used various techniques and methods to explain the relationships between particular variables. Unexpectedly, Structural Equation Modeling (SEM has acquired very little concern in factors affecting project quality studies. To address this limitation in the body of knowledge, the objective of this study was to apply the SEM approach and build a model that explained and identified the critical factors affecting quality in social infrastructure projects. The authors developed a quantitative approach using smart-PLS version 3.2.7. This study shed light on the views of different experts based on their experience in public construction projects in Pakistan. Particularly, the authors aimed to find out the relationships between construction, stakeholders, materials, design, and external factors, and how these relate to project quality. The findings of this study revealed that the R2 value of the model was scored at 0.749, which meant that the five exogenous latent constructs collectively explained 74.9% of the variance in project quality. The Goodness-of-Fit of the model was 0.458. The construction related factor was the most important out of the five constructs. This study determined that better planning and monitoring and evaluation should be developed to better address and control the quality defects by decision-makers, project managers as well as contractors. These findings might support practitioners and decision makers to focus on quality related problems that might occur in their current or future projects.

Critical success factors model developing for sustainable Kaizen implementation in manufactur-ing industry in Ethiopia

OpenAIRE

Haftu Hailu; Abdelkadir Kedir; Getachew Bassa; Kassu Jilcha

2017-01-01

The purpose of the research is to identify critical success factors and model developing for sustaining kaizen implementation. Peacock shoe is one of the manufacturing industries in Ethiopia facing challenges on sustaining. The methodology followed is factor analysis and empirically testing hypothesis. A database was designed using SPSS version 20. The survey was validated using statistical validation using the Cronbach alpha index; the result is 0.908. The KMO index value was obtained for th...

Using association rule mining to identify risk factors for early childhood caries.

Science.gov (United States)

Ivančević, Vladimir; Tušek, Ivan; Tušek, Jasmina; Knežević, Marko; Elheshk, Salaheddin; Luković, Ivan

2015-11-01

Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Bačka area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A conceptual model of factors contributing to the development of muscle dysmorphia.

Science.gov (United States)

Grieve, Frederick G

2007-01-01

Muscle dysmorphia is a recently described subcategory of Body Dysmorphic Disorder. It is most prevalent in males and has a number of cognitive, behavioral, socioenviornmental, emotional, and psychological factors that influence its expression. An etiological model describing these influences is presented for evaluation. Nine variables (body mass, media influences, ideal body internalization, low self-esteem, body dissatisfaction, health locus of control, negative affect, perfectionism, and body distortion) were identified through the use of extant literature on muscle dysmorphia and through extrapolation from literature involving women and eating disorders. The functional relationships among these variables are described and implications of the model are discussed.

Incorporation of human factors into ship collision risk models focusing on human centred design aspects

International Nuclear Information System (INIS)

Sotiralis, P.; Ventikos, N.P.; Hamann, R.; Golyshev, P.; Teixeira, A.P.

2016-01-01

This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks. - Highlights: • A collision risk model for the incorporation of human factors into quantitative risk analysis is proposed. • The model takes into account the human performance in different operational conditions leading to the collision. • The most important contributors to human performance and ship collision are identified. • The model developed is applied to assess the collision risk of a feeder operating in Dover strait.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

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Warren D Anderson

2017-07-01

Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients.

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Shayna Stein

2018-01-01

Full Text Available Human primary glioblastomas (GBM often harbor mutations within the epidermal growth factor receptor (EGFR. Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. However, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynamics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules currently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from proliferative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other cancer types.

Identifying context factors explaining physician's low performance in communication assessment: an explorative study in general practice.

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Essers, Geurt; van Dulmen, Sandra; van Weel, Chris; van der Vleuten, Cees; Kramer, Anneke

2011-12-13

Communication is a key competence for health care professionals. Analysis of registrar and GP communication performance in daily practice, however, suggests a suboptimal application of communication skills. The influence of context factors could reveal why communication performance levels, on average, do not appear adequate. The context of daily practice may require different skills or specific ways of handling these skills, whereas communication skills are mostly treated as generic. So far no empirical analysis of the context has been made. Our aim was to identify context factors that could be related to GP communication. A purposive sample of real-life videotaped GP consultations was analyzed (N = 17). As a frame of reference we chose the MAAS-Global, a widely used assessment instrument for medical communication. By inductive reasoning, we analyzed the GP behaviour in the consultation leading to poor item scores on the MAAS-Global. In these cases we looked for the presence of an intervening context factor, and how this might explain the actual GP communication behaviour. We reached saturation after having viewed 17 consultations. We identified 19 context factors that could potentially explain the deviation from generic recommendations on communication skills. These context factors can be categorized into doctor-related, patient-related, and consultation-related factors. Several context factors seem to influence doctor-patient communication, requiring the GP to apply communication skills differently from recommendations on communication. From this study we conclude that there is a need to explicitly account for context factors in the assessment of GP (and GP registrar) communication performance. The next step is to validate our findings.

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control.

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Drop, Frank M; Pool, Daan M; van Paassen, Marinus Rene M; Mulder, Max; Bulthoff, Heinrich H

2018-01-01

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

Exploring Environmental Factors in Nursing Workplaces That Promote Psychological Resilience: Constructing a Unified Theoretical Model

OpenAIRE

Cusack, Lynette; Smith, Morgan; Hegney, Desley; Rees, Clare S.; Breen, Lauren J.; Witt, Regina R.; Rogers, Cath; Williams, Allison; Cross, Wendy; Cheung, Kin

2016-01-01

Building nurses' resilience to complex and stressful practice environments is necessary to keep skilled nurses in the workplace and ensuring safe patient care. A unified theoretical framework titled Health Services Workplace Environmental Resilience Model (HSWERM), is presented to explain the environmental factors in the workplace that promote nurses' resilience. The framework builds on a previously-published theoretical model of individual resilience, which identified the key constructs of p...

Assessing Reliability of Cellulose Hydrolysis Models to Support Biofuel Process Design – Identifiability and Uncertainty Analysis

DEFF Research Database (Denmark)

Sin, Gürkan; Meyer, Anne S.; Gernaey, Krist

2010-01-01

The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done in the ori......The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done...

Variability in connectivity patterns of fish with ontogenetic migrations: Modelling effects of abiotic and biotic factors

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Susanne Eva Tanner

2015-10-01

Full Text Available Connectivity is a critical property of marine fish populations as it drives population replenishment, determines colonization patterns and the resilience of populations to harvest. Understanding connectivity patterns is particularly important in species that present ontogenetic migrations and segregated habitat use during their life history, such as marine species with estuarine nursery areas. Albeit challenging, fish movement can be estimated and quantified using different methodologies depending on the life history stages of interest (e.g. biophysical modelling, otolith chemistry, genetic markers. Relative contributions from estuarine nursery areas to the adult coastal populations were determined using otolith elemental composition and maximum likelihood estimation for four commercially important species (Dicentrarchus labrax, Plathichtys flesus, Solea senegalensis and Solea solea and showed high interannual variability. Here, the effects of abiotic and biotic factors on the observed variability in connectivity rates and extent between estuarine juvenile and coastal adult subpopulations are investigated using generalized linear models (GLM and generalized mixed models (GMM. Abiotic factors impacting both larval and juvenile life history stages are included in the models (e.g. wind force and direction, NAO, water temperature while biotic factors relative to the estuarine residency of juvenile fish are evaluated (e.g. juvenile density, food availability. Factors contributing most to the observed variability in connectivity rates are singled out and compared among species. General trends are identified and results area discussed in the general context of identifying potential management frameworks applicable to different life stages and which may prove useful for ontogenetically migrating species.

Testing alternative factor models of PTSD and the robustness of the dysphoria factor.

Science.gov (United States)

Elklit, Ask; Armour, Cherie; Shevlin, Mark

2010-01-01

This study first aimed to examine the structure of self-reported posttraumatic stress disorder (PTSD) symptoms using three different samples. The second aim of the paper was to test the robustness of the factor analytic model when depression scores were controlled for. Based on previous factor analytic findings and the DSM-IV formulation, six confirmatory factor models were specified and estimated that reflected different symptom clusters. The best fitting model was subsequently re-fitted to the data after including a depression variable. The analyses were based on responses from 973 participants across three samples. Sample 1 consisted of 633 parents who were members of 'The National Association of Infant Death' and who had lost a child. Sample 2 consisted of 227 victims of rape, who completed a questionnaire within 4 weeks of the rape. Each respondent had been in contact with the Centre for Rape Victims (CRV) at the Aarhus University Hospital, Denmark. Sample 3 consisted of 113 refugees resident in Denmark. All participants had been referred to a treatment centre which focused on rehabilitating refugees through treatment for psychosocial integration problems (RRCF: Rehabliterings og Revliderings Centre for Flygtninge). In total 500 participants received a diagnosis of PTSD/sub-clinical PTSD (Sample 1, N=214; 2, N=176; 3, N=110). A correlated four-factor model with re-experiencing, avoidance, dysphoria, and arousal factors provided the best fit to the sample data. The average attenuation in the factor loadings was highest for the dysphoria factor (M=-.26, SD=.11) compared to the re-experiencing (M=-.14, SD=.18), avoidance (M=-.10, SD=.21), and arousal (M=-.09, SD=.13) factors. With regards to the best fitting factor model these results concur with previous research findings using different trauma populations but do not reflect the current DSM-IV symptom groupings. The attenuation of dysphoria factor loadings suggests that dysphoria is a non-specific component of

The asset pricing model of musharakah factors

Science.gov (United States)

Simon, Shahril; Omar, Mohd; Lazam, Norazliani Md

2015-02-01

The existing three-factor model developed by Fama and French for conventional investment was formulated based on risk-free rates element in which contradict with Shariah principles. We note that the underlying principles that govern Shariah investment were mutual risk and profit sharing between parties, the assurance of fairness for all and that transactions were based on an underlying asset. In addition, the three-factor model did not exclude stock that was not permissible by Shariah such as financial services based on riba (interest), gambling operator, manufacture or sale of non-halal products or related products and other activities deemed non-permissible according to Shariah. Our approach to construct the factor model for Shariah investment was based on the basic tenets of musharakah in tabulating the factors. We start by noting that Islamic stocks with similar characteristics should have similar returns and risks. This similarity between Islamic stocks was defined by the similarity of musharakah attributes such as business, management, profitability and capital. These attributes define factor exposures (or betas) to factors. The main takeaways were that musharakah attributes we chose had explain stock returns well in cross section and were significant in different market environments. The management factor seemed to be responsible for the general dynamics of the explanatory power.

Modeling the assessment of the economic factors impact on the development of social entrepreneurship

Science.gov (United States)

Absalyamov, T.; Kundakchyan, R.; Zulfakarova, L.; Zapparova, Z.

2017-12-01

The article deals with the research of modern trends in the development of social entrepreneurship in Russia. The results of the research allow the authors to identify a system of factors that affect the development of entrepreneurship in the modern Russian economy. Moreover, the authors argue the regional specificity of the development of social entrepreneurship. The paper considers specific features and formulates the main limitations of the development of entrepreneurship and the competitive environment in the social sphere. The authors suggest an econometric model for assessing the influence of economic factors on the development of socially-oriented entrepreneurship and present an algorithm for calculating its components. The results of the econometric analysis identify the main factors of the change in the performance indicators of entrepreneurial activity and determine the degree of their impact on social entrepreneurship. The results and conclusions can serve as an estimation of the socioeconomic consequences of the sustainability disruption of the entrepreneurial potential realization in the social sphere.

Local acceptance of wind energy: Factors of success identified in French and German case studies

International Nuclear Information System (INIS)

Jobert, Arthur; Laborgne, Pia; Mimler, Solveig

2007-01-01

The objective of this paper is to identify and analyse factors that are important for winning acceptance of wind-energy parks on the local level. The developers of wind-energy parks need to know how to manage 'social acceptance' at the different stages of planning, realisation and operation. Five case studies in France and Germany focused on factors of success in developing a wind-energy project on a given site and illuminated how policy frameworks influence local acceptance. Our hypothesis is that these factors fall into two categories: institutional conditions, such as economic incentives and regulations; and site-specific conditions (territorial factors), such as the local economy, the local geography, local actors, and the actual on-site planning process (project management)

Identification of major factors in Australian primary care pharmacists' practice environment that have a bearing on the implementation of professional models of practice.

Science.gov (United States)

Jackson, John K; Hussainy, Safeera Y; Kirkpatrick, Carl M J

2017-08-01

Objective The aim of the present study was to describe an environmental framework for pharmacists in primary care in Australia and determine the major factors within that environment that have the greatest bearing on their capacity to implement patient-focused models of professional practice. Methods A draft framework for pharmacists' practice was developed by allocating structures, systems and related factors known to the researchers or identified from the literature as existing within pharmacists' internal, operational and external environments to one of five domains: Social, Technological, Economic, Environmental or Political [STEEP]. Focus groups of pharmacists used an adapted nominal group technique to assess the draft and add factors where necessary. Where applicable, factors were consolidated into groups to establish a revised framework. The three major factors or groups in each domain were identified. The results were compared with the enabling factors described in the profession's vision statement. Results Seventy-eight individual factors were ultimately identified, with 86% able to be grouped. The three dominant groups in each of the five domains that had a bearing on the implementation of professional models of practice were as follows: (1) Social: the education of pharmacists, their beliefs and the capacity of the pharmacist workforce; (2) Technological: current and future practice models, technology and workplace structures; (3) Economic: funding of services, the viability of practice and operation of the Pharmaceutical Benefits Scheme; (4) Environmental: attitudes and expectations of stakeholders, including consumers, health system reform and external competition; and (5) Political: regulation of practice, representation of the profession and policies affecting practice. Conclusions The three dominant groups of factors in each of the five STEEP environmental domains, which have a bearing on pharmacists' capacity to implement patient-focused models of

A Global Interactome Map of the Dengue Virus NS1 Identifies Virus Restriction and Dependency Host Factors

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Mohamed Lamine Hafirassou

2017-12-01

Full Text Available Dengue virus (DENV infections cause the most prevalent mosquito-borne viral disease worldwide, for which no therapies are available. DENV encodes seven non-structural (NS proteins that co-assemble and recruit poorly characterized host factors to form the DENV replication complex essential for viral infection. Here, we provide a global proteomic analysis of the human host factors that interact with the DENV NS1 protein. Combined with a functional RNAi screen, this study reveals a comprehensive network of host cellular processes involved in DENV infection and identifies DENV host restriction and dependency factors. We highlight an important role of RACK1 and the chaperonin TRiC (CCT and oligosaccharyltransferase (OST complexes during DENV replication. We further show that the OST complex mediates NS1 and NS4B glycosylation, and pharmacological inhibition of its N-glycosylation function strongly impairs DENV infection. In conclusion, our study provides a global interactome of the DENV NS1 and identifies host factors targetable for antiviral therapies.

An extended technology acceptance model for detecting influencing factors: An empirical investigation

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Mohamd Hakkak

2013-11-01

Full Text Available The rapid diffusion of the Internet has radically changed the delivery channels applied by the financial services industry. The aim of this study is to identify the influencing factors that encourage customers to adopt online banking in Khorramabad. The research constructs are developed based on the technology acceptance model (TAM and incorporates some extra important control variables. The model is empirically verified to study the factors influencing the online banking adoption behavior of 210 customers of Tejarat Banks in Khorramabad. The findings of the study suggest that the quality of the internet connection, the awareness of online banking and its benefits, the social influence and computer self-efficacy have significant impacts on the perceived usefulness (PU and perceived ease of use (PEOU of online banking acceptance. Trust and resistance to change also have significant impact on the attitude towards the likelihood of adopting online banking.

Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

DEFF Research Database (Denmark)

Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna

2015-01-01

Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines...... based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted...... for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies....

Resident and Facility Factors Associated With the Incidence of Urinary Tract Infections Identified in the Nursing Home Minimum Data Set.

Science.gov (United States)

Castle, Nicholas; Engberg, John B; Wagner, Laura M; Handler, Steven

2017-02-01

This research examined resident and facility-specific factors associated with a diagnosis of a urinary tract infection (UTI) in the nursing home setting. Minimum Data Set and Online Survey, Certification and Reporting system data were used to identify all nursing home residents in the United States on April 1, 2006, who did not have a UTI ( n = 1,138,418). Residents were followed until they contracted a UTI (9.5%), died (8.3%), left the nursing home (33.2%), or the year ended (49.0%). A Cox proportional hazards model was estimated, controlling for resident and facility characteristics and for the state of residence. The presence of an indwelling catheter was the primary predictor of whether a resident contracted a UTI (adjusted incidence ratio = 3.35, p factors such as percentage of Medicaid residents, for-profit, and chain status was less significant. Estimates regarding staffing levels indicate that increased contact hours with more highly educated nursing staff are associated with less catheter use. Several facility-specific risk factors are of significance. Of significance, UTIs may be reduced by modifying factors such as staffing levels.

Interpersonal success factors for strategy implementation: a case study using group model building

OpenAIRE

Rodney J Scott; Robert Y Cavana; Donald Cameron

2015-01-01

Strategy implementation has been identified as an area of system dynamics literature requiring greater attention. Most strategies fail to be implemented successfully, and processes for effectively implementing strategy are yet to be fully explained and explored. The reported interpersonal success factors for strategy implementation are reported outcomes for group model building, suggesting potential applicability. A case study using validated survey methods yielded promising results, and sugg...

Random T-DNA mutagenesis identifies a Cu-Zn-superoxide dismutase gene as a virulence factor of Sclerotinia sclerotiorum

Science.gov (United States)

Agrobacterium-mediated transformation (AMT) was used to identify potential virulence factors in Sclerotinia sclerotiorum. Screening AMT transformants identified two mutants showing significantly reduced virulence. The mutants showed similar growth rate, colony morphology, and sclerotial and oxalate ...

Hierarchical and coupling model of factors influencing vessel traffic flow.

Science.gov (United States)

Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Critical success factors model developing for sustainable Kaizen implementation in manufactur-ing industry in Ethiopia

Directory of Open Access Journals (Sweden)

Haftu Hailu

2017-12-01

Full Text Available The purpose of the research is to identify critical success factors and model developing for sustaining kaizen implementation. Peacock shoe is one of the manufacturing industries in Ethiopia facing challenges on sustaining. The methodology followed is factor analysis and empirically testing hypothesis. A database was designed using SPSS version 20. The survey was validated using statistical validation using the Cronbach alpha index; the result is 0.908. The KMO index value was obtained for the 32 items and had a value of 0.642 with Bartlett's Test of Sphericity Approx. Chi-Square 4503.007, degree of freedom 496 and significance value 0.000. A factor analysis by principal components and varimax rotation was applied for finding the critical success factors. Finding designates that 32 items were merged into eight critical success factors. All the eight factors together explain for 76.941 % of the variance. Multiple regression model analysis has indicated that some of the critical success factors had relationship with success indicators. Due to constraint of time, the researcher focused only at peacock shoe manufacturing industry. Other limitation also includes the absence of any local research that shows the critical success factors at the moment.

Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

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Nurhayati Ai

2018-01-01

Full Text Available Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread questionnaires to consumer, then from those questionnaires we identified 16 variables that needs to be considered on selecting antivirus software. This 16 variables then divided into 5 factors by using factor analysis method in SPSS software. These five factors are security, performance, internal, time and capacity. To rank those factors we spread questionnaires to 6 IT expert then the data is analyzed using AHP method. The result is that performance factors gained the highest rank from all of the other factors. Thus, consumer can select antivirus software by judging the variables in the performance factors. Those variables are software loading speed, user friendly, no excessive memory use, thorough scanning, and scanning virus fast and accurately.

Identifying the effects of parameter uncertainty on the reliability of modeling the stability of overhanging, multi-layered, river banks

Science.gov (United States)

Samadi, A.; Amiri-Tokaldany, E.; Davoudi, M. H.; Darby, S. E.

2011-11-01

Composite river banks consist of a basal layer of non-cohesive material overlain by a cohesive layer of fine-grained material. In such banks, fluvial erosion of the lower, non-cohesive, layer typically occurs at a much higher rate than erosion of the upper part of the bank. Consequently, such banks normally develop a cantilevered bank profile, with bank retreat of the upper part of the bank taking place predominantly by the failure of these cantilevers. To predict the undesirable impacts of this type of bank retreat, a number of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of resisting and driving forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the overhanging block geometry, and the geotechnical properties of the bank materials. In this paper, we introduce a new bank stability relation (for shear-type cantilever failures) that considers the hydrological status of cantilevered riverbanks, while beam-type failures are analyzed using a previously proposed relation. We employ these stability models to evaluate the effects of parameter uncertainty on the reliability of riverbank stability modeling of overhanging banks. This is achieved by employing a simple model of overhanging failure with respect to shear and beam failure mechanisms in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. The results show that care is required in parameterising (i) the geometrical shape of the overhanging-block and (ii) the bank material cohesion and unit weight, as predictions of bank stability are sensitive to variations of these factors.

How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior.

Science.gov (United States)

de Oña, Juan; de Oña, Rocio; Eboli, Laura; Forciniti, Carmen; Mazzulla, Gabriella

2014-12-01

Road crashes can be caused by different factors, including infrastructure, vehicles, and human variables. Many research studies have focused solely on identifying the key factors that cause road crashes. From these studies, it emerged that human factors have the most relevant impact on accident severity. More specifically, accident severity depends on several factors related directly to the driver, i.e., driving experience, driver's socio-economic characteristics, and driving behavior and attitudes. In this paper, we investigate driver behaviors and attitudes while driving and specifically focus on different methods for identifying the factors that most affect the driver's perception of accident risk. To this end, we designed and conducted a survey in two different European contexts: the city of Cosenza, which is located in the south of Italy, and the city of Granada, which is located in the south of Spain. Samples of drivers were contacted for their opinions on certain aspects of driving rules and attitudes while driving, and different types of questions were addressed to the drivers to assess their judgments of these aspects. Consequently, different methods of data analysis were applied to determine the aspects that heavily influence driver perception of accident risk. An experiment based on the stated preferences (SP) was carried out with the drivers, and the SP data were analyzed using an ordered probit (OP) model. Interesting findings emerged from different analyses of the data and from the comparisons among the data collected in the two different territorial contexts. We found that both Italian and Spanish drivers consider driving in an altered psychophysical state and violating the overtaking rules to be the most risky behaviors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Air quality models and unusually large ozone increases: Identifying model failures, understanding environmental causes, and improving modeled chemistry

Science.gov (United States)

Couzo, Evan A.

Several factors combine to make ozone (O3) pollution in Houston, Texas, unique when compared to other metropolitan areas. These include complex meteorology, intense clustering of industrial activity, and significant precursor emissions from the heavily urbanized eight-county area. Decades of air pollution research have borne out two different causes, or conceptual models, of O 3 formation. One conceptual model describes a gradual region-wide increase in O3 concentrations "typical" of many large U.S. cities. The other conceptual model links episodic emissions of volatile organic compounds to spatially limited plumes of high O3, which lead to large hourly increases that have exceeded 100 parts per billion (ppb) per hour. These large hourly increases are known to lead to violations of the federal O 3 standard and impact Houston's status as a non-attainment area. There is a need to further understand and characterize the causes of peak O 3 levels in Houston and simulate them correctly so that environmental regulators can find the most cost-effective pollution controls. This work provides a detailed understanding of unusually large O 3 increases in the natural and modeled environments. First, we probe regulatory model simulations and assess their ability to reproduce the observed phenomenon. As configured for the purpose of demonstrating future attainment of the O3 standard, the model fails to predict the spatially limited O3 plumes observed in Houston. Second, we combine ambient meteorological and pollutant measurement data to identify the most likely geographic origins and preconditions of the concentrated O3 plumes. We find evidence that the O3 plumes are the result of photochemical activity accelerated by industrial emissions. And, third, we implement changes to the modeled chemistry to add missing formation mechanisms of nitrous acid, which is an important radical precursor. Radicals control the chemical reactivity of atmospheric systems, and perturbations to

Identifiability and error minimization of receptor model parameters with PET

International Nuclear Information System (INIS)

Delforge, J.; Syrota, A.; Mazoyer, B.M.

1989-01-01

The identifiability problem and the general framework for experimental design optimization are presented. The methodology is applied to the problem of the receptor-ligand model parameter estimation with dynamic positron emission tomography data. The first attempts to identify the model parameters from data obtained with a single tracer injection led to disappointing numerical results. The possibility of improving parameter estimation using a new experimental design combining an injection of the labelled ligand and an injection of the cold ligand (displacement experiment) has been investigated. However, this second protocol led to two very different numerical solutions and it was necessary to demonstrate which solution was biologically valid. This has been possible by using a third protocol including both a displacement and a co-injection experiment. (authors). 16 refs.; 14 figs

Modeling the Factors Associated with Children's Mental Health Difficulties in Primary School: A Multilevel Study

Science.gov (United States)

Humphrey, Neil; Wigelsworth, Michael

2012-01-01

The current study explores some of the factors associated with children's mental health difficulties in primary school. Multilevel modeling with data from 628 children from 36 schools was used to determine how much variation in mental health difficulties exists between and within schools, and to identify characteristics at the school and…

Size, Value and Business Cycle Variables. The Three-Factor Model and Future Economic Growth: Evidence from an Emerging Market

Directory of Open Access Journals (Sweden)

Fahad Ali

2018-02-01

Full Text Available The paper empirically investigates three different methods to construct factors and identifies some pitfalls that arise in the application of Fama-French’s three-factor model to the Pakistani stock returns. We find that the special features in Pakistan significantly affect size and value factors and also influence the explanatory power of the three-factor model. Additionally, the paper examines the ability of the three factors to predict the future growth of Pakistan’s economy. Using monthly data of both financial and non-financial companies between 2002 and 2016, the article empirically investigates and finds that: (1 size and book-to-market factors exist in the Pakistani stock market, two mimic portfolios SMB and HML generate a return of 9.15% and 12.27% per annum, respectively; (2 adding SMB and HML factors into the model meaningfully increases the explanatory power of the model; and (3 the model’s factors, except for value factor, predict future gross domestic product (GDP growth of Pakistan and remain robust. Our results are robust across sub-periods, risk regimes, and under three different methods of constructing the factors.

Identifying environmental risk factors and mapping the risk of human West Nile virus in South Dakota.

Science.gov (United States)

Hess, A.; Davis, J. K.; Wimberly, M. C.

2017-12-01

Human West Nile virus (WNV) first arrived in the USA in 1999 and has since then spread across the country. Today, the highest incidence rates are found in the state of South Dakota. The disease occurrence depends on the complex interaction between the mosquito vector, the bird host and the dead-end human host. Understanding the spatial domain of this interaction and being able to identify disease transmission hotspots is crucial for effective disease prevention and mosquito control. In this study we use geospatial environmental information to understand what drives the spatial distribution of cases of human West Nile virus in South Dakota and to map relative infection risk across the state. To map the risk of human West Nile virus in South Dakota, we used geocoded human case data from the years 2004-2016. Satellite data from the Landsat ETM+ and MODIS for the years 2003 to 2016 were used to characterize environmental patterns. From these datasets we calculated indices, such as the normalized differenced vegetation index (NDVI) and the normalized differenced water index (NDWI). In addition, datasets such as the National Land Data Assimilation System (NLDAS), National Land Cover Dataset (NLCD), National Wetland inventory (NWI), National Elevation Dataset (NED) and Soil Survey Geographic Database (SSURGO) were utilized. Environmental variables were summarized for a buffer zone around the case and control points. We used a boosted regression tree model to identify the most important variables describing the risk of WNV infection. We generated a risk map by applying this model across the entire state. We found that the highest relative risk is present in the James River valley in northeastern South Dakota. Factors that were identified as influencing the transmission risk include inter-annual variability of vegetation cover, water availability and temperature. Land covers such as grasslands, low developed areas and wetlands were also found to be good predictors for human

Verification of the model of predisposition in triathlon – structural model of confirmative factor analysis

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Lenka Kovářová

2012-09-01

Full Text Available BACKGROUND: The triathlon is a combination of three different types of sport – swimming, cycling, and running. Each of these requires different top level predispositions and complex approach to talent selection is a rather difficult process. Attempts to identify assumptions in the triathlon have so far been specific and focused only on some groups of predispositions (physiology, motor tests, and psychology. The latest studies missed the structural approach and were based on determinants of sport performance, theory of sports training and expert assessment. OBJECTIVE: The aim of our study was to verify the model of predisposition in the short triathlon for talent assessment of young male athletes age 17–20 years. METHODS: The research sample consisted of 55 top level triathletes – men, who were included in the Government supported sports talent programme in the Czech Republic at the age of 17–20 years. We used a confirmative factor analysis (FA and Path diagram to verify the model, which allow us to explain mutual relationships among observed variables. For statistical data processing we used a structure equating modeling (SEM by software Lisrel L88. RESULTS: The study confirms best structural model for talent selection in triathlon at the age of 17–20 years old men, which composed seventeen indicators (tests and explained 91% of all cross-correlations (Goodness of Fit Index /GFI/ 0.91, Root Mean Square Residual /RMSR/ 0.13. Tests for predispositions in triathlons were grouped into five items, three motor predispositions (swimming, cycling and running skills, aerobic and psychological predispositions. Aerobic predispositions showed the highest importance to the assumptions to the general factor (1.00; 0. Running predispositions were measured as a very significant factor (–0.85; 0.28 which confirms importance of this critical stage of the race. Lower factor weight showed clusters of swimming (–0.61; 0.63 and cycling (0.53; 0

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

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Lu Xinghua

2006-02-01

Full Text Available Abstract Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

Factors affecting strategic plan implementation using interpretive structural modeling (ISM).

Science.gov (United States)

Bahadori, Mohammadkarim; Teymourzadeh, Ehsan; Tajik, Hamidreza; Ravangard, Ramin; Raadabadi, Mehdi; Hosseini, Seyed Mojtaba

2018-06-11

Purpose Strategic planning is the best tool for managers seeking an informed presence and participation in the market without surrendering to changes. Strategic planning enables managers to achieve their organizational goals and objectives. Hospital goals, such as improving service quality and increasing patient satisfaction cannot be achieved if agreed strategies are not implemented. The purpose of this paper is to investigate the factors affecting strategic plan implementation in one teaching hospital using interpretive structural modeling (ISM). Design/methodology/approach The authors used a descriptive study involving experts and senior managers; 16 were selected as the study sample using a purposive sampling method. Data were collected using a questionnaire designed and prepared based on previous studies. Data were analyzed using ISM. Findings Five main factors affected strategic plan implementation. Although all five variables and factors are top level, "senior manager awareness and participation in the strategic planning process" and "creating and maintaining team participation in the strategic planning process" had maximum drive power. "Organizational structure effects on the strategic planning process" and "Organizational culture effects on the strategic planning process" had maximum dependence power. Practical implications Identifying factors affecting strategic plan implementation is a basis for healthcare quality improvement by analyzing the relationship among factors and overcoming the barriers. Originality/value The authors used ISM to analyze the relationship between factors affecting strategic plan implementation.

Resident Workflow and Psychiatric Emergency Consultation: Identifying Factors for Quality Improvement in a Training Environment.

Science.gov (United States)

Blair, Thomas; Wiener, Zev; Seroussi, Ariel; Tang, Lingqi; O'Hora, Jennifer; Cheung, Erick

2017-06-01

Quality improvement to optimize workflow has the potential to mitigate resident burnout and enhance patient care. This study applied mixed methods to identify factors that enhance or impede workflow for residents performing emergency psychiatric consultations. The study population consisted of all psychiatry program residents (55 eligible, 42 participating) at the Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles. The authors developed a survey through iterative piloting, surveyed all residents, and then conducted a focus group. The survey included elements hypothesized to enhance or impede workflow, and measures pertaining to self-rated efficiency and stress. Distributional and bivariate analyses were performed. Survey findings were clarified in focus group discussion. This study identified several factors subjectively associated with enhanced or impeded workflow, including difficulty with documentation, the value of personal organization systems, and struggles to communicate with patients' families. Implications for resident education are discussed.

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach.

Science.gov (United States)

Jordanous, Anna; Keller, Bill

2016-01-01

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Dynamic Multi-Factor Credit Risk Model with Fat-Tailed Factors

Czech Academy of Sciences Publication Activity Database

Gapko, Petr; Šmíd, Martin

2012-01-01

Roč. 62, č. 2 (2012), s. 125-140 ISSN 0015-1920 R&D Projects: GA ČR GD402/09/H045; GA ČR GA402/09/0965 Grant - others:Univerzita Karlova(CZ) GAUK 46108 Institutional research plan: CEZ:AV0Z10750506 Keywords : credit risk * probability of default * loss given default * credit loss * credit loss distribution * Basel II Subject RIV: AH - Economics Impact factor: 0.340, year: 2012 http://library.utia.cas.cz/separaty/2012/E/smid-dynamic multi-factor credit risk model with fat-tailed factors.pdf

Hierarchical and coupling model of factors influencing vessel traffic flow.

Directory of Open Access Journals (Sweden)

Zhao Liu

Full Text Available Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Ebola Virus Infection Modelling and Identifiability Problems

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Van-Kinh eNguyen

2015-04-01

Full Text Available The recent outbreaks of Ebola virus (EBOV infections have underlined the impact of the virus as a major threat for human health. Due to the high biosafety classification of EBOV (level 4, basic research is very limited. Therefore, the development of new avenues of thinking to advance quantitative comprehension of the virus and its interaction with the host cells is urgently neededto tackle this lethal disease. Mathematical modelling of the EBOV dynamics can be instrumental to interpret Ebola infection kinetics on quantitative grounds. To the best of our knowledge, a mathematical modelling approach to unravel the interaction between EBOV and the host cells isstill missing. In this paper, a mathematical model based on differential equations is used to represent the basic interactions between EBOV and wild-type Vero cells in vitro. Parameter sets that represent infectivity of pathogens are estimated for EBOV infection and compared with influenza virus infection kinetics. The average infecting time of wild-type Vero cells in EBOV is slower than in influenza infection. Simulation results suggest that the slow infecting time of EBOV could be compensated by its efficient replication. This study reveals several identifiability problems and what kind of experiments are necessary to advance the quantification of EBOV infection. A first mathematical approach of EBOV dynamics and the estimation of standard parametersin viral infections kinetics is the key contribution of this work, paving the way for future modelling work on EBOV infection.

An alternative method for centrifugal compressor loading factor modelling

Science.gov (United States)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Chromosome 17 alterations identify good-risk and poor-risk tumors independently of clinical factors in medulloblastoma

Science.gov (United States)

McCabe, Martin G.; Bäcklund, L. Magnus; Leong, Hui Sun; Ichimura, Koichi; Collins, V. Peter

2011-01-01

Current risk stratification schemas for medulloblastoma, based on combinations of clinical variables and histotype, fail to accurately identify particularly good- and poor-risk tumors. Attempts have been made to improve discriminatory power by combining clinical variables with cytogenetic data. We report here a pooled analysis of all previous reports of chromosomal copy number related to survival data in medulloblastoma. We collated data from previous reports that explicitly quoted survival data and chromosomal copy number in medulloblastoma. We analyzed the relative prognostic significance of currently used clinical risk stratifiers and the chromosomal aberrations previously reported to correlate with survival. In the pooled dataset metastatic disease, incomplete tumor resection and severe anaplasia were associated with poor outcome, while young age at presentation was not prognostically significant. Of the chromosomal variables studied, isolated 17p loss and gain of 1q correlated with poor survival. Gain of 17q without associated loss of 17p showed a trend to improved outcome. The most commonly reported alteration, isodicentric chromosome 17, was not prognostically significant. Sequential multivariate models identified isolated 17p loss, isolated 17q gain, and 1q gain as independent prognostic factors. In a historical dataset, we have identified isolated 17p loss as a marker of poor outcome and 17q gain as a novel putative marker of good prognosis. Biological markers of poor-risk and good-risk tumors will be critical in stratifying treatment in future trials. Our findings should be prospectively validated independently in future clinical studies. PMID:21292688

Modeling Success: Using Preenrollment Data to Identify Academically At-Risk Students

Science.gov (United States)

Gansemer-Topf, Ann M.; Compton, Jonathan; Wohlgemuth, Darin; Forbes, Greg; Ralston, Ekaterina

2015-01-01

Improving student success and degree completion is one of the core principles of strategic enrollment management. To address this principle, institutional data were used to develop a statistical model to identify academically at-risk students. The model employs multiple linear regression techniques to predict students at risk of earning below a…

Selection of asset investment models by hospitals: examination of influencing factors, using Switzerland as an example.

Science.gov (United States)

Eicher, Bernhard

2016-10-01

Hospitals are responsible for a remarkable part of the annual increase in healthcare expenditure. This article examines one of the major cost drivers, the expenditure for investment in hospital assets. The study, conducted in Switzerland, identifies factors that influence hospitals' investment decisions. A suggestion on how to categorize asset investment models is presented based on the life cycle of an asset, and its influencing factors defined based on transaction cost economics. The influence of five factors (human asset specificity, physical asset specificity, uncertainty, bargaining power, and privacy of ownership) on the selection of an asset investment model is examined using a two-step fuzzy-set Qualitative Comparative Analysis. The research shows that outsourcing-oriented asset investment models are particularly favored in the presence of two combinations of influencing factors: First, if technological uncertainty is high and both human asset specificity and bargaining power of a hospital are low. Second, if assets are very specific, technological uncertainty is high and there is a private hospital with low bargaining power, outsourcing-oriented asset investment models are favored too. Using Qualitative Comparative Analysis, it can be demonstrated that investment decisions of hospitals do not depend on isolated influencing factors but on a combination of factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Identifying Critical Factors in the Eco-Efficiency of Remanufacturing Based on the Fuzzy DEMATEL Method

Directory of Open Access Journals (Sweden)

Qianwang Deng

2015-11-01

Full Text Available Remanufacturing can bring considerable economic and environmental benefits such as cost saving, conservation of energy and resources, and reduction of emissions. With the increasing awareness of sustainable manufacturing, remanufacturing gradually becomes the research priority. Most studies concentrate on the analysis of influencing factors, or the evaluation of the economic and environmental performance in remanufacturing, while little effort has been devoted to investigating the critical factors influencing the eco-efficiency of remanufacturing. Considering the current development of the remanufacturing industry in China, this paper proposes a set of factors influencing the eco-efficiency of remanufacturing and then utilizes a fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL method to establish relation matrixes reflecting the interdependent relationships among these factors. Finally, the contributions of each factor to eco-efficiency and mutual influence values among them are obtained, and critical factors in eco-efficiency of remanufacturing are identified. The results of the present work can provide theoretical supports for the government to make appropriate policies to improve the eco-efficiency of remanufacturing.

STUDY OF IDENTIFYING AND PRIORITIZING THE AFFECTING FACTORS ON BANK BRAND CUSTOMER LOYALTY

OpenAIRE

Zahra Aliyari; Yosef Beygzadeh

2017-01-01

Today, customer loyalty is the key to business success. By increased customers’ loyalty, market share and profitability level of enterprises will rise. Market perception along with planning and adopting appropriate strategies for making customers loyal and enhancing their rate of loyalty leads to long-term benefits for the enterprises. Given the importance of the issue, the goal of this study was to identify and prioritize the factors affecting loyalty to a banking brand from perspective of K...

Systemic Thinking and Requisite Holism in Mastering Logistics Risks: the Model for Identifying Risks in Organisations and Supply Chain

OpenAIRE

Borut Jereb; Teodora Ivanuša; Bojan Rosi

2013-01-01

Risks in logistic processes represent one of the major issues in supply chain management nowadays. Every organization strives for success, and uninterrupted operations are the key factors in achieving this goal, which cannot be achieved without efficient risk management. In the scope of supply chain risk research, we identified some key issues in the field, the major issue being the lack of standardization and models, which can make risk management in an organization easier and more efficient...

Genome-wide CRISPR/Cas9 Screen Identifies Host Factors Essential for Influenza Virus Replication

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Julianna Han

2018-04-01

Full Text Available Summary: The emergence of influenza A viruses (IAVs from zoonotic reservoirs poses a great threat to human health. As seasonal vaccines are ineffective against zoonotic strains, and newly transmitted viruses can quickly acquire drug resistance, there remains a need for host-directed therapeutics against IAVs. Here, we performed a genome-scale CRISPR/Cas9 knockout screen in human lung epithelial cells with a human isolate of an avian H5N1 strain. Several genes involved in sialic acid biosynthesis and related glycosylation pathways were highly enriched post-H5N1 selection, including SLC35A1, a sialic acid transporter essential for IAV receptor expression and thus viral entry. Importantly, we have identified capicua (CIC as a negative regulator of cell-intrinsic immunity, as loss of CIC resulted in heightened antiviral responses and restricted replication of multiple viruses. Therefore, our study demonstrates that the CRISPR/Cas9 system can be utilized for the discovery of host factors critical for the replication of intracellular pathogens. : Using a genome-wide CRISPR/Cas9 screen, Han et al. demonstrate that the major hit, the sialic acid transporter SLC35A1, is an essential host factor for IAV entry. In addition, they identify the DNA-binding transcriptional repressor CIC as a negative regulator of cell-intrinsic immunity. Keywords: CRISPR/Cas9 screen, GeCKO, influenza virus, host factors, sialic acid pathway, SLC35A1, Capicua, CIC, cell-intrinsic immunity, H5N1

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

Science.gov (United States)

Urbain, Jay

2015-12-01

We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results. Copyright © 2015 Elsevier Inc. All rights reserved.

Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.

Science.gov (United States)

Parodi, Stefano; Dosi, Corrado; Zambon, Antonella; Ferrari, Enrico; Muselli, Marco

2017-12-01

Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.

Drosophila Cancer Models Identify Functional Differences between Ret Fusions.

Science.gov (United States)

Levinson, Sarah; Cagan, Ross L

2016-09-13

We generated and compared Drosophila models of RET fusions CCDC6-RET and NCOA4-RET. Both RET fusions directed cells to migrate, delaminate, and undergo EMT, and both resulted in lethality when broadly expressed. In all phenotypes examined, NCOA4-RET was more severe than CCDC6-RET, mirroring their effects on patients. A functional screen against the Drosophila kinome and a library of cancer drugs found that CCDC6-RET and NCOA4-RET acted through different signaling networks and displayed distinct drug sensitivities. Combining data from the kinome and drug screens identified the WEE1 inhibitor AZD1775 plus the multi-kinase inhibitor sorafenib as a synergistic drug combination that is specific for NCOA4-RET. Our work emphasizes the importance of identifying and tailoring a patient's treatment to their specific RET fusion isoform and identifies a multi-targeted therapy that may prove effective against tumors containing the NCOA4-RET fusion. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

Science.gov (United States)

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

A Parametric Factor Model of the Term Structure of Mortality

DEFF Research Database (Denmark)

Haldrup, Niels; Rosenskjold, Carsten Paysen T.

The prototypical Lee-Carter mortality model is characterized by a single common time factor that loads differently across age groups. In this paper we propose a factor model for the term structure of mortality where multiple factors are designed to influence the age groups differently via...... on the loading functions, the factors are not designed to be orthogonal but can be dependent and can possibly cointegrate when the factors have unit roots. We suggest two estimation procedures similar to the estimation of the dynamic Nelson-Siegel term structure model. First, a two-step nonlinear least squares...... procedure based on cross-section regressions together with a separate model to estimate the dynamics of the factors. Second, we suggest a fully specified model estimated by maximum likelihood via the Kalman filter recursions after the model is put on state space form. We demonstrate the methodology for US...

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

Science.gov (United States)

Francis, Andrew; Moulton, Vincent

2018-06-07

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors.

Science.gov (United States)

Borzouei, Shiva; Soltanian, Ali Reza

2018-01-01

To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model. This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps. Variables found to be significant at a level of pneural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM. In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Extracting the Evaluations of Stereotypes: Bi-factor Model of the Stereotype Content Structure

OpenAIRE

Sayans-Jiménez, Pablo; Cuadrado, Isabel; Rojas, Antonio J.; Barrada, Juan R.

2017-01-01

Stereotype dimensions—competence, morality and sociability—are fundamental to studying the perception of other groups. These dimensions have shown moderate/high positive correlations with each other that do not reflect the theoretical expectations. The explanation for this (e.g., halo effect) undervalues the utility of the shared variance identified. In contrast, in this work we propose that this common variance could represent the global evaluation of the perceived group. Bi-factor models ar...

Identifying sources of atmospheric fine particles in Havana City using Positive Matrix Factorization technique

International Nuclear Information System (INIS)

Pinnera, I.; Perez, G.; Ramos, M.; Guibert, R.; Aldape, F.; Flores M, J.; Martinez, M.; Molina, E.; Fernandez, A.

2011-01-01

In previous study a set of samples of fine and coarse airborne particulate matter collected in a urban area of Havana City were analyzed by Particle-Induced X-ray Emission (PIXE) technique. The concentrations of 14 elements (S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br and Pb) were consistently determined in both particle sizes. The analytical database provided by PIXE was statistically analyzed in order to determine the local pollution sources. The Positive Matrix Factorization (PMF) technique was applied to fine particle data in order to identify possible pollution sources. These sources were further verified by enrichment factor (EF) calculation. A general discussion about these results is presented in this work. (Author)

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

Science.gov (United States)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input

Factors Affecting Retention Behavior: A Model To Predict At-Risk Students. AIR 1997 Annual Forum Paper.

Science.gov (United States)

Sadler, William E.; Cohen, Frederic L.; Kockesen, Levent

This paper describes a methodology used in an on-going retention study at New York University (NYU) to identify a series of easily measured factors affecting student departure decisions. Three logistic regression models for predicting student retention were developed, each containing data available at three distinct times during the first…

Study of Factors Preventing Children from Enrolment in Primary School in the Republic of Honduras: Analysis Using Structural Equation Modelling

Science.gov (United States)

Ashida, Akemi

2015-01-01

Studies have investigated factors that impede enrolment in Honduras. However, they have not analysed individual factors as a whole or identified the relationships among them. This study used longitudinal data for 1971 children who entered primary schools from 1986 to 2000, and employed structural equation modelling to examine the factors…

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.

Science.gov (United States)

Grandjean, Thomas R B; Chappell, Michael J; Yates, James W T; Evans, Neil D

2014-05-01

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available. Copyright © 2013. Published by Elsevier Ireland Ltd.

Systemic Thinking and Requisite Holism in Mastering Logistics Risks: the Model for Identifying Risks in Organisations and Supply Chain

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Bojan Rosi

2013-02-01

Full Text Available Risks in logistic processes represent one of the major issues in supply chain management nowadays. Every organization strives for success, and uninterrupted operations are the key factors in achieving this goal, which cannot be achieved without efficient risk management. In the scope of supply chain risk research, we identified some key issues in the field, the major issue being the lack of standardization and models, which can make risk management in an organization easier and more efficient. Consequently, we developed a model, which captures and identifies risks in an organization and its supply chain. It is in accordance with the general risk management standard – ISO 31000, and incorporates some relevant recent findings from general and supply chain risk management, especially from the viewpoint of public segmentation. This experimental catalogue (which is also published online can serve as a checklist and a starting point of supply chain risk management in organizations. Its main idea is cooperation between experts from the area in order to compile an ever-growing list of possible risks and to provide an insight in the model and its value in practice, for which reason input and opinions of anyone who uses our model are greatly appreciated and included in the catalogue.

Condensation in models with factorized and pair-factorized stationary states

International Nuclear Information System (INIS)

Evans, M R; Waclaw, B

2015-01-01

Non-equilibrium real-space condensation is a phenomenon in which a finite fraction of some conserved quantity (mass, particles, etc) becomes spatially localized. We review two popular stochastic models of hopping particles that lead to condensation and whose stationary states assume a factorized form: the zero-range process and the misanthrope process, and their various generalizations. We also introduce a new model—a misanthrope process with parallel dynamics—that exhibits condensation and has a pair-factorized stationary state

Transcriptome analysis of Neisseria meningitidis in human whole blood and mutagenesis studies identify virulence factors involved in blood survival.

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Hebert Echenique-Rivera

2011-05-01

Full Text Available During infection Neisseria meningitidis (Nm encounters multiple environments within the host, which makes rapid adaptation a crucial factor for meningococcal survival. Despite the importance of invasion into the bloodstream in the meningococcal disease process, little is known about how Nm adapts to permit survival and growth in blood. To address this, we performed a time-course transcriptome analysis using an ex vivo model of human whole blood infection. We observed that Nm alters the expression of ≈30% of ORFs of the genome and major dynamic changes were observed in the expression of transcriptional regulators, transport and binding proteins, energy metabolism, and surface-exposed virulence factors. In particular, we found that the gene encoding the regulator Fur, as well as all genes encoding iron uptake systems, were significantly up-regulated. Analysis of regulated genes encoding for surface-exposed proteins involved in Nm pathogenesis allowed us to better understand mechanisms used to circumvent host defenses. During blood infection, Nm activates genes encoding for the factor H binding proteins, fHbp and NspA, genes encoding for detoxifying enzymes such as SodC, Kat and AniA, as well as several less characterized surface-exposed proteins that might have a role in blood survival. Through mutagenesis studies of a subset of up-regulated genes we were able to identify new proteins important for survival in human blood and also to identify additional roles of previously known virulence factors in aiding survival in blood. Nm mutant strains lacking the genes encoding the hypothetical protein NMB1483 and the surface-exposed proteins NalP, Mip and NspA, the Fur regulator, the transferrin binding protein TbpB, and the L-lactate permease LctP were sensitive to killing by human blood. This increased knowledge of how Nm responds to adaptation in blood could also be helpful to develop diagnostic and therapeutic strategies to control the devastating

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model

International Nuclear Information System (INIS)

Chang, Y.H.J.; Mosleh, A.

2007-01-01

This is the second in a series of five papers describing the information, decision, and action in crew context (IDAC) model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, psychological, and physical activities during the course of an accident. This paper identifies the IDAC set of performance influencing factors (PIFs), providing their definitions and causal organization in the form of a modular influence diagram. Fifty PIFs are identified to support the IDAC model to be implemented in a computer simulation environment. They are classified into eleven hierarchically structured groups. The PIFs within each group are independent to each other; however, dependencies may exist between PIFs within different groups. The supporting evidence for the selection and organization of the influence paths based on psychological literature, observations, and various human reliability analysis methodologies is also indicated

Identifying factors which enhance capacity to engage in clinical education among podiatry practitioners: an action research project.

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Abey, Sally; Lea, Susan; Callaghan, Lynne; Shaw, Steve; Cotton, Debbie

2015-01-01

Health profession students develop practical skills whilst integrating theory with practice in a real world environment as an important component of their training. Research in the area of practice placements has identified challenges and barriers to the delivery of effective placement learning. However, there has been little research in podiatry and the question of which factors impact upon clinical educators' capacity to engage with the role remains an under-researched area. This paper presents the second phase of an action research project designed to determine the factors that impact upon clinical educators' capacity to engage with the mentorship role. An online survey was developed and podiatry clinical educators recruited through National Health Service (NHS) Trusts. The survey included socio-demographic items, and questions relating to the factors identified as possible variables influencing clinical educator capacity; the latter was assessed using the 'Clinical Educator Capacity to Engage' scale (CECE). Descriptive statistics were used to explore demographic data whilst the relationship between the CECE and socio-demographic factors were examined using inferential statistics in relation to academic profile, career profile and organisation of the placement. The survey response rate was 42 % (n = 66). Multiple linear regression identified four independent variables which explain a significant proportion of the variability of the dependent variable, 'capacity to engage with clinical education', with an adjusted R2 of 0.428. The four variables were: protected mentorship time, clinical educator relationship with university, sign-off responsibility, and volunteer status. The identification of factors that impact upon clinical educators' capacity to engage in mentoring of students has relevance for strategic planning and policy-making with the emphasis upon capacity-building at an individual level, so that the key attitudes and characteristics that are linked

Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach

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Johnson, Matthew J.; Hatten, James R.; Holmes, Jennifer A.; Shafroth, Patrick B.

2017-01-01

The western population of the yellow-billed cuckoo (Coccyzus americanus) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat

Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model

International Nuclear Information System (INIS)

Zhou, Jian; Qi, Jinyi

2014-01-01

A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D time-of-flight PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon’s ray-tracer, we propose another more simplified geometrical projector based on the Bresenham’s ray-tracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a non-factored model, while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve the optimal reconstruction performance based on a sparse factorization model with an image domain resolution model. (paper)

Bayes Factor Covariance Testing in Item Response Models.

Science.gov (United States)

Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

2017-12-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

Machine learning models identify molecules active against the Ebola virus in vitro [version 3; referees: 2 approved

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Sean Ekins

2017-01-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

Machine learning models identify molecules active against the Ebola virus in vitro [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Sean Ekins

2015-10-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Sean Ekins

2016-01-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

Food hygiene practices and its associated factors among model and non model households in Abobo district, southwestern Ethiopia: Comparative cross-sectional study.

Science.gov (United States)

Okugn, Akoma; Woldeyohannes, Demelash

2018-01-01

In developing country most of human infectious diseases are caused by eating contaminated food. Estimated nine out ten of the diarrheal disease is attributable to the environment and associated with risk factors of poor food hygiene practice. Understanding the risk of eating unsafe food is the major concern to prevent and control food borne diseases. The main goal of this study was to assessing food hygiene practices and its associated factors among model and non model households at Abobo district. This study was conducted from 18 October 2013 to 13 June 2014. A community-based comparative cross-sectional study design was used. Pretested structured questionnaire was used to collect data. A total of 1247 households (417 model and 830 non model households) were included in the study from Abobo district. Bivariate and multivariate logistic regression analysis was used to identify factors associated with outcome variable. The study revealed that good food hygiene practice was 51%, of which 79% were model and 36.70% were non model households. Type of household [AOR: 2.07, 95% CI: (1.32-3.39)], sex of household head [AOR: 1.63, 95% CI: (1.06-2.48)], Availability of liquid wastes disposal pit [AOR: 2.23, 95% CI: (1.39,3.63)], Knowledge of liquid waste to cause diseases [AOR: 1.95, 95% (1.23,3.08)], and availability of functional hand washing facility [AOR: 3.61, 95% CI: (1.86-7.02)] were the factors associated with food handling practices. This study revealed that good food handling practice is low among model and non model households. While type of household (model versus non model households), sex, knowledge of solid waste to cause diseases, availability of functional hand washing facility, and availability of liquid wastes disposal pit were the factors associated with outcome variable. Health extension workers should play a great role in educating households regarding food hygiene practices to improve their knowledge and practices of the food hygiene.

Identifying Variability in Mental Models Within and Between Disciplines Caring for the Cardiac Surgical Patient.

Science.gov (United States)

Brown, Evans K H; Harder, Kathleen A; Apostolidou, Ioanna; Wahr, Joyce A; Shook, Douglas C; Farivar, R Saeid; Perry, Tjorvi E; Konia, Mojca R

2017-07-01

The cardiac operating room is a complex environment requiring efficient and effective communication between multiple disciplines. The objectives of this study were to identify and rank critical time points during the perioperative care of cardiac surgical patients, and to assess variability in responses, as a correlate of a shared mental model, regarding the importance of these time points between and within disciplines. Using Delphi technique methodology, panelists from 3 institutions were tasked with developing a list of critical time points, which were subsequently assigned to pause point (PP) categories. Panelists then rated these PPs on a 100-point visual analog scale. Descriptive statistics were expressed as percentages, medians, and interquartile ranges (IQRs). We defined low response variability between panelists as an IQR ≤ 20, moderate response variability as an IQR > 20 and ≤ 40, and high response variability as an IQR > 40. Panelists identified a total of 12 PPs. The PPs identified by the highest number of panelists were (1) before surgical incision, (2) before aortic cannulation, (3) before cardiopulmonary bypass (CPB) initiation, (4) before CPB separation, and (5) at time of transfer of care from operating room (OR) to intensive care unit (ICU) staff. There was low variability among panelists' ratings of the PP "before surgical incision," moderate response variability for the PPs "before separation from CPB," "before transfer from OR table to bed," and "at time of transfer of care from OR to ICU staff," and high response variability for the remaining 8 PPs. In addition, the perceived importance of each of these PPs varies between disciplines and between institutions. Cardiac surgical providers recognize distinct critical time points during cardiac surgery. However, there is a high degree of variability within and between disciplines as to the importance of these times, suggesting an absence of a shared mental model among disciplines caring for

Developing a Causal Model of Human and Organizational Culture Factors Affecting the Knowledge Management Maturity Using Meta-Synthesis Approach

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Younis Jabarzadeh

2016-03-01

Full Text Available Identifying influential factors which contribute to the knowledge management maturity and studying their interaction over time helps managers to understand the complex behavior of knowledge management system. It also leads them to make right decisions for utilizing these factors in promoting knowledge management and achieve strategic goals of the organization by providing a sound insight and an appropriate mechanism to reach to the optimal maturity level. In this study, all aspects and components of knowledge management with an emphasis on human factors and organizational culture, and relations between them have been identified by using a systematic literature review and meta-synthesis qualitative research approach. Then by using consultation and consensus of experts, all results verified. The results include 64 codes which are classified in 9 dimensions and two categories. Finally, due to the obtained classification and their relations, the dynamic model of knowledge management maturity is presented. The results of this study could be a suitable framework for improving mental models of knowledge management executives and experts. It makes possible Developing dynamic analysis models and appropriate policies in order to improve the knowledge management maturity in organizations.

Identifying Contextual and Emotional Factors to Explore Weight Disparities between Obese Black and White Women

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NiCole R. Keith

2016-01-01

Full Text Available Background Obese black women enrolled in weight loss interventions experience 50% less weight reduction than obese white women. This suggests that current weight loss strategies may increase health disparities. Objective We evaluated the feasibility of identifying daily contextual factors that may influence obesity. Methods In-home interviews with 16 obese (body mass index ≥ 30 black and white urban poor women were performed. For 14 days, ecological momentary assessment (EMA was used to capture emotion and social interactions every other day, and day reconstruction method surveys were used the following day to reconstruct the context of the prior day's EMA. Results Factors included percentage of participants without weight scales (43.8% or fitness equipment (68.8% in the home and exposed to food at work (55.6%. The most frequently reported location, activity, and emotion were home (19.4 ± 8.53, working (7.1 ± 8.80, and happy (6.9 ± 10.03, respectively. Conclusion Identifying individual contexts may lead to valuable insights about obesogenic behaviors and new interventions to improve weight management.

A 3-factor model for the FACIT-Sp.

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Canada, Andrea L; Murphy, Patricia E; Fitchett, George; Peterman, Amy H; Schover, Leslie R

2008-09-01

The 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-being Scale (FACIT-Sp) is a popular measure of the religious/spiritual (R/S) components of quality of life (QoL) in patients with cancer. The original factor analyses of the FACIT-Sp supported two factors: Meaning/Peace and Faith. Because Meaning suggests a cognitive aspect of R/S and Peace an affective component, we hypothesized a 3-factor solution: Meaning, Peace, and Faith. Participants were 240 long-term female survivors of cancer who completed the FACIT-Sp, the SF-12, and the BSI 18. We used confirmatory factor analysis to compare the 2- and 3-factor models of the FACIT-Sp and subsequently assessed associations between the resulting solutions and QoL domains. Survivors averaged 44 years of age and 10 years post-diagnosis. A 3-factor solution of the FACIT-Sp significantly improved the fit of the model to the data over the original 2-factor structure (Delta chi(2)=72.36, df=2, p<0.001). Further adjustments to the 3-factor model resulted in a final solution with even better goodness-of-fit indices (chi(2)=59.11, df=1, p=0.13, CFI=1.00, SMRM=0.05).The original Meaning/Peace factor controlling for Faith was associated with mental (r=0.63, p<0.000) and physical (r=0.22, p<0.01) health on the SF-12, and the original Faith factor controlling for Meaning/Peace was negatively associated with mental health (r=-0.15, p<0.05). The 3-factor model was more informative. Specifically, using partial correlations, the Peace factor was only related to mental health (r=0.53, p<0.001); Meaning was related to both physical (r=0.18, p<0.01) and mental (r=0.17, p<0.01) health; and Faith was negatively associated with mental health (r=-0.17, p<0.05). The results of this study support a 3-factor solution of the FACIT-Sp. The new solution not only represents a psychometric improvement over the original, but also enables a more detailed examination of the contribution of different dimensions of R/S to QoL. (c

Measurement Invariance and the Five-Factor Model of Personality: Asian International and Euro American Cultural Groups.

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Rollock, David; Lui, P Priscilla

2016-10-01

This study examined measurement invariance of the NEO Five-Factor Inventory (NEO-FFI), assessing the five-factor model (FFM) of personality among Euro American (N = 290) and Asian international (N = 301) students (47.8% women, Mage = 19.69 years). The full 60-item NEO-FFI data fit the expected five-factor structure for both groups using exploratory structural equation modeling, and achieved configural invariance. Only 37 items significantly loaded onto the FFM-theorized factors for both groups and demonstrated metric invariance. Threshold invariance was not supported with this reduced item set. Groups differed the most in the item-factor relationships for Extraversion and Agreeableness, as well as in response styles. Asian internationals were more likely to use midpoint responses than Euro Americans. While the FFM can characterize broad nomothetic patterns of personality traits, metric invariance with only the subset of NEO-FFI items identified limits direct group comparisons of correlation coefficients among personality domains and with other constructs, and of mean differences on personality domains. © The Author(s) 2015.

Modelling the critical success factors of agile software development projects in South Africa

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Tawanda B. Chiyangwa

2017-10-01

Full Text Available Background: The continued in failure of agile and traditional software development projects have led to the consideration, attention and dispute to critical success factors that are the aspects which are most vital to make a software engineering methodology fruitful. Although there is an increasing variety of critical success factors and methodologies, the conceptual frameworks which have causal relationship are limited. Objective: The objective of this study was to identify and provide insights into the critical success factors that influence the success of software development projects using agile methodologies in South Africa. Method: Quantitative method of collecting data was used. Data were collected in South Africa through a Web-based survey using structured questionnaires. Results: These results show that organisational factors have a great influence on performance expectancy characteristics. Conclusion: The results of this study discovered a comprehensive model that could provide guidelines to the agile community and to the agile professionals.

TEST OF THE FAMA-FRENCH THREE-FACTOR MODEL IN CROATIA

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Denis Dolinar

2013-06-01

Full Text Available This paper empirically examines the Fama-French three-factor model of stock returns for Croatia. In contrast to the results of Fama and French (1993 for the U.S. stock market, their three-factor model did not show so successful when describing risk-return relation of Croatian stocks. This paper shows that the Fama-French three-factor model is a valid pricing model, since it explains cross-section of average returns on stocks in Croatia, and that has a greater explanatory power in comparison to the CAPM. In the case of Croatian stock market, size and B/M factors are not always significant, but on average they individually have certain marginal explanatory power. Namely, they capture small common variation in returns that is missed by the market factor. Moreover, B/M factor has shown as a stronger common risk proxy in relation to size factor. Finally, there is still a large portion of common variation in stock return that may be explained by other factors. Because emerging capital markets bear their own specificity, special care needs to be taken when applying existing or developing new pricing models.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands.

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Ente J J Rood

Full Text Available Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

Application of Delphi method for determining the affecting factors upon audit risk model

Directory of Open Access Journals (Sweden)

Zohreh Hajiha

2012-01-01

Full Text Available The assessment of risks in an audit work could directly influence the costs, timing, and strategies as well as audit quality. The purpose of this paper is to identify the critical affecting factors on risks proposed in Audit Risk Model (ARM, in Iranian audit environment of Iran. In the present, the Delphi Method consists of 60 audit partners and managers is employed. The panel consists of two equally divided groups, one from audit organization, a governmental organization, and the other from private audit firms. We employ two rounds of Delphi and 58 critical risk factors extracted from auditing literature and Iranian auditing standards and present them to the experts. There are 43 factors categorized as important factors to assess the risks in ARM. The results are considerable in an Iranian audit environment, findings show the most important factors are in inherent risk factors. Finally, we made a comparison with a similar study in Taiwan. Differences indicate that in professional judgment issues like risk assessment, the consideration of particular culture and environment could help enhance the precision of assessments, especially in assessing control risk factors.

Dependent defaults and losses with factor copula models

Directory of Open Access Journals (Sweden)

Ackerer Damien

2017-12-01

Full Text Available We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with paircopula constructions, and nest many standard models as special cases. The loss distribution of a portfolio of contingent claims can be exactly and efficiently computed when individual losses are discretely supported on a finite grid. Numerical examples study the key features affecting the loss distribution and multi-name credit derivatives prices. An empirical exercise illustrates the flexibility of our approach by fitting credit index tranche prices.

Identifying socio-environmental factors that facilitate resilience among Canadian palliative family caregivers: a qualitative case study.

Science.gov (United States)

Giesbrecht, Melissa; Wolse, Faye; Crooks, Valorie A; Stajduhar, Kelli

2015-06-01

In Canada, friends and family members are becoming increasingly responsible for providing palliative care in the home. This is resulting in some caregivers experiencing high levels of stress and burden that may ultimately surpass their ability to cope. Recent palliative care research has demonstrated the potential for caregiver resilience within such contexts. This research, however, is primarily focused on exploring individual-level factors that contribute to resilience, minimizing the inherent complexity of this concept, and how it is simultaneously influenced by one's social context. Therefore, our study aims to identify socio-environmental factors that contribute to palliative family caregiver resilience in the Canadian homecare context. Drawing on ethnographic fieldnotes and semistructured interviews with family caregivers, care recipients, and homecare nurses, this secondary analysis employs an intersectionality lens and qualitative case study approach to identify socio-environmental factors that facilitate family caregivers' capacity for resilience. Following a case study methodology, two cases are purposely selected for analysis. Findings demonstrate that family caregiver resilience is influenced not only by individual-level factors but also by the social environment, which sets the lived context from which caregiving roles are experienced. Thematic findings of the two case studies revealed six socio-environmental factors that play a role in shaping resilience: access to social networks, education/knowledge/awareness, employment status, housing status, geographic location, and life-course stage. Findings contribute to existing research on caregiver resilience by empirically demonstrating the role of socio-environmental factors in caregiving experiences. Furthermore, utilizing an intersectional approach, these findings build on existing notions that resilience is a multidimensional and complex process influenced by numerous related variables that intersect

Study on visibility evaluation model which is considered field factors; Field factor wo koryoshita shininsei hyoka model ni kansuru kenkyu

Energy Technology Data Exchange (ETDEWEB)

Nakanishi, M; Hagiwara, T [Hokkaido University, Sapporo (Japan)

1997-10-01

The present study proposes a model to evaluate visual performance of road traffic facilities required for drivers. Two factors were employed to obtain the suitable contrast for drivers under driving situation. One factor is a suitable luminance range, which is derived from minimum required luminance and glare luminance. Another is a field. The model showed capability of providing visibility range in some cases. 8 refs., 4 figs., 2 tabs.

Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires

Energy Technology Data Exchange (ETDEWEB)

Yokelson, R. J.; Burling, I. R.; Gilman, J. B.; Warneke, C.; Stockwell, C. E.; de Gouw, J.; Akagi, S. K.; Urbanski, S. P.; Veres, P.; Roberts, J. M.; Kuster, W. C.; Reardon, J.; Griffith, D. W. T.; Johnson, T. J.; Hosseini, S.; Miller, J. W.; Cocker III, D. R.; Jung, H.; Weise, D. R.

2013-01-01

Vegetative fuels commonly consumed in prescribed fires were collected from five locations in the southeastern and southwestern U.S. and burned in a series of 77 fires at the U.S. Forest Service Fire Sciences Laboratory in Missoula, Montana. The particulate matter (PM2.5) emissions were measured by gravimetric filter sampling with subsequent analysis for elemental carbon (EC), organic carbon (OC), and 38 elements. The trace gas emissions were measured with a large suite of state-of-the-art instrumentation including an open-path Fourier transform infrared (OP FTIR) spectrometer, proton-transfer-reaction mass spectrometry (PTR-MS), proton-transfer ion-trap mass spectrometry (PIT-MS), negative-ion proton-transfer chemical-ionization mass spectrometry (NI-PT-CIMS), and gas chromatography with MS detection (GC-MS). 204 trace gas species (mostly non-methane organic compounds (NMOC)) were identified and quantified with the above instruments. An additional 152 significant peaks in the unit mass resolution mass spectra were quantified, but either could not be identified or most of the signal at that molecular mass was unaccounted for by identifiable species. As phase II of this study, we conducted airborne and ground-based sampling of the emissions from real prescribed fires mostly in the same land management units where the fuels for the lab fires were collected. A broad variety, but smaller number of species (21 trace gas species and PM2.5) was measured on 14 fires in chaparral and oak savanna in the southwestern US, as well as pine forest understory in the southeastern US and Sierra Nevada mountains of California. These extensive field measurements of emission factors (EF) for temperate biomass burning are useful both for modeling and to examine the representativeness of our lab fire EF. The lab/field EF ratio for the pine understory fuels was not statistically different from one, on average. However, our lab EF for “smoldering compounds” emitted by burning the semi

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

Science.gov (United States)

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Identifying Multiple Levels of Discussion-Based Teaching Strategies for Constructing Scientific Models

Science.gov (United States)

Williams, Grant; Clement, John

2015-01-01

This study sought to identify specific types of discussion-based strategies that two successful high school physics teachers using a model-based approach utilized in attempting to foster students' construction of explanatory models for scientific concepts. We found evidence that, in addition to previously documented dialogical strategies that…

IDENTIFYING ELEVEN FACTORS OF SERVICE MARKETING MIX (4PS) EFFECTIVE ON TENDENCY OF PATIENTS TOWARD PRIVATE HOSPITAL.

Science.gov (United States)

Hosseini, Seyed Mojtaba; Etesaminia, Samira; Jafari, Mehrnoosh

2016-10-01

One of the important factors of correct management is to identify the reasons for patient tendency toward private hospitals. This study measures these factors based on service marketing mixes. This study used a cross sectional descriptive methodology. The study was conducted during 6 months in 2015. The studied population included patients of private hospitals in Tehran. Random sampling was used (n = 200). Data was collected by an author-made questionnaire for service marketing factors. Reliability and validity of the questionnaire were confirmed. Data analysis was done using factor analysis test in SPSS 20. The results showed that constant attendance of physicians and nurses has the highest effect (0.707%) on patient tendency toward private hospitals.

Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants.

Science.gov (United States)

Bonetti, Debbie; Johnston, Marie; Clarkson, Jan E; Grimshaw, Jeremy; Pitts, Nigel B; Eccles, Martin; Steen, Nick; Thomas, Ruth; Maclennan, Graeme; Glidewell, Liz; Walker, Anne

2010-04-08

Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs) in Scotland. Outcomes were behavioural simulation (scenario decision-making), and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model, and knowledge (a non-theoretical construct). Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value. Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT), timeline acute (CS-SRM), and outcome expectancy (SCT) entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT) and attitude (TPB) entered the equation, together explaining 68% of the variance in intention. The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors that may predict clinical behaviour

Evaluation of bentonite alteration due to interactions with iron. Sensitivity analyses to identify the important factors for the bentonite alteration

International Nuclear Information System (INIS)

Sasamoto, Hiroshi; Wilson, James; Sato, Tsutomu

2013-01-01

Performance assessment of geological disposal systems for high-level radioactive waste requires a consideration of long-term systems behaviour. It is possible that the alteration of swelling clay present in bentonite buffers might have an impact on buffer functions. In the present study, iron (as a candidate overpack material)-bentonite (I-B) interactions were evaluated as the main buffer alteration scenario. Existing knowledge on alteration of bentonite during I-B interactions was first reviewed, then the evaluation methodology was developed considering modeling techniques previously used overseas. A conceptual model for smectite alteration during I-B interactions was produced. The following reactions and processes were selected: 1) release of Fe 2+ due to overpack corrosion; 2) diffusion of Fe 2+ in compacted bentonite; 3) sorption of Fe 2+ on smectite edge and ion exchange in interlayers; 4) dissolution of primary phases and formation of alteration products. Sensitivity analyses were performed to identify the most important factors for the alteration of bentonite by I-B interactions. (author)

Relating genes to function: identifying enriched transcription factors using the ENCODE ChIP-Seq significance tool.

Science.gov (United States)

Auerbach, Raymond K; Chen, Bin; Butte, Atul J

2013-08-01

Biological analysis has shifted from identifying genes and transcripts to mapping these genes and transcripts to biological functions. The ENCODE Project has generated hundreds of ChIP-Seq experiments spanning multiple transcription factors and cell lines for public use, but tools for a biomedical scientist to analyze these data are either non-existent or tailored to narrow biological questions. We present the ENCODE ChIP-Seq Significance Tool, a flexible web application leveraging public ENCODE data to identify enriched transcription factors in a gene or transcript list for comparative analyses. The ENCODE ChIP-Seq Significance Tool is written in JavaScript on the client side and has been tested on Google Chrome, Apple Safari and Mozilla Firefox browsers. Server-side scripts are written in PHP and leverage R and a MySQL database. The tool is available at http://encodeqt.stanford.edu. abutte@stanford.edu Supplementary material is available at Bioinformatics online.

A Conceptual Model to Identify Intent to Use Chemical-Biological Weapons

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Mary Zalesny

2017-10-01

Full Text Available This paper describes a conceptual model to identify and interrelate indicators of intent of non-state actors to use chemical or biological weapons. The model expands on earlier efforts to understand intent to use weapons of mass destruction by building upon well-researched theories of intent and behavior and focusing on a sub-set of weapons of mass destruction (WMD to account for the distinct challenges of employing different types of WMD in violent acts. The conceptual model is presented as a first, critical step in developing a computational model for assessing the potential for groups to use chemical or biological weapons.

Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis.

Science.gov (United States)

Oktem-Okullu, Sinem; Tiftikci, Arzu; Saruc, Murat; Cicek, Bahattin; Vardareli, Eser; Tozun, Nurdan; Kocagoz, Tanil; Sezerman, Ugur; Yavuz, Ahmet Sinan; Sayi-Yazgan, Ayca

2015-01-01

The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors; cagA, vacA, oipA, babA, hpaA, napA, dupA, ureA, ureB on T cell response in H. pylori infected patients have not been fully elucidated. We developed a multiplex- PCR assay to detect nine H. pylori virulence genes with in a three PCR reactions. Also, the expression levels of Th1, Th17 and Treg cell specific cytokines and transcription factors were detected by using qRT-PCR assays. Furthermore, a novel expert derived model is developed to identify set of factors and rules that can distinguish the ulcer patients from gastritis patients. Within all virulence factors that we tested, we identified a correlation between the presence of napA virulence gene and ulcer disease as a first data. Additionally, a positive correlation between the H. pylori dupA virulence factor and IFN-γ, and H. pylori babA virulence factor and IL-17 was detected in gastritis and ulcer patients respectively. By using computer-based models, clinical outcomes of a patients infected with H. pylori can be predicted by screening the patient's H. pylori vacA m1/m2, ureA and cagA status and IFN-γ (Th1), IL-17 (Th17), and FOXP3 (Treg) expression levels. Herein, we report, for the first time, the relationship between H. pylori virulence factors and host immune responses for diagnostic prediction of gastric diseases using computer-based models.

IDENTIFYING ELEVEN FACTORS OF SERVICE MARKETING MIX (4PS) EFFECTIVE ON TENDENCY OF PATIENTS TOWARD PRIVATE HOSPITAL

OpenAIRE

Hosseini, Seyed Mojtaba; Etesaminia, Samira; Jafari, Mehrnoosh

2016-01-01

Introduction: One of the important factors of correct management is to identify the reasons for patient tendency toward private hospitals. This study measures these factors based on service marketing mixes. Patients and methods: This study used a cross sectional descriptive methodology. The study was conducted during 6 months in 2015. The studied population included patients of private hospitals in Tehran. Random sampling was used (n = 200). Data was collected by an author-made questionnaire ...

A combined structural dynamics approach identifies a putative switch in factor VIIa employed by tissue factor to initiate blood coagulation

DEFF Research Database (Denmark)

Olsen, Ole H; Rand, Kasper D; Østergaard, Henrik

2007-01-01

Coagulation factor VIIa (FVIIa) requires tissue factor (TF) to attain full catalytic competency and to initiate blood coagulation. In this study, the mechanism by which TF allosterically activates FVIIa is investigated by a structural dynamics approach that combines molecular dynamics (MD......) simulations and hydrogen/deuterium exchange (HX) mass spectrometry on free and TF-bound FVIIa. The differences in conformational dynamics from MD simulations are shown to be confined to regions of FVIIa observed to undergo structural stabilization as judged by HX experiments, especially implicating activation...... in the presence of TF or an active-site inhibitor. Based on MD simulations, a key switch of the TF-induced structural changes is identified as the interacting pair Leu305{163} and Phe374{225} in FVIIa, whose mutual conformations are guided by the presence of TF and observed to be closely linked to the structural...

A Dynamic Multi-Level Factor Model with Long-Range Dependence

DEFF Research Database (Denmark)

Ergemen, Yunus Emre; Rodríguez-Caballero, Carlos Vladimir

A dynamic multi-level factor model with stationary or nonstationary global and regional factors is proposed. In the model, persistence in global and regional common factors as well as innovations allows for the study of fractional cointegrating relationships. Estimation of global and regional...

Application of Geomorphologic Factors for Identifying Soil Loss in Vulnerable Regions of the Cameron Highlands

Directory of Open Access Journals (Sweden)

Kahhoong Kok

2018-03-01

Full Text Available The main purpose of this study is to propose a methodology for identifying vulnerable regions in the Cameron Highlands that are susceptible to soil loss, based on runoff aggregation structure and the energy expenditure pattern of the natural river basin, within the framework of power law distribution. To this end, three geomorphologic factors, namely shear stress and stream power, as well as the drainage area of every point in the basin of interest, have been extracted using GIS, and then their complementary cumulative distributions are graphically analyzed by fitting them to power law distribution, with the purpose of identifying the sensitive points within the basin that are susceptible to soil loss with respect to scaling regimes of shear stress and stream power. It is observed that the range of vulnerable regions by the scaling regime of shear stress is much narrower than by the scaling regime of stream power. This result seems to suggest that shear stress is a scale-dependent factor, which does not follow power law distribution and does not adequately reflect the energy expenditure pattern of a river basin. Therefore, stream power is preferred as a more reasonable factor for the evaluation of soil loss. The methodology proposed in this study can be validated by visualizing the path of soil loss, which is generated from the hillslope process (characterized by the local slope to the valley through a fluvial process (characterized by the drainage area as well as the local slope.

Identifying Socio-Cultural Factors That Impact the Use of Open Educational Resources in Local Public Administrations

OpenAIRE

Julia Stoffregen; Jan M. Pawlowski; Eric Ras; Snezana Scepanovic; Dragica Zugic

2016-01-01

The goal of this paper is to define relevant barriers to the exchange of Open Educational Resources in local public administrations. Building upon a cultural model, eleven experts were interviewed and asked to evaluate several factors, such as openness in discourse, learning at the workplace, and superior support, among others. The result is a set of socio-cultural factors that shape the use of Open Educational Resources in public administrations. Significant factors are, in...

Tuning and Test of Fragmentation Models Based on Identified Particles and Precision Event Shape Data

CERN Document Server

Abreu, P; Adye, T; Ajinenko, I; Alekseev, G D; Alemany, R; Allport, P P; Almehed, S; Amaldi, Ugo; Amato, S; Andreazza, A; Andrieux, M L; Antilogus, P; Apel, W D; Åsman, B; Augustin, J E; Augustinus, A; Baillon, Paul; Bambade, P; Barão, F; Barate, R; Barbi, M S; Bardin, Dimitri Yuri; Baroncelli, A; Bärring, O; Barrio, J A; Bartl, Walter; Bates, M J; Battaglia, Marco; Baubillier, M; Baudot, J; Becks, K H; Begalli, M; Beillière, P; Belokopytov, Yu A; Belous, K S; Benvenuti, Alberto C; Berggren, M; Bertini, D; Bertrand, D; Besançon, M; Bianchi, F; Bigi, M; Bilenky, S M; Billoir, P; Bloch, D; Blume, M; Bolognese, T; Bonesini, M; Bonivento, W; Booth, P S L; Bosio, C; Botner, O; Boudinov, E; Bouquet, B; Bourdarios, C; Bowcock, T J V; Bozzo, M; Branchini, P; Brand, K D; Brenke, T; Brenner, R A; Bricman, C; Brown, R C A; Brückman, P; Brunet, J M; Bugge, L; Buran, T; Burgsmüller, T; Buschmann, P; Buys, A; Cabrera, S; Caccia, M; Calvi, M; Camacho-Rozas, A J; Camporesi, T; Canale, V; Canepa, M; Cankocak, K; Cao, F; Carena, F; Carroll, L; Caso, Carlo; Castillo-Gimenez, M V; Cattai, A; Cavallo, F R; Chabaud, V; Charpentier, P; Chaussard, L; Checchia, P; Chelkov, G A; Chen, M; Chierici, R; Chliapnikov, P V; Chochula, P; Chorowicz, V; Chudoba, J; Cindro, V; Collins, P; Contreras, J L; Contri, R; Cortina, E; Cosme, G; Cossutti, F; Cowell, J H; Crawley, H B; Crennell, D J; Crosetti, G; Cuevas-Maestro, J; Czellar, S; Dahl-Jensen, Erik; Dahm, J; D'Almagne, B; Dam, M; Damgaard, G; Dauncey, P D; Davenport, Martyn; Da Silva, W; Defoix, C; Deghorain, A; Della Ricca, G; Delpierre, P A; Demaria, N; De Angelis, A; de Boer, Wim; De Brabandere, S; De Clercq, C; La Vaissière, C de; De Lotto, B; De Min, A; De Paula, L S; De Saint-Jean, C; Dijkstra, H; Di Ciaccio, Lucia; Di Diodato, A; Djama, F; Dolbeau, J; Dönszelmann, M; Doroba, K; Dracos, M; Drees, J; Drees, K A; Dris, M; Durand, J D; Edsall, D M; Ehret, R; Eigen, G; Ekelöf, T J C; Ekspong, Gösta; Elsing, M; Engel, J P; Erzen, B; Espirito-Santo, M C; Falk, E; Fassouliotis, D; Feindt, Michael; Ferrer, A; Fichet, S; Filippas-Tassos, A; Firestone, A; Fischer, P A; Föth, H; Fokitis, E; Fontanelli, F; Formenti, F; Franek, B J; Frenkiel, P; Fries, D E C; Frodesen, A G; Frühwirth, R; Fulda-Quenzer, F; Fuster, J A; Galloni, A; Gamba, D; Gandelman, M; García, C; García, J; Gaspar, C; Gasparini, U; Gavillet, P; Gazis, E N; Gelé, D; Gerber, J P; Gokieli, R; Golob, B; Gopal, Gian P; Gorn, L; Górski, M; Guz, Yu; Gracco, Valerio; Graziani, E; Green, C; Grefrath, A; Gris, P; Grosdidier, G; Grzelak, K; Gumenyuk, S A; Gunnarsson, P; Günther, M; Guy, J; Hahn, F; Hahn, S; Hajduk, Z; Hallgren, A; Hamacher, K; Harris, F J; Hedberg, V; Henriques, R P; Hernández, J J; Herquet, P; Herr, H; Hessing, T L; Higón, E; Hilke, Hans Jürgen; Hill, T S; Holmgren, S O; Holt, P J; Holthuizen, D J; Hoorelbeke, S; Houlden, M A; Hrubec, Josef; Huet, K; Hultqvist, K; Jackson, J N; Jacobsson, R; Jalocha, P; Janik, R; Jarlskog, C; Jarlskog, G; Jarry, P; Jean-Marie, B; Johansson, E K; Jönsson, L B; Jönsson, P E; Joram, Christian; Juillot, P; Kaiser, M; Kapusta, F; Karafasoulis, K; Karlsson, M; Karvelas, E; Katsanevas, S; Katsoufis, E C; Keränen, R; Khokhlov, Yu A; Khomenko, B A; Khovanskii, N N; King, B J; Kjaer, N J; Klapp, O; Klein, H; Klovning, A; Kluit, P M; Köne, B; Kokkinias, P; Koratzinos, M; Korcyl, K; Kostyukhin, V; Kourkoumelis, C; Kuznetsov, O; Kreuter, C; Kronkvist, I J; Krumshtein, Z; Krupinski, W; Kubinec, P; Kucewicz, W; Kurvinen, K L; Lacasta, C; Laktineh, I; Lamsa, J; Lanceri, L; Lane, D W; Langefeld, P; Lapin, V; Laugier, J P; Lauhakangas, R; Leder, Gerhard; Ledroit, F; Lefébure, V; Legan, C K; Leitner, R; Lemonne, J; Lenzen, Georg; Lepeltier, V; Lesiak, T; Libby, J; Liko, D; Lindner, R; Lipniacka, A; Lippi, I; Lörstad, B; Loken, J G; López, J M; Loukas, D; Lutz, P; Lyons, L; Naughton, J M; Maehlum, G; Mahon, J R; Maio, A; Malmgren, T G M; Malychev, V; Mandl, F; Marco, J; Marco, R P; Maréchal, B; Margoni, M; Marin, J C; Mariotti, C; Markou, A; Martínez-Rivero, C; Martínez-Vidal, F; Martí i García, S; Masik, J; Matorras, F; Matteuzzi, C; Matthiae, Giorgio; Mazzucato, M; McCubbin, M L; McKay, R; McNulty, R; Medbo, J; Merk, M; Meroni, C; Meyer, S; Meyer, W T; Myagkov, A; Michelotto, M; Migliore, E; Mirabito, L; Mitaroff, Winfried A; Mjörnmark, U; Moa, T; Møller, R; Mönig, K; Monge, M R; Morettini, P; Müller, H; Mulders, M; Mundim, L M; Murray, W J; Muryn, B; Myatt, Gerald; Naraghi, F; Navarria, Francesco Luigi; Navas, S; Nawrocki, K; Negri, P; Neumann, W; Neumeister, N; Nicolaidou, R; Nielsen, B S; Nieuwenhuizen, M; Nikolaenko, V; Niss, P; Nomerotski, A; Normand, Ainsley; Oberschulte-Beckmann, W; Obraztsov, V F; Olshevskii, A G; Onofre, A; Orava, Risto; Österberg, K; Ouraou, A; Paganini, P; Paganoni, M; Pagès, P; Pain, R; Palka, H; Papadopoulou, T D; Papageorgiou, K; Pape, L; Parkes, C; Parodi, F; Passeri, A; Pegoraro, M; Peralta, L; Pernegger, H; Pernicka, Manfred; Perrotta, A; Petridou, C; Petrolini, A; Petrovykh, M; Phillips, H T; Piana, G; Pierre, F; Plaszczynski, S; Podobrin, O; Pol, M E; Polok, G; Poropat, P; Pozdnyakov, V; Privitera, P; Pukhaeva, N; Pullia, Antonio; Radojicic, D; Ragazzi, S; Rahmani, H; Rames, J; Ratoff, P N; Read, A L; Reale, M; Rebecchi, P; Redaelli, N G; Regler, Meinhard; Reid, D; Renton, P B; Resvanis, L K; Richard, F; Richardson, J; Rídky, J; Rinaudo, G; Ripp, I; Romero, A; Roncagliolo, I; Ronchese, P; Roos, L; Rosenberg, E I; Rosso, E; Roudeau, Patrick; Rovelli, T; Rückstuhl, W; Ruhlmann-Kleider, V; Ruiz, A; Rybicki, K; Saarikko, H; Sacquin, Yu; Sadovskii, A; Sahr, O; Sajot, G; Salt, J; Sánchez, J; Sannino, M; Schimmelpfennig, M; Schneider, H; Schwickerath, U; Schyns, M A E; Sciolla, G; Scuri, F; Seager, P; Sedykh, Yu; Segar, A M; Seitz, A; Sekulin, R L; Serbelloni, L; Shellard, R C; Siegrist, P; Silvestre, R; Simonetti, S; Simonetto, F; Sissakian, A N; Sitár, B; Skaali, T B; Smadja, G; Smirnov, N; Smirnova, O G; Smith, G R; Sokolov, A; Sosnowski, R; Souza-Santos, D; Spassoff, Tz; Spiriti, E; Sponholz, P; Squarcia, S; Stanescu, C; Stapnes, Steinar; Stavitski, I; Stevenson, K; Stichelbaut, F; Stocchi, A; Strauss, J; Strub, R; Stugu, B; Szczekowski, M; Szeptycka, M; Tabarelli de Fatis, T; Tavernet, J P; Chikilev, O G; Thomas, J; Tilquin, A; Timmermans, J; Tkatchev, L G; Todorov, T; Todorova, S; Toet, D Z; Tomaradze, A G; Tomé, B; Tonazzo, A; Tortora, L; Tranströmer, G; Treille, D; Trischuk, W; Tristram, G; Trombini, A; Troncon, C; Tsirou, A L; Turluer, M L; Tyapkin, I A; Tyndel, M; Tzamarias, S; Überschär, B; Ullaland, O; Uvarov, V; Valenti, G; Vallazza, E; van Apeldoorn, G W; van Dam, P; Van Eldik, J; Vassilopoulos, N; Vegni, G; Ventura, L; Venus, W A; Verbeure, F; Verlato, M; Vertogradov, L S; Vilanova, D; Vincent, P; Vitale, L; Vlasov, E; Vodopyanov, A S; Vrba, V; Wahlen, H; Walck, C; Waldner, F; Weierstall, M; Weilhammer, Peter; Weiser, C; Wetherell, Alan M; Wicke, D; Wickens, J H; Wielers, M; Wilkinson, G R; Williams, W S C; Winter, M; Witek, M; Woschnagg, K; Yip, K; Yushchenko, O P; Zach, F; Zaitsev, A; Zalewska-Bak, A; Zalewski, Piotr; Zavrtanik, D; Zevgolatakos, E; Zimin, N I; Zito, M; Zontar, D; Zucchelli, G C; Zumerle, G

1996-01-01

Event shape and charged particle inclusive distributions are measured using 750000 decays of the $Z$ to hadrons from the DELPHI detector at LEP. These precise data allow a decisive confrontation with models of the hadronization process. Improved tunings of the JETSET ARIADNE and HERWIG parton shower models and the JETSET matrix element model are obtained by fitting the models to these DELPHI data as well as to identified particle distributions from all LEP experiments. The description of the data distributions by the models is critically reviewed with special importance attributed to identified particles.

Matrix factorizations, minimal models and Massey products

International Nuclear Information System (INIS)

Knapp, Johanna; Omer, Harun

2006-01-01

We present a method to compute the full non-linear deformations of matrix factorizations for ADE minimal models. This method is based on the calculation of higher products in the cohomology, called Massey products. The algorithm yields a polynomial ring whose vanishing relations encode the obstructions of the deformations of the D-branes characterized by these matrix factorizations. This coincides with the critical locus of the effective superpotential which can be computed by integrating these relations. Our results for the effective superpotential are in agreement with those obtained from solving the A-infinity relations. We point out a relation to the superpotentials of Kazama-Suzuki models. We will illustrate our findings by various examples, putting emphasis on the E 6 minimal model

Prevalence of HIV in pregnant women identified with a risk factor at a tertiary care hospital.

Science.gov (United States)

Mahmud, Ghazala; Abbas, Shazra

2009-01-01

HIV is an epidemic quite unlike any other, combining the problems of a lifelong medical disease with immense social, psychological, economic and public health consequences. Since we are living in a global village where human interactions has become fast and frequent, diseases like HIV are no more alien to us. HIV/AIDS in Pakistan is slowly gaining recognition as a public health issue of great importance. Objectives of this study were to determine the prevalence of HIV in pregnant women identified with a high risk factor/behaviour at a tertiary care hospital. It is a Descriptive study. All pregnant women attending antenatal booking clinic were assessed via a pre-designed 'Risk assessment questionnaire'. Women identified with a risk factor were offered HIV Rapid screening test (Capillus HIV1/2). Positive (reactive) results on screening test were confirmed with ELISA. During the study period (March 2007-May 2008), out of 5263 antenatal bookings 785 (14%) women were identified with a risk factor. HIV screening test was done in 779 (99%), and 6 women refused testing. Three women (0.3%) were found positive (reactive) on screening. Two out of 3 women were confirmed positive (0.2%) on ELISA. Husbands of both women were tested and one found positive (migrant from Dubai). Second women had history of blood transfusion. Her husband was HIV negative. During the study period, in addition to 2 pregnant women diagnosed as HIV positive through ANC risk screening, 6 confirmed HIV positive women, found pregnant were referred from 'HIV Treatment Centre', Pakistan Institute of Medical Sciences (PIMS) to Prevention of Parent to Child Transmission (PPTCT) centre for obstetric care. Spouses of 5 out of 6 had history of working abroad and extramarital sexual relationships. All positive (8) women were referred to PPTCT centre for further management. A simple 'Risk Assessment Questionnaire' can help us in identifying women who need HIV screening. Sexual transmission still remains the

Modelling the regional application of stakeholder identified land management strategies.

Science.gov (United States)

Irvine, B. J.; Fleskens, L.; Kirkby, M. J.

2012-04-01

The DESIRE project has trialled a series of sustainable land management (SLM) technologies. These technologies have been identified as being beneficial in mitigating land degradation by local stakeholders from a range of semi-arid study sites. The field results and the qualitative WOCAT technology assessment ftom across the study sites have been used to develop the adapted PESERA SLM model. This paper considers the development of the adapted PESERA SLM model and the potential for applying locally successful SLM technologies across a wider range of climatic and environmental conditions with respect to degradation risk, biomass production and the investment cost interface (PESERA/DESMICE). The integrate PESERA/DESMICE model contributes to the policy debate by providing a biophysical and socio-economic assessment of technology and policy scenarios.

Model of a ternary complex between activated factor VII, tissue factor and factor IX.

Science.gov (United States)

Chen, Shu-wen W; Pellequer, Jean-Luc; Schved, Jean-François; Giansily-Blaizot, Muriel

2002-07-01

Upon binding to tissue factor, FVIIa triggers coagulation by activating vitamin K-dependent zymogens, factor IX (FIX) and factor X (FX). To understand recognition mechanisms in the initiation step of the coagulation cascade, we present a three-dimensional model of the ternary complex between FVIIa:TF:FIX. This model was built using a full-space search algorithm in combination with computational graphics. With the known crystallographic complex FVIIa:TF kept fixed, the FIX docking was performed first with FIX Gla-EGF1 domains, followed by the FIX protease/EGF2 domains. Because the FIXa crystal structure lacks electron density for the Gla domain, we constructed a chimeric FIX molecule that contains the Gla-EGF1 domains of FVIIa and the EGF2-protease domains of FIXa. The FVIIa:TF:FIX complex has been extensively challenged against experimental data including site-directed mutagenesis, inhibitory peptide data, haemophilia B database mutations, inhibitor antibodies and a novel exosite binding inhibitor peptide. This FVIIa:TF:FIX complex provides a powerful tool to study the regulation of FVIIa production and presents new avenues for developing therapeutic inhibitory compounds of FVIIa:TF:substrate complex.

Positive matrix factorization and trajectory modelling for source identification: A new look at Indian Ocean Experiment ship observations

Science.gov (United States)

Bhanuprasad, S. G.; Venkataraman, Chandra; Bhushan, Mani

The sources of aerosols on a regional scale over India have only recently received attention in studies using back trajectory analysis and chemical transport modelling. Receptor modelling approaches such as positive matrix factorization (PMF) and the potential source contribution function (PSCF) are effective tools in source identification of urban and regional-scale pollution. In this work, PMF and PSCF analysis is applied to identify categories and locations of sources that influenced surface concentrations of aerosols in the Indian Ocean Experiment (INDOEX) domain measured on-board the research vessel Ron Brown [Quinn, P.K., Coffman, D.J., Bates, T.S., Miller, T.L., Johnson, J.E., Welton, E.J., et al., 2002. Aerosol optical properties during INDOEX 1999: means, variability, and controlling factors. Journal of Geophysical Research 107, 8020, doi:10.1029/2000JD000037]. Emissions inventory information is used to identify sources co-located with probable source regions from PSCF. PMF analysis identified six factors influencing PM concentrations during the INDOEX cruise of the Ron Brown including a biomass combustion factor (35-40%), three industrial emissions factors (35-40%), primarily secondary sulphate-nitrate, balance trace elements and Zn, and two dust factors (20-30%) of Si- and Ca-dust. The identified factors effectively predict the measured submicron PM concentrations (slope of regression line=0.90±0.20; R2=0.76). Probable source regions shifted based on changes in surface and elevated flows during different times in the ship cruise. They were in India in the early part of the cruise, but in west Asia, south-east Asia and Africa, during later parts of the cruise. Co-located sources include coal-fired electric utilities, cement, metals and petroleum production in India and west Asia, biofuel combustion for energy and crop residue burning in India, woodland/forest burning in north sub-Saharan Africa and forest burning in south-east Asia. Significant findings

Genome-wide strategies identify downstream target genes of chick connective tissue-associated transcription factors.

Science.gov (United States)

Orgeur, Mickael; Martens, Marvin; Leonte, Georgeta; Nassari, Sonya; Bonnin, Marie-Ange; Börno, Stefan T; Timmermann, Bernd; Hecht, Jochen; Duprez, Delphine; Stricker, Sigmar

2018-03-29

Connective tissues support organs and play crucial roles in development, homeostasis and fibrosis, yet our understanding of their formation is still limited. To gain insight into the molecular mechanisms of connective tissue specification, we selected five zinc-finger transcription factors - OSR1, OSR2, EGR1, KLF2 and KLF4 - based on their expression patterns and/or known involvement in connective tissue subtype differentiation. RNA-seq and ChIP-seq profiling of chick limb micromass cultures revealed a set of common genes regulated by all five transcription factors, which we describe as a connective tissue core expression set. This common core was enriched with genes associated with axon guidance and myofibroblast signature, including fibrosis-related genes. In addition, each transcription factor regulated a specific set of signalling molecules and extracellular matrix components. This suggests a concept whereby local molecular niches can be created by the expression of specific transcription factors impinging on the specification of local microenvironments. The regulatory network established here identifies common and distinct molecular signatures of limb connective tissue subtypes, provides novel insight into the signalling pathways governing connective tissue specification, and serves as a resource for connective tissue development. © 2018. Published by The Company of Biologists Ltd.

Identifying and prioritizing different factors influencing the success of advertisement during the economic depression

Directory of Open Access Journals (Sweden)

Aram Rashidi

2014-04-01

Full Text Available During the financial crisis of 2007, many businesses and banks faced unexpected circumstances and declared bankruptcy. Market mortgage crisis and the collapse of the economic system in United States created a substantial amount of damage in world economy. Within a few years, the economic downturn was transferred to developing countries such as Iran. The recession has created conditions for Iranian companies that have led them to focus more on the subject of advertising since this is the primary tool of communication and business customers business. Success and failure of many organizations and companies depend on their advertisement planning. In this study, the factors contributing to the success and effectiveness of advertising during the recession time are identified. This survey has been accomplished on investigating an Iranian dairy firm named “Kalle”. Using a questionnaire in Likert scale, the study determines the effects of various factors of advertisement on sales improvement in this firm using Pearson correlation ratio and rank them based on Freedman test. Cronbach alpha has been calculated as 0.93. According to the results, factors that contribute to the success of advertising during a recession include: Responsiveness to customers’ needs, advertising tools, content factors, the amount of money spent and availability.

A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

Science.gov (United States)

Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

2010-01-01

The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

A hierarchical model for ordinal matrix factorization

DEFF Research Database (Denmark)

Paquet, Ulrich; Thomson, Blaise; Winther, Ole

2012-01-01

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based...

Cloud Computing Adoption Business Model Factors: Does Enterprise Size Matter?

OpenAIRE

Bogataj Habjan, Kristina; Pucihar, Andreja

2017-01-01

This paper presents the results of research investigating the impact of business model factors on cloud computing adoption. The introduced research model consists of 40 cloud computing business model factors, grouped into eight factor groups. Their impact and importance for cloud computing adoption were investigated among enterpirses in Slovenia. Furthermore, differences in opinion according to enterprise size were investigated. Research results show no statistically significant impacts of in...

Applying complexity theory: A primer for identifying and modeling firm anomalies

Directory of Open Access Journals (Sweden)

Arch G. Woodside

2018-01-01

Full Text Available This essay elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., non-innovative versus highly innovative, small versus large size in sales or number of employees, or serving local versus international markets provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability—even if directional analyses (e.g., regression analysis, including structural equation modeling indicates that the independent (main effects of the individual antecedents relate to outcomes directionally—because firm (case anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of non-innovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing—null hypotheses statistic testing (NHST—to embrace somewhat precise outcome testing (SPOT is necessary for extracting highly useful information about the causes of anomalies—associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advances strategic management beyond the current dominant logic of directional modeling of single outcomes.

IDENTIFYING ELEVEN FACTORS OF SERVICE MARKETING MIX (4PS) EFFECTIVE ON TENDENCY OF PATIENTS TOWARD PRIVATE HOSPITAL

Science.gov (United States)

Hosseini, Seyed Mojtaba; Etesaminia, Samira; Jafari, Mehrnoosh

2016-01-01

Introduction: One of the important factors of correct management is to identify the reasons for patient tendency toward private hospitals. This study measures these factors based on service marketing mixes. Patients and methods: This study used a cross sectional descriptive methodology. The study was conducted during 6 months in 2015. The studied population included patients of private hospitals in Tehran. Random sampling was used (n = 200). Data was collected by an author-made questionnaire for service marketing factors. Reliability and validity of the questionnaire were confirmed. Data analysis was done using factor analysis test in SPSS 20. Results: The results showed that constant attendance of physicians and nurses has the highest effect (0.707%) on patient tendency toward private hospitals. PMID:27999486

Pilot Critical Incident Reports as a Means to Identify Human Factors of Remotely Piloted Aircraft

Science.gov (United States)

Hobbs, Alan; Cardoza, Colleen; Null, Cynthia

2016-01-01

It has been estimated that aviation accidents are typically preceded by numerous minor incidents arising from the same causal factors that ultimately produced the accident. Accident databases provide in-depth information on a relatively small number of occurrences, however incident databases have the potential to provide insights into the human factors of Remotely Piloted Aircraft System (RPAS) operations based on a larger volume of less-detailed reports. Currently, there is a lack of incident data dealing with the human factors of unmanned aircraft systems. An exploratory study is being conducted to examine the feasibility of collecting voluntary critical incident reports from RPAS pilots. Twenty-three experienced RPAS pilots volunteered to participate in focus groups in which they described critical incidents from their own experience. Participants were asked to recall (1) incidents that revealed a system flaw, or (2) highlighted a case where the human operator contributed to system resilience or mission success. Participants were asked to only report incidents that could be included in a public document. During each focus group session, a note taker produced a de-identified written record of the incident narratives. At the end of the session, participants reviewed each written incident report, and made edits and corrections as necessary. The incidents were later analyzed to identify contributing factors, with a focus on design issues that either hindered or assisted the pilot during the events. A total of 90 incidents were reported. Human factor issues included the impact of reduced sensory cues, traffic separation in the absence of an out-the-window view, control latencies, vigilance during monotonous and ultra-long endurance flights, control station design considerations, transfer of control between control stations, the management of lost link procedures, and decision-making during emergencies. Pilots participated willingly and enthusiastically in the study

Examples of testing global identifiability of biological and biomedical models with the DAISY software.

Science.gov (United States)

Saccomani, Maria Pia; Audoly, Stefania; Bellu, Giuseppina; D'Angiò, Leontina

2010-04-01

DAISY (Differential Algebra for Identifiability of SYstems) is a recently developed computer algebra software tool which can be used to automatically check global identifiability of (linear and) nonlinear dynamic models described by differential equations involving polynomial or rational functions. Global identifiability is a fundamental prerequisite for model identification which is important not only for biological or medical systems but also for many physical and engineering systems derived from first principles. Lack of identifiability implies that the parameter estimation techniques may not fail but any obtained numerical estimates will be meaningless. The software does not require understanding of the underlying mathematical principles and can be used by researchers in applied fields with a minimum of mathematical background. We illustrate the DAISY software by checking the a priori global identifiability of two benchmark nonlinear models taken from the literature. The analysis of these two examples includes comparison with other methods and demonstrates how identifiability analysis is simplified by this tool. Thus we illustrate the identifiability analysis of other two examples, by including discussion of some specific aspects related to the role of observability and knowledge of initial conditions in testing identifiability and to the computational complexity of the software. The main focus of this paper is not on the description of the mathematical background of the algorithm, which has been presented elsewhere, but on illustrating its use and on some of its more interesting features. DAISY is available on the web site http://www.dei.unipd.it/ approximately pia/. 2010 Elsevier Ltd. All rights reserved.

Skyrme-model πNN form factor and nucleon-nucleon interaction

International Nuclear Information System (INIS)

Holzwarth, G.; Machleidt, R.

1997-01-01

We apply the strong πNN form factor, which emerges from the Skyrme model, in the two-nucleon system using a one-boson-exchange (OBE) model for the nucleon-nucleon (NN) interaction. Deuteron properties and phase parameters of NN scattering are reproduced well. In contrast to the form factor of monopole shape that is traditionally used in OBE models, the Skyrme form factor leaves low-momentum transfers essentially unaffected while it suppresses the high-momentum region strongly. It turns out that this behavior is very appropriate for models of the NN interaction and makes it possible to use a soft pion form factor in the NN system. As a consequence, the πN and the NN systems can be described using the same πNN form factor, which is impossible with the monopole. copyright 1997 The American Physical Society

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

Science.gov (United States)

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

Dynamic Factor Models for the Volatility Surface

DEFF Research Database (Denmark)

van der Wel, Michel; Ozturk, Sait R.; Dijk, Dick van

The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable...

Identifying Socio-Cultural Factors That Impact the Use of Open Educational Resources in Local Public Administrations

Directory of Open Access Journals (Sweden)

Julia Stoffregen

2016-12-01

Full Text Available The goal of this paper is to define relevant barriers to the exchange of Open Educational Resources in local public administrations. Building upon a cultural model, eleven experts were interviewed and asked to evaluate several factors, such as openness in discourse, learning at the workplace, and superior support, among others. The result is a set of socio-cultural factors that shape the use of Open Educational Resources in public administrations. Significant factors are, in this respect, the independent choice of learning resources, the spirit of the platform, the range of available formats and access to technologies. Practitioners use these factors to elaborate on the readiness of public administrations towards the use of open e-Learning systems. To academic debates on culture in e-Learning, the results provide an alternative model that is contextualized to meet the demands of public sector contexts. Overall, the paper contributes to the lack of research about open e-Learning systems in the public sector, as well as regarding culture in the management of learning and knowledge exchange.

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

Science.gov (United States)

Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han

2018-05-03

Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.

Factor analysis in the Genetics of Asthma International Network family study identifies five major quantitative asthma phenotypes

NARCIS (Netherlands)

Pillai, S. G.; Tang, Y.; van den Oord, E.; Klotsman, M.; Barnes, K.; Carlsen, K.; Gerritsen, J.; Lenney, W.; Silverman, M.; Sly, P.; Sundy, J.; Tsanakas, J.; von Berg, A.; Whyte, M.; Ortega, H. G.; Anderson, W. H.; Helms, P. J.

Background Asthma is a clinically heterogeneous disease caused by a complex interaction between genetic susceptibility and diverse environmental factors. In common with other complex diseases the lack of a standardized scheme to evaluate the phenotypic variability poses challenges in identifying the

Antidepressant Effects of Pharmacopuncture on Behavior and Brain-Derived Neurotrophic Factor (BDNF Expression in Chronic Stress Model of Mice

Directory of Open Access Journals (Sweden)

Yunna Kim

2017-12-01

Conclusion: HJ11 improves depressive-like behaviors in the stress-induced mouse model of depression, and the results indicate that the neuroprotective effect of HJ11, identified by brain-derived neurotrophic factor expression, may play a critical role in its antidepressant effect.

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

Directory of Open Access Journals (Sweden)

Anil Raj

Full Text Available Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

Science.gov (United States)

Raj, Anil; Shim, Heejung; Gilad, Yoav; Pritchard, Jonathan K; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

Psychosocial factors, musculoskeletal disorders and work-related fatigue amongst nurses in Brunei: structural equation model approach.

Science.gov (United States)

Abdul Rahman, Hanif; Abdul-Mumin, Khadizah; Naing, Lin

2017-09-01

Psychosocial factors, musculoskeletal disorders and work-related fatigue have adverse effects on individual nurses and place a substantial financial burden on health care. Evidence of an association has been reported in the literature, but no theoretical explanation has been published to date. To explore and develop a structural model to provide a theoretical explanation for this relationship. A cross-sectional study using data from 201 valid samples of emergency and critical care nurses across public hospitals in Brunei was performed via self-administered questionnaire. The structural equation model was assessed using partial least squares analysis. A valid and robust structural model was constructed. This revealed that 61.5% of the variance in chronic fatigue could be explained by psychosocial factors and musculoskeletal disorders pathways. Among the psychosocial factors, work-family conflict was identified as a key mediator for progression of musculoskeletal problems and subsequent fatigue through stress and burnout. This report provides a novel theoretical contribution to understanding the relationship between psychosocial factors, musculoskeletal disorders and work-related fatigue. These preliminary results may be useful for future studies on the development of work-related fatigue and musculoskeletal disorders, particularly the central role of work-family conflict. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiplex-PCR-Based Screening and Computational Modeling of Virulence Factors and T-Cell Mediated Immunity in Helicobacter pylori Infections for Accurate Clinical Diagnosis.

Directory of Open Access Journals (Sweden)

Sinem Oktem-Okullu

Full Text Available The outcome of H. pylori infection is closely related with bacteria's virulence factors and host immune response. The association between T cells and H. pylori infection has been identified, but the effects of the nine major H. pylori specific virulence factors; cagA, vacA, oipA, babA, hpaA, napA, dupA, ureA, ureB on T cell response in H. pylori infected patients have not been fully elucidated. We developed a multiplex- PCR assay to detect nine H. pylori virulence genes with in a three PCR reactions. Also, the expression levels of Th1, Th17 and Treg cell specific cytokines and transcription factors were detected by using qRT-PCR assays. Furthermore, a novel expert derived model is developed to identify set of factors and rules that can distinguish the ulcer patients from gastritis patients. Within all virulence factors that we tested, we identified a correlation between the presence of napA virulence gene and ulcer disease as a first data. Additionally, a positive correlation between the H. pylori dupA virulence factor and IFN-γ, and H. pylori babA virulence factor and IL-17 was detected in gastritis and ulcer patients respectively. By using computer-based models, clinical outcomes of a patients infected with H. pylori can be predicted by screening the patient's H. pylori vacA m1/m2, ureA and cagA status and IFN-γ (Th1, IL-17 (Th17, and FOXP3 (Treg expression levels. Herein, we report, for the first time, the relationship between H. pylori virulence factors and host immune responses for diagnostic prediction of gastric diseases using computer-based models.

New JLS-Factor Model versus the Standard JLS Model: A Case Study on Chinese Stock Bubbles

Directory of Open Access Journals (Sweden)

Zongyi Hu

2017-01-01

Full Text Available In this paper, we extend the Johansen-Ledoit-Sornette (JLS model by introducing fundamental economic factors in China (including the interest rate and deposit reserve rate and the historical volatilities of targeted and US equity indices into the original model, which is a flexible tool to detect bubbles and predict regime changes in financial markets. We then derive a general method to incorporate these selected factors in addition to the log-periodic power law signature of herding and compare the prediction accuracy of the critical time between the original and the new JLS models (termed the JLS-factor model by applying these two models to fit two well-known Chinese stock indices in three bubble periods. The results show that the JLS-factor model with Chinese characteristics successfully depicts the evolutions of bubbles and “antibubbles” and constructs efficient end-of-bubble signals for all bubbles in Chinese stock markets. In addition, the results of standard statistical tests demonstrate the excellent explanatory power of these additive factors and confirm that the new JLS model provides useful improvements over the standard JLS model.

Modeling Factors with Influence on Sustainable University Management

Directory of Open Access Journals (Sweden)

Oana Dumitrascu

2015-01-01

Full Text Available The main objective of this paper is to present the factors with influence on the sustainable university management and the relationships between them. In the scientific approach we begin from a graphical model, according to which the extracurricular activities together with internal environmental factors influence students’ involvement in such activities, the university attractiveness, their academic performance and their integration into the socially-economic and natural environment (components related with sustainable development. The model emphasizes that individual performances, related to students’ participation in extracurricular activities, have a positive influence on the sustainability of university management. The results of the study have shown that the university sustainability may be influenced by a number of factors, such as students’ performance, students’ involvement in extracurricular activities or university’s attractiveness and can in turn influence implicitly also the sustainability of university management. The originality of the paper consists in the relationships study using the modeling method in general and informatics tools of modeling in particular, as well as through graphical visualization of some influences, on the sustainability university management.

BRAVO identifies critical success factors for logistics

NARCIS (Netherlands)

Kokke, C.J.T.M.; Donselaar, van K.H.; Allessie, M.

1997-01-01

Good operational performance depends on knowing which operational factors are critical to success. Bravo, a research project involving 150 transport and distribution companies in The Netherlands, has developed a tool now being adopted nationally by all companies in the sector to find opportunities

AN INTERACTION MODEL BETWEEN ENVIRONMENTAL FACTORS AND BLACK RICE GROWTH IN IRRIGATED ORGANIC PADDY FIELD

Directory of Open Access Journals (Sweden)

Budiman

2015-02-01

Full Text Available Black rice production in organic farming system does not meet the demand of local customers because of its low productivity. This research aimed to set an interaction model using multivariate analysis via smartPLS to identify environmental factors which simultaneously affects the growth of black rice. The growth of black rice in two irrigated organic paddy field in Malang, Indonesia was observed during planting period from November 2011 to March 2012. In each rice field, the growth was periodically recorded during planting periods: 19-29 days after planting (dap, 41-45 dap, 62-66 dap, 77-81 dap, 90-94 dap and 104-106 dap. Environmental factors such as water quantities, soil conditions, weed communities and cultivation system around the black rice population were also measured. Black rice growth was influenced simultaneously by water quantities, soil, weed communities and cultivating systems with predictive-relevance value reaching 92.83%. Based on the model, water quantities in paddy field is a key factor which directly and indirectly determined the growth and productivity of black rice.

Identifying and assessing the factors affecting skill gap in digital marketing in communication industry companies

OpenAIRE

Ghotbifar, Fereshteh; Marjani, Mohammad Reza; Ramazani, Abbas

2017-01-01

As far as new communication channels are concerned, there have been extensive developments in communications and marketing in digital era. Today, therefore, companies try to take advantage of digital marketing channels to provide suitable services to customers to improve their satisfaction level. However, this study aimed to identify and assess factors affecting skill gap in digital marketing. This was descriptive correlation study. The population consisted of experts in communications indust...

Risk factors and visual fatigue of baggage X-ray security screeners: a structural equation modelling analysis.

Science.gov (United States)

Yu, Rui-Feng; Yang, Lin-Dong; Wu, Xin

2017-05-01

This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue using structural equation modelling approach. Two hundred and five X-ray security screeners participated in a questionnaire survey. The result showed that satisfaction with the VDT's physical features and the work environment conditions were negatively correlated with the intensity of visual fatigue, whereas job stress and job burnout had direct positive influences. The path coefficient between the image quality of VDT and visual fatigue was not significant. The total effects of job burnout, job stress, the VDT's physical features and the work environment conditions on visual fatigue were 0.471, 0.469, -0.268 and -0.251 respectively. These findings indicated that both extrinsic factors relating to VDT and workplace environment and psychological factors including job burnout and job stress should be considered in the workplace design and work organisation of security screening tasks to reduce screeners' visual fatigue. Practitioner Summary: This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue. The findings were of great importance to the workplace design and the work organisation of security screening tasks to reduce screeners' visual fatigue.

Critical success factors for positive user experience in hotel websites:applying Herzberg’s two factor theory for user experience modeling