Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.

Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan

PubMed Central

2011-01-01

Background The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases. Method This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression. Results Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model. Conclusions There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study. PMID:21513554

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

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

Bramer, L. M.; Rounds, J.; Burleyson, C. D.

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions is examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and datasets were examined. A penalized logistic regression model fit at the operation-zone levelmore » was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at different time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

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

Bramer, Lisa M.; Rounds, J.; Burleyson, C. D.

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which wasmore » fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

DOE PAGES

Bramer, Lisa M.; Rounds, J.; Burleyson, C. D.; ...

2017-09-22

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which wasmore » fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

PubMed

Bond, H S; Sullivan, S G; Cowling, B J

2016-06-01

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

Effect of Initial Conditions on Reproducibility of Scientific Research

PubMed Central

Djulbegovic, Benjamin; Hozo, Iztok

2014-01-01

Background: It is estimated that about half of currently published research cannot be reproduced. Many reasons have been offered as explanations for failure to reproduce scientific research findings- from fraud to the issues related to design, conduct, analysis, or publishing scientific research. We also postulate a sensitive dependency on initial conditions by which small changes can result in the large differences in the research findings when attempted to be reproduced at later times. Methods: We employed a simple logistic regression equation to model the effect of covariates on the initial study findings. We then fed the input from the logistic equation into a logistic map function to model stability of the results in repeated experiments over time. We illustrate the approach by modeling effects of different factors on the choice of correct treatment. Results: We found that reproducibility of the study findings depended both on the initial values of all independent variables and the rate of change in the baseline conditions, the latter being more important. When the changes in the baseline conditions vary by about 3.5 to about 4 in between experiments, no research findings could be reproduced. However, when the rate of change between the experiments is ≤2.5 the results become highly predictable between the experiments. Conclusions: Many results cannot be reproduced because of the changes in the initial conditions between the experiments. Better control of the baseline conditions in-between the experiments may help improve reproducibility of scientific findings. PMID:25132705

Effect of initial conditions on reproducibility of scientific research.

PubMed

Djulbegovic, Benjamin; Hozo, Iztok

2014-06-01

It is estimated that about half of currently published research cannot be reproduced. Many reasons have been offered as explanations for failure to reproduce scientific research findings- from fraud to the issues related to design, conduct, analysis, or publishing scientific research. We also postulate a sensitive dependency on initial conditions by which small changes can result in the large differences in the research findings when attempted to be reproduced at later times. We employed a simple logistic regression equation to model the effect of covariates on the initial study findings. We then fed the input from the logistic equation into a logistic map function to model stability of the results in repeated experiments over time. We illustrate the approach by modeling effects of different factors on the choice of correct treatment. We found that reproducibility of the study findings depended both on the initial values of all independent variables and the rate of change in the baseline conditions, the latter being more important. When the changes in the baseline conditions vary by about 3.5 to about 4 in between experiments, no research findings could be reproduced. However, when the rate of change between the experiments is ≤2.5 the results become highly predictable between the experiments. Many results cannot be reproduced because of the changes in the initial conditions between the experiments. Better control of the baseline conditions in-between the experiments may help improve reproducibility of scientific findings.

Application of logistic regression to case-control association studies involving two causative loci.

PubMed

North, Bernard V; Curtis, David; Sham, Pak C

2005-01-01

Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci. We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci.

Combination of a Stressor-Response Model with a Conditional Probability Analysis Approach for Developing Candidate Criteria from MBSS

EPA Science Inventory

I show that a conditional probability analysis using a stressor-response model based on a logistic regression provides a useful approach for developing candidate water quality criteria from empirical data, such as the Maryland Biological Streams Survey (MBSS) data.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

Leavers, Movers, and Stayers: The Role of Workplace Conditions in Teacher Mobility Decisions

ERIC Educational Resources Information Center

Kukla-Acevedo, Sharon

2009-01-01

The author explored whether 3 workplace conditions were related to teacher mobility decisions. The modeling strategy incorporated a series of binomial and multinomial logistic models to estimate the effects of administrative support, classroom control, and behavioral climate on teachers' decisions to quit teaching or switch schools. The results…

Combination of a Stresor-Response Model with a Conditional Probability Anaylsis Approach to Develop Candidate Criteria from Empirical Data

EPA Science Inventory

We show that a conditional probability analysis that utilizes a stressor-response model based on a logistic regression provides a useful approach for developing candidate water quality criterai from empirical data. The critical step in this approach is transforming the response ...

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

Vehicle Scheduling Schemes for Commercial and Emergency Logistics Integration

PubMed Central

Li, Xiaohui; Tan, Qingmei

2013-01-01

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models. PMID:24391724

Vehicle scheduling schemes for commercial and emergency logistics integration.

PubMed

Li, Xiaohui; Tan, Qingmei

2013-01-01

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models.

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

PubMed

Tangen, C M; Koch, G G

1999-03-01

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

Relating induced in situ conditions of raw chicken breast meat to pinking.

PubMed

Holownia, K; Chinnan, M S; Reynolds, A E; Davis, J W

2004-01-01

Our objective was to simulate the pink color defect in cooked chicken breast meat with treatment combinations that would induce measurable changes in the conditions of raw meat. In addition, the feasibility of using induced raw meat conditions to develop a logistic regression model for prediction of pinking was studied. Approximately 960 breast fillets from 2 plants with 2 replications were used for inducing in situ conditions with 16 combinations of sodium chloride, sodium tripolyphosphate, sodium erythorbate, and sodium nitrite (present and not present). Muscles in all treatments were subjected to individual injections, followed by tumbling, cooking, and chilling. Raw samples were analyzed for pH, oxidation-reduction potential, and pigment evaluation. Results indicated a significant role of induced in situ conditions of raw meat in the occurrence of pinking. Presence of 1 ppm or more of sodium nitrite in raw meat produced significant pinking of cooked meat. The light muscle color group was least affected and the dark group was most affected by induced pH, oxidation-reduction potential conditions, and metmyoglobin and nitrosopigment content. The predictive ability of the logistic model was more than 90% with nitrosopigment, pH, and reducing conditions being the most important factors. Moreover, validation of the model was confirmed by close association between observed pink samples and those predicted as pink.

Modeling of pathogen survival during simulated gastric digestion.

PubMed

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-02-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

Modeling of Pathogen Survival during Simulated Gastric Digestion ▿

PubMed Central

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-01-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens. PMID:21131530

Identifying crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Kinetic models for batch ethanol production from sweet sorghum juice under normal and high gravity fermentations: Logistic and modified Gompertz models.

PubMed

Phukoetphim, Niphaphat; Salakkam, Apilak; Laopaiboon, Pattana; Laopaiboon, Lakkana

2017-02-10

The aim of this study was to model batch ethanol production from sweet sorghum juice (SSJ), under normal gravity (NG, 160g/L of total sugar) and high gravity (HG, 240g/L of total sugar) conditions with and without nutrient supplementation (9g/L of yeast extract), by Saccharomyces cerevisiae NP 01. Growth and ethanol production increased with increasing initial sugar concentration, and the addition of yeast extract enhanced both cell growth and ethanol production. From the results, either logistic or a modified Gompertz equation could be used to describe yeast growth, depending on information required. Furthermore, the modified Gompertz model was suitable for modeling ethanol production. Both the models fitted the data very well with coefficients of determination exceeding 0.98. The results clearly showed that these models can be employed in the development of ethanol production processes using SSJ under both NG and HG conditions. The models were also shown to be applicable to other ethanol fermentation systems employing pure and mixed sugars as carbon sources. Copyright © 2016 Elsevier B.V. All rights reserved.

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

Earthworks logistics in the high density urban development conditions - case study

NASA Astrophysics Data System (ADS)

Sobotka, A.; Blajer, M.

2017-10-01

Realisation of the construction projects on highly urbanised areas carries many difficulties and logistic problems. Earthworks conducted in such conditions constitute a good example of how important it is to properly plan the works and use the technical means of the logistics infrastructure. The construction processes on the observed construction site, in combination with their external logistics service are a complex system, difficult for mathematical modelling and achievement of appropriate data for planning the works. The paper shows describe and analysis of earthworks during construction of the Centre of Power Engineering of AGH in Krakow for two stages of a construction project. At the planning stage in the preparatory phase (before realization) and in the implementation phase of construction works (foundation). In the first case, an example of the use of queuing theory for prediction of excavation time under random work conditions of the excavator and the associated trucks is provided. In the second case there is a change of foundation works technology resulting as a consequence of changes in logistics earthworks. Observation of the construction has confirmed that the use of appropriate methods of construction works management, and in this case agile management, the time and cost of the project have not been exceeded. The success of a project depends on the ability of the contractor to react quickly when changes occur in the design, technology, environment, etc.

Space Operations Center orbit altitude selection strategy

NASA Technical Reports Server (NTRS)

Indrikis, J.; Myers, H. L.

1982-01-01

The strategy for the operational altitude selection has to respond to the Space Operation Center's (SOC) maintenance requirements and the logistics demands of the missions to be supported by the SOC. Three orbit strategies are developed: two are constant altitude, and one variable altitude. In order to minimize the effect of atmospheric uncertainty the dynamic altitude method is recommended. In this approach the SOC will operate at the optimum altitude for the prevailing atmospheric conditions and logistics model, provided that mission safety constraints are not violated. Over a typical solar activity cycle this method produces significant savings in the overall logistics cost.

A Comparison of Strategies for Estimating Conditional DIF

ERIC Educational Resources Information Center

Moses, Tim; Miao, Jing; Dorans, Neil J.

2010-01-01

In this study, the accuracies of four strategies were compared for estimating conditional differential item functioning (DIF), including raw data, logistic regression, log-linear models, and kernel smoothing. Real data simulations were used to evaluate the estimation strategies across six items, DIF and No DIF situations, and four sample size…

Stochastic foundations in nonlinear density-regulation growth

NASA Astrophysics Data System (ADS)

Méndez, Vicenç; Assaf, Michael; Horsthemke, Werner; Campos, Daniel

2017-08-01

In this work we construct individual-based models that give rise to the generalized logistic model at the mean-field deterministic level and that allow us to interpret the parameters of these models in terms of individual interactions. We also study the effect of internal fluctuations on the long-time dynamics for the different models that have been widely used in the literature, such as the theta-logistic and Savageau models. In particular, we determine the conditions for population extinction and calculate the mean time to extinction. If the population does not become extinct, we obtain analytical expressions for the population abundance distribution. Our theoretical results are based on WKB theory and the probability generating function formalism and are verified by numerical simulations.

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

NASA Astrophysics Data System (ADS)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry.

PubMed

Zhu, Xiaoyan; Li, Xueping; Yao, Qingzhu; Chen, Yuerong

2011-01-01

This paper analyzed the uniqueness and challenges in designing the logistics system for dedicated biomass-to-bioenergy industry, which differs from the other industries, due to the unique features of dedicated biomass (e.g., switchgrass) including its low bulk density, restrictions on harvesting season and frequency, content variation with time and circumambient conditions, weather effects, scattered distribution over a wide geographical area, and so on. To design it, this paper proposed a mixed integer linear programming model. It covered from planting and harvesting switchgrass to delivering to a biorefinery and included the residue handling, concentrating on integrating strategic decisions on the supply chain design and tactical decisions on the annual operation schedules. The present numerical examples verified the model and demonstrated its use in practice. This paper showed that the operations of the logistics system were significantly different for harvesting and non-harvesting seasons, and that under the well-designed biomass logistics system, the mass production with a steady and sufficient supply of biomass can increase the unit profit of bioenergy. The analytical model and practical methodology proposed in this paper will help realize the commercial production in biomass-to-bioenergy industry. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Focused Logistics and Support for Force Projection in Force XXI and Beyond

DTIC Science & Technology

1999-12-09

business system linking trading partners with point of sale demand and real time manufacturing for clothing items.17 Quick Response achieved $1.7...be able to determine the real - time status and supply requirements of units. With "distributed logistics system software model hosts൨ and active...location, quantity, condition, and movement of assets. The system is designed to be fully automated, operate in near- real time with an open-architecture

Research on configuration of railway self-equipped tanker based on minimum cost maximum flow model

NASA Astrophysics Data System (ADS)

Yang, Yuefang; Gan, Chunhui; Shen, Tingting

2017-05-01

In the study of the configuration of the tanker of chemical logistics park, the minimum cost maximum flow model is adopted. Firstly, the transport capacity of the park loading and unloading area and the transportation demand of the dangerous goods are taken as the constraint condition of the model; then the transport arc capacity, the transport arc flow and the transport arc edge weight are determined in the transportation network diagram; finally, the software calculations. The calculation results show that the configuration issue of the tankers can be effectively solved by the minimum cost maximum flow model, which has theoretical and practical application value for tanker management of railway transportation of dangerous goods in the chemical logistics park.

Consistency of Rasch Model Parameter Estimation: A Simulation Study.

ERIC Educational Resources Information Center

van den Wollenberg, Arnold L.; And Others

1988-01-01

The unconditional--simultaneous--maximum likelihood (UML) estimation procedure for the one-parameter logistic model produces biased estimators. The UML method is inconsistent and is not a good alternative to conditional maximum likelihood method, at least with small numbers of items. The minimum Chi-square estimation procedure produces unbiased…

Optimization of lipids' ultrasonic extraction and production from Chlorella sp. using response-surface methodology.

PubMed

Hadrich, Bilel; Akremi, Ismahen; Dammak, Mouna; Barkallah, Mohamed; Fendri, Imen; Abdelkafi, Slim

2018-04-17

Three steps are very important in order to produce microalgal lipids: (1) controlling microalgae cultivation via experimental and modeling investigations, (2) optimizing culture conditions to maximize lipids production and to determine the fatty acid profile the most appropriate for biodiesel synthesis, and (3) optimizing the extraction of the lipids accumulated in the microalgal cells. Firstly, three kinetics models, namely logistic, logistic-with-lag and modified Gompertz, were tested to fit the experimental kinetics of the Chlorella sp. microalga culture established on standard conditions. Secondly, the response-surface methodology was used for two optimizations in this study. The first optimization was established for lipids production from Chlorella sp. culture under different culture conditions. In fact, different levels of nitrate concentrations, salinities and light intensities were applied to the culture medium in order to study their influences on lipids production and determine their fatty acid profile. The second optimization was concerned with the lipids extraction factors: ultrasonic's time and temperature, and chloroform-methanol solvent ratio. All models (logistic, logistic-with-lag and modified Gompertz) applied for the experimental kinetics of Chlorella sp. show a very interesting fitting quality. The logistic model was chosen to describe the Chlorella sp. kinetics, since it yielded the most important statistical criteria: coefficient of determination of the order of 94.36%; adjusted coefficient of determination equal to 93.79% and root mean square error reaching 3.685 cells · ml - 1 . Nitrate concentration and the two interactions involving the light intensity (Nitrate concentration × light intensity, and salinities × light intensity) showed a very significant influence on lipids production in the first optimization (p < 0.05). Yet, only the quadratic term of chloroform-methanol solvent ratio showed a significant influence on lipids extraction relative to the second step of optimization (p < 0.05). The two most abundant fatty acid methyl esters (≈72%) derived from the Chlorella sp. microalga cultured in the determined optimal conditions are: palmitic acid (C16:0) and oleic acid (C18:1) with the corresponding yields of 51.69% and 20.55% of total fatty acids, respectively. Only the nitrate deficiency and the high intensity of light can influence the microalgal lipids production. The corresponding fatty acid methyl esters composition is very suitable for biodiesel production. Lipids extraction is efficient only over long periods of time when using a solvent with a 2/1 chloroform/methanol ratio.

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

PubMed Central

Zhang, Dezhi; Li, Shuangyan

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

PubMed

Zhang, Dezhi; Li, Shuangyan; Qin, Jin

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

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

PubMed

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

2017-09-06

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

Semiparametric time varying coefficient model for matched case-crossover studies.

PubMed

Ortega-Villa, Ana Maria; Kim, Inyoung; Kim, H

2017-03-15

In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. This is because any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. However, some matching covariates such as time often play an important role as an effect modification leading to incorrect statistical estimation and prediction. Therefore, we propose three approaches to evaluate effect modification by time. The first is a parametric approach, the second is a semiparametric penalized approach, and the third is a semiparametric Bayesian approach. Our parametric approach is a two-stage method, which uses conditional logistic regression in the first stage and then estimates polynomial regression in the second stage. Our semiparametric penalized and Bayesian approaches are one-stage approaches developed by using regression splines. Our semiparametric one stage approach allows us to not only detect the parametric relationship between the predictor and binary outcomes, but also evaluate nonparametric relationships between the predictor and time. We demonstrate the advantage of our semiparametric one-stage approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. We also provide statistical inference for the semiparametric Bayesian approach using Bayes Factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Testing Gene-Gene Interactions in the Case-Parents Design

PubMed Central

Yu, Zhaoxia

2011-01-01

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used. PMID:21778736

Work performance decrements are associated with Australian working conditions, particularly the demand to work longer hours.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Vecchio, Nerina N; Whiteford, Harvey A

2010-03-01

To demonstrate the importance of including a range of working conditions in models exploring the association between health- and work-related performance. The Australian Work Outcomes Research Cost-benefit study cross-sectional screening data set was used to explore health-related absenteeism and work performance losses on a sample of approximately 78,000 working Australians, including available demographic and working condition factors. Data collected using the World Health Organization Health and Productivity Questionnaire were analyzed with negative binomial logistic regression and multinomial logistic regressions for absenteeism and work performance, respectively. Hours expected to work, annual wage, and job insecurity play a vital role in the association between health- and work-related performance for both work attendance and self-reported work performance. Australian working conditions are contributing to both absenteeism and low work performance, regardless of health status.

U.S. Emergency Department Admissions for Nontraumatic Dental Conditions for Individuals with Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Chi, Donald L.; Masterson, Erin E.; Wong, Jacqueline J.

2014-01-01

The authors hypothesized that individuals with intellectual and developmental disabilities (IDDs) are more likely to have an emergency department (ED) admission for nontraumatic dental conditions (NTDCs). The authors analyzed 2009 U.S. National Emergency Department Sample data and ran logistic regression models for children ages 3-17 years and…

Sensitivity to initial conditions in the Bak-Sneppen model of biological evolution

NASA Astrophysics Data System (ADS)

Tamarit, F. A.; Cannas, S. A.; Tsallis, C.

1998-03-01

We consider biological evolution as described within the Bak and Sneppen 1993 model. We exhibit, at the self-organized critical state, a power-law sensitivity to the initial conditions, calculate the associated exponent, and relate it to the recently introduced nonextensive thermostatistics. The scenario which here emerges without tuning strongly reminds of that of the tuned onset of chaos in say logistic-like one-dimensional maps. We also calculate the dynamical exponent z.

Addressing data privacy in matched studies via virtual pooling.

PubMed

Saha-Chaudhuri, P; Weinberg, C R

2017-09-07

Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distributed regression, meta-analysis and score-based methods can have important limitations. We propose a novel application of an existing epidemiologic tool, specimen pooling, to enable confidentiality-preserving analysis of data arising from a matched case-control, multi-center design. Instead of pooling specimens prior to assay, we apply the methodology to virtually pool (aggregate) covariates within nodes. Such virtual pooling retains most of the information used in an analysis with individual data and since individual participant data is not shared externally, within-node virtual pooling preserves data confidentiality. We show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratios of interest. The parameter estimates from the standard conditional logistic regression are compared to the estimates based on a conditional logistic regression model with aggregated data. The parameter estimates are shown to be similar to those without pooling and to have comparable standard errors and confidence interval coverage. Virtual data pooling can be used to maintain confidentiality of data from multi-center study and can be particularly useful in research with large-scale distributed data.

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network

PubMed Central

Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

2016-01-01

The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. PMID:27195005

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

PubMed

Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

2016-01-01

The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

A longitudinal analysis of the influence of the neighborhood built environment on walking for transportation: the RESIDE study.

PubMed

Knuiman, Matthew W; Christian, Hayley E; Divitini, Mark L; Foster, Sarah A; Bull, Fiona C; Badland, Hannah M; Giles-Corti, Billie

2014-09-01

The purpose of the present analysis was to use longitudinal data collected over 7 years (from 4 surveys) in the Residential Environments (RESIDE) Study (Perth, Australia, 2003-2012) to more carefully examine the relationship of neighborhood walkability and destination accessibility with walking for transportation that has been seen in many cross-sectional studies. We compared effect estimates from 3 types of logistic regression models: 2 that utilize all available data (a population marginal model and a subject-level mixed model) and a third subject-level conditional model that exclusively uses within-person longitudinal evidence. The results support the evidence that neighborhood walkability (especially land-use mix and street connectivity), local access to public transit stops, and variety in the types of local destinations are important determinants of walking for transportation. The similarity of subject-level effect estimates from logistic mixed models and those from conditional logistic models indicates that there is little or no bias from uncontrolled time-constant residential preference (self-selection) factors; however, confounding by uncontrolled time-varying factors, such as health status, remains a possibility. These findings provide policy makers and urban planners with further evidence that certain features of the built environment may be important in the design of neighborhoods to increase walking for transportation and meet the health needs of residents. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

PubMed

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

2006-01-01

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

Rethinking the logistic approach for population dynamics of mutualistic interactions.

PubMed

García-Algarra, Javier; Galeano, Javier; Pastor, Juan Manuel; Iriondo, José María; Ramasco, José J

2014-12-21

Mutualistic communities have an internal structure that makes them resilient to external perturbations. Late research has focused on their stability and the topology of the relations between the different organisms to explain the reasons of the system robustness. Much less attention has been invested in analyzing the systems dynamics. The main population models in use are modifications of the r-K formulation of logistic equation with additional terms to account for the benefits produced by the interspecific interactions. These models have shortcomings as the so-called r-K formulation diverges under some conditions. In this work, we introduce a model for population dynamics under mutualism that preserves the original logistic formulation. It is mathematically simpler than the widely used type II models, although it shows similar complexity in terms of fixed points and stability of the dynamics. We perform an analytical stability analysis and numerical simulations to study the model behavior in general interaction scenarios including tests of the resilience of its dynamics under external perturbations. Despite its simplicity, our results indicate that the model dynamics shows an important richness that can be used to gain further insights in the dynamics of mutualistic communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

LANDSCAPE METRICS THAT ARE USEFUL FOR EXPLAINING ESTUARINE ECOLOGICAL RESPONSES

EPA Science Inventory

We investigated whether land use/cover characteristics of watersheds associated with estuaries exhibit a strong enough signal to make landscape metrics useful for predicting estuarine ecological condition. We used multivariate logistic regression models to discriminate between su...

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

An Experimental Realization of a Chaos-Based Secure Communication Using Arduino Microcontrollers.

PubMed

Zapateiro De la Hoz, Mauricio; Acho, Leonardo; Vidal, Yolanda

2015-01-01

Security and secrecy are some of the important concerns in the communications world. In the last years, several encryption techniques have been proposed in order to improve the secrecy of the information transmitted. Chaos-based encryption techniques are being widely studied as part of the problem because of the highly unpredictable and random-look nature of the chaotic signals. In this paper we propose a digital-based communication system that uses the logistic map which is a mathematically simple model that is chaotic under certain conditions. The input message signal is modulated using a simple Delta modulator and encrypted using a logistic map. The key signal is also encrypted using the same logistic map with different initial conditions. In the receiver side, the binary-coded message is decrypted using the encrypted key signal that is sent through one of the communication channels. The proposed scheme is experimentally tested using Arduino shields which are simple yet powerful development kits that allows for the implementation of the communication system for testing purposes.

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

PubMed

Nepal, S K

2003-09-01

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

An investigation of the speeding-related crash designation through crash narrative reviews sampled via logistic regression.

PubMed

Fitzpatrick, Cole D; Rakasi, Saritha; Knodler, Michael A

2017-01-01

Speed is one of the most important factors in traffic safety as higher speeds are linked to increased crash risk and higher injury severities. Nearly a third of fatal crashes in the United States are designated as "speeding-related", which is defined as either "the driver behavior of exceeding the posted speed limit or driving too fast for conditions." While many studies have utilized the speeding-related designation in safety analyses, no studies have examined the underlying accuracy of this designation. Herein, we investigate the speeding-related crash designation through the development of a series of logistic regression models that were derived from the established speeding-related crash typologies and validated using a blind review, by multiple researchers, of 604 crash narratives. The developed logistic regression model accurately identified crashes which were not originally designated as speeding-related but had crash narratives that suggested speeding as a causative factor. Only 53.4% of crashes designated as speeding-related contained narratives which described speeding as a causative factor. Further investigation of these crashes revealed that the driver contributing code (DCC) of "driving too fast for conditions" was being used in three separate situations. Additionally, this DCC was also incorrectly used when "exceeding the posted speed limit" would likely have been a more appropriate designation. Finally, it was determined that the responding officer only utilized one DCC in 82% of crashes not designated as speeding-related but contained a narrative indicating speed as a contributing causal factor. The use of logistic regression models based upon speeding-related crash typologies offers a promising method by which all possible speeding-related crashes could be identified. Published by Elsevier Ltd.

Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Burgueño, Juan; Eskridge, Kent

2015-08-18

Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link. Copyright © 2015 Montesinos-López et al.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

PubMed

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Assessing Lake Trophic Status: A Proportional Odds Logistic Regression Model

EPA Science Inventory

Lake trophic state classifications are good predictors of ecosystem condition and are indicative of both ecosystem services (e.g., recreation and aesthetics), and disservices (e.g., harmful algal blooms). Methods for classifying trophic state are based off the foundational work o...

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

NASA Astrophysics Data System (ADS)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

A Comparison of Methods for Estimating Conditional Item Score Differences in Differential Item Functioning (DIF) Assessments. Research Report. ETS RR-10-15

ERIC Educational Resources Information Center

Moses, Tim; Miao, Jing; Dorans, Neil

2010-01-01

This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus…

Glucose-6-phosphate dehydrogenase deficiency and diabetes mellitus with severe retinal complications in a Sardinian population, Italy.

PubMed

Pinna, Antonio; Contini, Emma Luigia; Carru, Ciriaco; Solinas, Giuliana

2013-01-01

Glucose-6-Phosphate Dehydrogenase (G6PD) deficiency is one of the most common human genetic abnormalities, with a high prevalence in Sardinia, Italy. Evidence indicates that G6PD-deficient patients are protected against vascular disease. Little is known about the relationship between G6PD deficiency and diabetes mellitus. The purpose of this study was to compare G6PD deficiency prevalence in Sardinian diabetic men with severe retinal vascular complications and in age-matched non-diabetic controls and ascertain whether G6PD deficiency may offer protection against this vascular disorder. Erythrocyte G6PD activity was determined using a quantitative assay in 390 diabetic men with proliferative diabetic retinopathy (PDR) and 390 male non-diabetic controls, both aged ≥50 years. Conditional logistic regression models were used to investigate the association between G6PD deficiency and diabetes with severe retinal complications. G6PD deficiency was found in 21 (5.4 %) diabetic patients and 33 (8.5 %) controls (P=0.09). In a univariate conditional logistic regression model, G6PD deficiency showed a trend for protection against diabetes with PDR, but the odds ratio (OR) fell short of statistical significance (OR=0.6, 95% confidence interval=0.35-1.08, P=0.09). In multivariate conditional logistic regression models, including as covariates G6PD deficiency, plasma glucose, and systemic hypertension or systolic or diastolic blood pressure, G6PD deficiency showed no statistically significant protection against diabetes with PDR. The prevalence of G6PD deficiency in diabetic men with PDR was lower than in age-matched non-diabetic controls. G6PD deficiency showed a trend for protection against diabetes with PDR, but results were not statistically significant.

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China.

PubMed

Yuan, Quan; Lu, Meng; Theofilatos, Athanasios; Li, Yi-Bing

2017-02-01

Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle (FV). This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. Moreover, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers' age, weight difference between vehicles, visibility condition and lane number of road significantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers' age and drivers' original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beijing on weekdays. Copyright © 2016 Daping Hospital and the Research Institute of Surgery of the Third Military Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Classical conditioning through auditory stimuli in Drosophila: methods and models

PubMed Central

Menda, Gil; Bar, Haim Y.; Arthur, Ben J.; Rivlin, Patricia K.; Wyttenbach, Robert A.; Strawderman, Robert L.; Hoy, Ronald R.

2011-01-01

SUMMARY The role of sound in Drosophila melanogaster courtship, along with its perception via the antennae, is well established, as is the ability of this fly to learn in classical conditioning protocols. Here, we demonstrate that a neutral acoustic stimulus paired with a sucrose reward can be used to condition the proboscis-extension reflex, part of normal feeding behavior. This appetitive conditioning produces results comparable to those obtained with chemical stimuli in aversive conditioning protocols. We applied a logistic model with general estimating equations to predict the dynamics of learning, which successfully predicts the outcome of training and provides a quantitative estimate of the rate of learning. Use of acoustic stimuli with appetitive conditioning provides both an alternative to models most commonly used in studies of learning and memory in Drosophila and a means of testing hearing in both sexes, independently of courtship responsiveness. PMID:21832129

Dynamical analysis of cigarette smoking model with a saturated incidence rate

NASA Astrophysics Data System (ADS)

Zeb, Anwar; Bano, Ayesha; Alzahrani, Ebraheem; Zaman, Gul

2018-04-01

In this paper, we consider a delayed smoking model in which the potential smokers are assumed to satisfy the logistic equation. We discuss the dynamical behavior of our proposed model in the form of Delayed Differential Equations (DDEs) and show conditions for asymptotic stability of the model in steady state. We also discuss the Hopf bifurcation analysis of considered model. Finally, we use the nonstandard finite difference (NSFD) scheme to show the results graphically with help of MATLAB.

A Predictive Model for Readmissions Among Medicare Patients in a California Hospital.

PubMed

Duncan, Ian; Huynh, Nhan

2017-11-17

Predictive models for hospital readmission rates are in high demand because of the Centers for Medicare & Medicaid Services (CMS) Hospital Readmission Reduction Program (HRRP). The LACE index is one of the most popular predictive tools among hospitals in the United States. The LACE index is a simple tool with 4 parameters: Length of stay, Acuity of admission, Comorbidity, and Emergency visits in the previous 6 months. The authors applied logistic regression to develop a predictive model for a medium-sized not-for-profit community hospital in California using patient-level data with more specific patient information (including 13 explanatory variables). Specifically, the logistic regression is applied to 2 populations: a general population including all patients and the specific group of patients targeted by the CMS penalty (characterized as ages 65 or older with select conditions). The 2 resulting logistic regression models have a higher sensitivity rate compared to the sensitivity of the LACE index. The C statistic values of the model applied to both populations demonstrate moderate levels of predictive power. The authors also build an economic model to demonstrate the potential financial impact of the use of the model for targeting high-risk patients in a sample hospital and demonstrate that, on balance, whether the hospital gains or loses from reducing readmissions depends on its margin and the extent of its readmission penalties.

An Experimental Realization of a Chaos-Based Secure Communication Using Arduino Microcontrollers

PubMed Central

Zapateiro De la Hoz, Mauricio; Vidal, Yolanda

2015-01-01

Security and secrecy are some of the important concerns in the communications world. In the last years, several encryption techniques have been proposed in order to improve the secrecy of the information transmitted. Chaos-based encryption techniques are being widely studied as part of the problem because of the highly unpredictable and random-look nature of the chaotic signals. In this paper we propose a digital-based communication system that uses the logistic map which is a mathematically simple model that is chaotic under certain conditions. The input message signal is modulated using a simple Delta modulator and encrypted using a logistic map. The key signal is also encrypted using the same logistic map with different initial conditions. In the receiver side, the binary-coded message is decrypted using the encrypted key signal that is sent through one of the communication channels. The proposed scheme is experimentally tested using Arduino shields which are simple yet powerful development kits that allows for the implementation of the communication system for testing purposes. PMID:26413563

Familial aggregation and linkage analysis with covariates for metabolic syndrome risk factors.

PubMed

Naseri, Parisa; Khodakarim, Soheila; Guity, Kamran; Daneshpour, Maryam S

2018-06-15

Mechanisms of metabolic syndrome (MetS) causation are complex, genetic and environmental factors are important factors for the pathogenesis of MetS In this study, we aimed to evaluate familial and genetic influences on metabolic syndrome risk factor and also assess association between FTO (rs1558902 and rs7202116) and CETP(rs1864163) genes' single nucleotide polymorphisms (SNP) with low HDL_C in the Tehran Lipid and Glucose Study (TLGS). The design was a cross-sectional study of 1776 members of 227 randomly-ascertained families. Selected families contained at least one affected metabolic syndrome and at least two members of the family had suffered a loss of HDL_C according to ATP III criteria. In this study, after confirming the familial aggregation with intra-trait correlation coefficients (ICC) of Metabolic syndrome (MetS) and the quantitative lipid traits, the genetic linkage analysis of HDL_C was performed using conditional logistic method with adjusted sex and age. The results of the aggregation analysis revealed a higher correlation between siblings than between parent-offspring pairs representing the role of genetic factors in MetS. In addition, the conditional logistic model with covariates showed that the linkage results between HDL_C and three marker, rs1558902, rs7202116 and rs1864163 were significant. In summary, a high risk of MetS was found in siblings confirming the genetic influences of metabolic syndrome risk factor. Moreover, the power to detect linkage increases in the one parameter conditional logistic model regarding the use of age and sex as covariates. Copyright © 2018. Published by Elsevier B.V.

Application of conditional moment tests to model checking for generalized linear models.

PubMed

Pan, Wei

2002-06-01

Generalized linear models (GLMs) are increasingly being used in daily data analysis. However, model checking for GLMs with correlated discrete response data remains difficult. In this paper, through a case study on marginal logistic regression using a real data set, we illustrate the flexibility and effectiveness of using conditional moment tests (CMTs), along with other graphical methods, to do model checking for generalized estimation equation (GEE) analyses. Although CMTs provide an array of powerful diagnostic tests for model checking, they were originally proposed in the econometrics literature and, to our knowledge, have never been applied to GEE analyses. CMTs cover many existing tests, including the (generalized) score test for an omitted covariate, as special cases. In summary, we believe that CMTs provide a class of useful model checking tools.

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China

PubMed Central

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-01-01

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation. PMID:28367963

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China.

PubMed

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-04-03

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation.

Ranking Theory and Conditional Reasoning.

PubMed

Skovgaard-Olsen, Niels

2016-05-01

Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals. Copyright © 2015 Cognitive Science Society, Inc.

Probability and predictors of cannabis use disorders relapse: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

PubMed

Flórez-Salamanca, Ludwing; Secades-Villa, Roberto; Budney, Alan J; García-Rodríguez, Olaya; Wang, Shuai; Blanco, Carlos

2013-09-01

This study aims to estimate the odds and predictors of Cannabis Use Disorders (CUD) relapse among individuals in remission. Analyses were done on the subsample of individuals with lifetime history of a CUD (abuse or dependence) who were in full remission at baseline (Wave 1) of the National Epidemiological Survey of Alcohol and Related Conditions (NESARC) (n=2350). Univariate logistic regression models and hierarchical logistic regression model were implemented to estimate odds of relapse and identify predictors of relapse at 3 years follow up (Wave 2). The relapse rate of CUD was 6.63% over an average of 3.6 year follow-up period. In the multivariable model, the odds of relapse were inversely related to time in remission, whereas having a history of conduct disorder or a major depressive disorder after Wave 1 increased the risk of relapse. Our findings suggest that maintenance of remission is the most common outcome for individuals in remission from a CUD. Treatment approaches may improve rates of sustained remission of individuals with CUD and conduct disorder or major depressive disorder. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

PubMed Central

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.

PubMed

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

Evaluating 1-, 2- and 3- Parameter Logistic Models Using Model-Based and Empirically-Based Simulations under Homogeneous and Heterogeneous Set Conditions

ERIC Educational Resources Information Center

Rizavi, Saba; Way, Walter D.; Lu, Ying; Pitoniak, Mary; Steffen, Manfred

2004-01-01

The purpose of this study was to use realistically simulated data to evaluate various CAT designs for use with the verbal reasoning measure of the Medical College Admissions Test (MCAT). Factors such as item pool depth, content constraints, and item formats often cause repeated adaptive administrations of an item at ability levels that are not…

Logistic regression of family data from retrospective study designs.

PubMed

Whittemore, Alice S; Halpern, Jerry

2003-11-01

We wish to study the effects of genetic and environmental factors on disease risk, using data from families ascertained because they contain multiple cases of the disease. To do so, we must account for the way participants were ascertained, and for within-family correlations in both disease occurrences and covariates. We model the joint probability distribution of the covariates of ascertained family members, given family disease occurrence and pedigree structure. We describe two such covariate models: the random effects model and the marginal model. Both models assume a logistic form for the distribution of one person's covariates that involves a vector beta of regression parameters. The components of beta in the two models have different interpretations, and they differ in magnitude when the covariates are correlated within families. We describe ascertainment assumptions needed to estimate consistently the parameters beta(RE) in the random effects model and the parameters beta(M) in the marginal model. Under the ascertainment assumptions for the random effects model, we show that conditional logistic regression (CLR) of matched family data gives a consistent estimate beta(RE) for beta(RE) and a consistent estimate for the covariance matrix of beta(RE). Under the ascertainment assumptions for the marginal model, we show that unconditional logistic regression (ULR) gives a consistent estimate for beta(M), and we give a consistent estimator for its covariance matrix. The random effects/CLR approach is simple to use and to interpret, but it can use data only from families containing both affected and unaffected members. The marginal/ULR approach uses data from all individuals, but its variance estimates require special computations. A C program to compute these variance estimates is available at http://www.stanford.edu/dept/HRP/epidemiology. We illustrate these pros and cons by application to data on the effects of parity on ovarian cancer risk in mother/daughter pairs, and use simulations to study the performance of the estimates. Copyright 2003 Wiley-Liss, Inc.

Breast arterial calcification is associated with reproductive factors in asymptomatic postmenopausal women.

PubMed

Bielak, Lawrence F; Whaley, Dana H; Sheedy, Patrick F; Peyser, Patricia A

2010-09-01

The etiology of breast arterial calcification (BAC) is not well understood. We examined reproductive history and cardiovascular disease (CVD) risk factor associations with the presence of detectable BAC in asymptomatic postmenopausal women. Reproductive history and CVD risk factors were obtained in 240 asymptomatic postmenopausal women from a community-based research study who had a screening mammogram within 2 years of their participation in the study. The mammograms were reviewed for the presence of detectable BAC. Age-adjusted logistic regression models were fit to assess the association between each risk factor and the presence of BAC. Multiple variable logistic regression models were used to identify the most parsimonious model for the presence of BAC. The prevalence of BAC increased with increased age (p < 0.0001). The most parsimonious logistic regression model for BAC presence included age at time of examination, increased parity (p = 0.01), earlier age at first birth (p = 0.002), weight, and an age-by-weight interaction term (p = 0.004). Older women with a smaller body size had a higher probability of having BAC than women of the same age with a larger body size. The presence or absence of BAC at mammography may provide an assessment of a postmenopausal woman's lifetime estrogen exposure and indicate women who could be at risk for hormonally related conditions.

Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects.

PubMed

Li, Shi; Batterman, Stuart; Wasilevich, Elizabeth; Wahl, Robert; Wirth, Julie; Su, Feng-Chiao; Mukherjee, Bhramar

2011-11-01

Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships. This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs. Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression. This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk. Copyright © 2011 Elsevier Inc. All rights reserved.

Use of nonlinear models for describing scrotal circumference growth in Guzerat bulls raised under grazing conditions.

PubMed

Loaiza-Echeverri, A M; Bergmann, J A G; Toral, F L B; Osorio, J P; Carmo, A S; Mendonça, L F; Moustacas, V S; Henry, M

2013-03-15

The objective was to use various nonlinear models to describe scrotal circumference (SC) growth in Guzerat bulls on three farms in the state of Minas Gerais, Brazil. The nonlinear models were: Brody, Logistic, Gompertz, Richards, Von Bertalanffy, and Tanaka, where parameter A is the estimated testis size at maturity, B is the integration constant, k is a maturating index and, for the Richards and Tanaka models, m determines the inflection point. In Tanaka, A is an indefinite size of the testis, and B and k adjust the shape and inclination of the curve. A total of 7410 SC records were obtained every 3 months from 1034 bulls with ages varying between 2 and 69 months (<240 days of age = 159; 241-365 days = 451; 366-550 days = 1443; 551-730 days = 1705; and >731 days = 3652 SC measurements). Goodness of fit was evaluated by coefficients of determination (R(2)), error sum of squares, average prediction error (APE), and mean absolute deviation. The Richards model did not reach the convergence criterion. The R(2) were similar for all models (0.68-0.69). The error sum of squares was lowest for the Tanaka model. All models fit the SC data poorly in the early and late periods. Logistic was the model which best estimated SC in the early phase (based on APE and mean absolute deviation). The Tanaka and Logistic models had the lowest APE between 300 and 1600 days of age. The Logistic model was chosen for analysis of the environmental influence on parameters A and k. Based on absolute growth rate, SC increased from 0.019 cm/d, peaking at 0.025 cm/d between 318 and 435 days of age. Farm, year, and season of birth significantly affected size of adult SC and SC growth rate. An increase in SC adult size (parameter A) was accompanied by decreased SC growth rate (parameter k). In conclusion, SC growth in Guzerat bulls was characterized by an accelerated growth phase, followed by decreased growth; this was best represented by the Logistic model. The inflection point occurred at approximately 376 days of age (mean SC of 17.9 cm). We inferred that early selection of testicular size might result in smaller testes at maturity. Copyright © 2013 Elsevier Inc. All rights reserved.

Racial Threat and White Opposition to Bilingual Education in Texas

ERIC Educational Resources Information Center

Hempel, Lynn M.; Dowling, Julie A.; Boardman, Jason D.; Ellison, Christopher G.

2013-01-01

This study examines local contextual conditions that influence opposition to bilingual education among non-Hispanic Whites, net of individual-level characteristics. Data from the Texas Poll (N = 615) are used in conjunction with U.S. Census data to test five competing hypotheses using binomial and multinomial logistic regression models. Our…

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

PubMed

Houpt, Joseph W; Bittner, Jennifer L

2018-07-01

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

Occupational exposures and non-Hodgkin's lymphoma: Canadian case-control study.

PubMed

Karunanayake, Chandima P; McDuffie, Helen H; Dosman, James A; Spinelli, John J; Pahwa, Punam

2008-08-07

The objective was to study the association between Non-Hodgkin's Lymphoma (NHL) and occupational exposures related to long held occupations among males in six provinces of Canada. A population based case-control study was conducted from 1991 to 1994. Males with newly diagnosed NHL (ICD-10) were stratified by province of residence and age group. A total of 513 incident cases and 1506 population based controls were included in the analysis. Conditional logistic regression was conducted to fit statistical models. Based on conditional logistic regression modeling, the following factors independently increased the risk of NHL: farmer and machinist as long held occupations; constant exposure to diesel exhaust fumes; constant exposure to ionizing radiation (radium); and personal history of another cancer. Men who had worked for 20 years or more as farmer and machinist were the most likely to develop NHL. An increased risk of developing NHL is associated with the following: long held occupations of faer and machinist; exposure to diesel fumes; and exposure to ionizing radiation (radium). The risk of NHL increased with the duration of employment as a farmer or machinist.

Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…

Development of a predictive program for Vibrio parahaemolyticus growth under various environmental conditions.

PubMed

Fujikawa, Hiroshi; Kimura, Bon; Fujii, Tateo

2009-09-01

In this study, we developed a predictive program for Vibrio parahaemolyticus growth under various environmental conditions. Raw growth data was obtained with a V. parahaemolyticus O3:K6 strain cultured at a variety of broth temperatures, pH, and salt concentrations. Data were analyzed with our logistic model and the parameter values of the model were analyzed with polynomial equations. A prediction program consisting of the growth model and the polynomial equations was then developed. After the range of the growth environments was modified, the program successfully predicted the growth for all environments tested. The program could be a useful tool to ensure the bacteriological safety of seafood.

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

Public Health Analysis Transport Optimization Model v. 1.0

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

Beyeler, Walt; Finley, Patrick; Walser, Alex

PHANTOM models logistic functions of national public health systems. The system enables public health officials to visualize and coordinate options for public health surveillance, diagnosis, response and administration in an integrated analytical environment. Users may simulate and analyze system performance applying scenarios that represent current conditions or future contingencies what-if analyses of potential systemic improvements. Public health networks are visualized as interactive maps, with graphical displays of relevant system performance metrics as calculated by the simulation modeling components.

Relation of depression with health behaviors and social conditions of dependent community-dwelling older persons in the Republic of Chile.

PubMed

Sandoval Garrido, Felipe Alfonso; Tamiya, Nanako; Lloyd-Sherlock, Peter; Noguchi, Haruko

2016-12-01

Depressive symptoms are a leading cause of disability and emotional suffering, particularly in old age. However, evidence on depression and old age in developing countries remains largely ignored. The aim of this study was to examine the relation between health behavior and social conditions with depression among dependent community-dwelling older persons in the Republic of Chile. This is a cross-sectional and inferential study, using nationally representative secondary data. Two models used logistic regression on 640 dependent community-dwelling older persons from all over Chile, who personally answered a depression assessment, excluding those taking antidepressants. The geriatric depression scale (GDS-15) was used as outcome. The first model aims at any kind of depression (GDS 5>). The second aims at severe depression (GDS 10>). As exposure, we used the health behavior and social conditions of the older persons. Socio-demographic and physical conditions were used as adjustment. 44.5% of the older persons presented depressive symptoms. Among them, 11% had severe depression. Logistic regression showed that significant detrimental factors for being depressed in both models were visiting the doctor five times or over because of acute diseases, feeling uncomfortable with their living arrangement, and feeling discriminated. On the other hand, every additional day of physical exercise and living alone had a beneficial and detrimental effect only in model one. Analyses on ways to support older persons living alone and the promotion of physical exercise to avoid depression are needed, along with a deeper understanding of the comfort with their living arrangement. Finally, ways to address the discrimination among older persons should be further explored.

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Design logistics performance measurement model of automotive component industry for srengthening competitiveness of dealing AEC 2015

NASA Astrophysics Data System (ADS)

Amran, T. G.; Janitra Yose, Mindy

2018-03-01

As the free trade Asean Economic Community (AEC) causes the tougher competition, it is important that Indonesia’s automotive industry have high competitiveness as well. A model of logistics performance measurement was designed as an evaluation tool for automotive component companies to improve their logistics performance in order to compete in AEC. The design of logistics performance measurement model was based on the Logistics Scorecard perspectives, divided into two stages: identifying the logistics business strategy to get the KPI and arranging the model. 23 KPI was obtained. The measurement result can be taken into consideration of determining policies to improve the performance logistics competitiveness.

Advanced Targeting Cost Function Design for Evolutionary Optimization of Control of Logistic Equation

NASA Astrophysics Data System (ADS)

Senkerik, Roman; Zelinka, Ivan; Davendra, Donald; Oplatkova, Zuzana

2010-06-01

This research deals with the optimization of the control of chaos by means of evolutionary algorithms. This work is aimed on an explanation of how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing very fast and precise stabilization of desired state for any initial conditions. As a model of deterministic chaotic system, the one dimensional Logistic equation was used. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF.

Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

NASA Astrophysics Data System (ADS)

Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

2018-01-01

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

Research on support effectiveness modeling and simulating of aviation materiel autonomic logistics system

NASA Astrophysics Data System (ADS)

Zhou, Yan; Zhou, Yang; Yuan, Kai; Jia, Zhiyu; Li, Shuo

2018-05-01

Aiming at the demonstration of autonomic logistics system to be used at the new generation of aviation materiel in our country, the modeling and simulating method of aviation materiel support effectiveness considering autonomic logistics are studied. Firstly, this paper introduced the idea of JSF autonomic logistics and analyzed the influence of autonomic logistics on support effectiveness from aspects of reliability, false alarm rate, troubleshooting time, and support delay time and maintenance level. On this basis, the paper studies the modeling and simulating methods of support effectiveness considering autonomic logistics, and puts forward the maintenance support simulation process considering autonomic logistics. Finally, taking the typical aviation materiel as an example, this paper analyzes and verifies the above-mentioned support effectiveness modeling and simulating method of aviation materiel considering autonomic logistics.

SPD-based Logistics Management Model of Medical Consumables in Hospitals.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei; Yang, Shanlin

2016-10-01

With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China.

Modelling Status Food Security Households Disease Sufferers Pulmonary Tuberculosis Uses the Method Regression Logistics Binary

NASA Astrophysics Data System (ADS)

Wulandari, S. P.; Salamah, M.; Rositawati, A. F. D.

2018-04-01

Food security is the condition where the food fulfilment is managed well for the country till the individual. Indonesia is one of the country which has the commitment to create the food security becomes main priority. However, the food necessity becomes common thing means that it doesn’t care about nutrient standard and the health condition of family member, so in the fulfilment of food necessity also has to consider the disease suffered by the family member, one of them is pulmonary tuberculosa. From that reasons, this research is conducted to know the factors which influence on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya by using binary logistic regression method. The analysis result by using binary logistic regression shows that the variables wife latest education, house density and spacious house ventilation significantly affect on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya, where the wife education level is University/equivalent, the house density is eligible or 8 m2/person and spacious house ventilation 10% of the floor area has the opportunity to become food secure households amounted to 0.911089. While the chance of becoming food insecure households amounted to 0.088911. The model household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya has been conformable, and the overall percentages of those classifications are at 71.8%.

Evolution dynamics modeling and simulation of logistics enterprise's core competence based on service innovation

NASA Astrophysics Data System (ADS)

Yang, Bo; Tong, Yuting

2017-04-01

With the rapid development of economy, the development of logistics enterprises in China is also facing a huge challenge, especially the logistics enterprises generally lack of core competitiveness, and service innovation awareness is not strong. Scholars in the process of studying the core competitiveness of logistics enterprises are mainly from the perspective of static stability, not from the perspective of dynamic evolution to explore. So the author analyzes the influencing factors and the evolution process of the core competence of logistics enterprises, using the method of system dynamics to study the cause and effect of the evolution of the core competence of logistics enterprises, construct a system dynamics model of evolution of core competence logistics enterprises, which can be simulated by vensim PLE. The analysis for the effectiveness and sensitivity of simulation model indicates the model can be used as the fitting of the evolution process of the core competence of logistics enterprises and reveal the process and mechanism of the evolution of the core competence of logistics enterprises, and provide management strategies for improving the core competence of logistics enterprises. The construction and operation of computer simulation model offers a kind of effective method for studying the evolution of logistics enterprise core competence.

Existence and uniqueness of steady state solutions of a nonlocal diffusive logistic equation

NASA Astrophysics Data System (ADS)

Sun, Linan; Shi, Junping; Wang, Yuwen

2013-08-01

In this paper, we consider a dynamical model of population biology which is of the classical Fisher type, but the competition interaction between individuals is nonlocal. The existence, uniqueness, and stability of the steady state solution of the nonlocal problem on a bounded interval with homogeneous Dirichlet boundary conditions are studied.

The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

ERIC Educational Resources Information Center

Anil, Duygu

2008-01-01

In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

The Use of Logistic Model in RUL Assessment

NASA Astrophysics Data System (ADS)

Gumiński, R.; Radkowski, S.

2017-12-01

The paper takes on the issue of assessment of remaining useful life (RUL). The goal of the paper was to develop a method, which would enable use of diagnostic information in the task of reducing the uncertainty related to technical risk. Prediction of the remaining useful life (RUL) of the system is a very important task for maintenance strategy. In the literature RUL of an engineering system is defined as the first future time instant in which thresholds of conditions (safety, operational quality, maintenance cost, etc) are violated. Knowledge of RUL offers the possibility of planning the testing and repair activities. Building models of damage development is important in this task. In the presented work, logistic function will be used to model fatigue crack development. It should be remembered that modeling of every phase of damage development is very difficult, yet modeling of every phase of damage separately, especially including on-line diagnostic information is more effective. Particular attention was paid to the possibility of forecasting the occurrence of damage due to fatigue while relying on the analysis of the structure of a vibroacoustic signal.

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

PubMed

Bennema, S C; Molento, M B; Scholte, R G; Carvalho, O S; Pritsch, I

2017-11-01

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

Breast Arterial Calcification Is Associated with Reproductive Factors in Asymptomatic Postmenopausal Women

PubMed Central

Whaley, Dana H.; Sheedy, Patrick F.; Peyser, Patricia A.

2010-01-01

Abstract Objective The etiology of breast arterial calcification (BAC) is not well understood. We examined reproductive history and cardiovascular disease (CVD) risk factor associations with the presence of detectable BAC in asymptomatic postmenopausal women. Methods Reproductive history and CVD risk factors were obtained in 240 asymptomatic postmenopausal women from a community-based research study who had a screening mammogram within 2 years of their participation in the study. The mammograms were reviewed for the presence of detectable BAC. Age-adjusted logistic regression models were fit to assess the association between each risk factor and the presence of BAC. Multiple variable logistic regression models were used to identify the most parsimonious model for the presence of BAC. Results The prevalence of BAC increased with increased age (p < 0.0001). The most parsimonious logistic regression model for BAC presence included age at time of examination, increased parity (p = 0.01), earlier age at first birth (p = 0.002), weight, and an age-by-weight interaction term (p = 0.004). Older women with a smaller body size had a higher probability of having BAC than women of the same age with a larger body size. Conclusions The presence or absence of BAC at mammography may provide an assessment of a postmenopausal woman's lifetime estrogen exposure and indicate women who could be at risk for hormonally related conditions. PMID:20629578

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

PubMed

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

2012-09-01

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

Mathematical assessment of the role of temperature and rainfall on mosquito population dynamics.

PubMed

Abdelrazec, Ahmed; Gumel, Abba B

2017-05-01

A new stage-structured model for the population dynamics of the mosquito (a major vector for numerous vector-borne diseases), which takes the form of a deterministic system of non-autonomous nonlinear differential equations, is designed and used to study the effect of variability in temperature and rainfall on mosquito abundance in a community. Two functional forms of eggs oviposition rate, namely the Verhulst-Pearl logistic and Maynard-Smith-Slatkin functions, are used. Rigorous analysis of the autonomous version of the model shows that, for any of the oviposition functions considered, the trivial equilibrium of the model is locally- and globally-asymptotically stable if a certain vectorial threshold quantity is less than unity. Conditions for the existence and global asymptotic stability of the non-trivial equilibrium solutions of the model are also derived. The model is shown to undergo a Hopf bifurcation under certain conditions (and that increased density-dependent competition in larval mortality reduces the likelihood of such bifurcation). The analyses reveal that the Maynard-Smith-Slatkin oviposition function sustains more oscillations than the Verhulst-Pearl logistic function (hence, it is more suited, from ecological viewpoint, for modeling the egg oviposition process). The non-autonomous model is shown to have a globally-asymptotically stable trivial periodic solution, for each of the oviposition functions, when the associated reproduction threshold is less than unity. Furthermore, this model, in the absence of density-dependent mortality rate for larvae, has a unique and globally-asymptotically stable periodic solution under certain conditions. Numerical simulations of the non-autonomous model, using mosquito surveillance and weather data from the Peel region of Ontario, Canada, show a peak mosquito abundance for temperature and rainfall values in the range [Formula: see text]C and [15-35] mm, respectively. These ranges are recorded in the Peel region between July and August (hence, this study suggests that anti-mosquito control effects should be intensified during this period).

[Development of a predictive program for microbial growth under various temperature conditions].

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi; Kimura, Bon; Fujii, Tateo

2006-12-01

A predictive program for microbial growth under various temperature conditions was developed with a mathematical model. The model was a new logistic model recently developed by us. The program predicts Escherichia coli growth in broth, Staphylococcus aureus growth and its enterotoxin production in milk, and Vibrio parahaemolyticus growth in broth at various temperature patterns. The program, which was built with Microsoft Excel (Visual Basic Application), is user-friendly; users can easily input the temperature history of a test food and obtain the prediction instantly on the computer screen. The predicted growth and toxin production can be important indices to determine whether a food is microbiologically safe or not. This program should be a useful tool to confirm the microbial safety of commercial foods.

Research on JD e-commerce's delivery model

NASA Astrophysics Data System (ADS)

Fan, Zhiguo; Ma, Mengkun; Feng, Chaoying

2017-03-01

E-commerce enterprises represented by JD have made a great contribution to the economic growth and economic development of our country. Delivery, as an important part of logistics, has self-evident importance. By establishing efficient and perfect self-built logistics systems and building good cooperation models with third-party logistics enterprises, e-commerce enterprises have created their own logistics advantages. Characterized by multi-batch and small-batch, e-commerce is much more complicated than traditional transaction. It's not easy to decide which delivery model e-commerce enterprises should adopt. Having e-commerce's logistics delivery as the main research object, this essay aims to find a more suitable logistics delivery model for JD's development.

SPD-based Logistics Management Model of Medical Consumables in Hospitals

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei; YANG, Shanlin

2016-01-01

Background: With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. Methods: We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. Results: We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. Conclusion: The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China. PMID:27957435

Selected Logistics Models and Techniques.

DTIC Science & Technology

1984-09-01

TI - 59 Programmable Calculator LCC...Program 27 TI - 59 Programmable Calculator LCC Model 30 Unmanned Spacecraft Cost Model 31 iv I: TABLE OF CONTENTS (CONT’D) (Subject Index) LOGISTICS...34"" - % - "° > - " ° .° - " .’ > -% > ]*° - LOGISTICS ANALYSIS MODEL/TECHNIQUE DATA MODEL/TECHNIQUE NAME: TI - 59 Programmable Calculator LCC Model TYPE MODEL: Cost Estimating DEVELOPED BY:

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs)

PubMed Central

2015-01-01

Background Over the past 50,000 years, shifts in human-environmental or human-human interactions shaped genetic differences within and among human populations, including variants under positive selection. Shaped by environmental factors, such variants influence the genetics of modern health, disease, and treatment outcome. Because evolutionary processes tend to act on gene regulation, we test whether regulatory variants are under positive selection. We introduce a new approach to enhance detection of genetic markers undergoing positive selection, using conditional entropy to capture recent local selection signals. Results We use conditional logistic regression to compare our Adjusted Haplotype Conditional Entropy (H|H) measure of positive selection to existing positive selection measures. H|H and existing measures were applied to published regulatory variants acting in cis (cis-eQTLs), with conditional logistic regression testing whether regulatory variants undergo stronger positive selection than the surrounding gene. These cis-eQTLs were drawn from six independent studies of genotype and RNA expression. The conditional logistic regression shows that, overall, H|H is substantially more powerful than existing positive-selection methods in identifying cis-eQTLs against other Single Nucleotide Polymorphisms (SNPs) in the same genes. When broken down by Gene Ontology, H|H predictions are particularly strong in some biological process categories, where regulatory variants are under strong positive selection compared to the bulk of the gene, distinct from those GO categories under overall positive selection. . However, cis-eQTLs in a second group of genes lack positive selection signatures detectable by H|H, consistent with ancient short haplotypes compared to the surrounding gene (for example, in innate immunity GO:0042742); under such other modes of selection, H|H would not be expected to be a strong predictor.. These conditional logistic regression models are adjusted for Minor allele frequency(MAF); otherwise, ascertainment bias is a huge factor in all eQTL data sets. Relationships between Gene Ontology categories, positive selection and eQTL specificity were replicated with H|H in a single larger data set. Our measure, Adjusted Haplotype Conditional Entropy (H|H), was essential in generating all of the results above because it: 1) is a stronger overall predictor for eQTLs than comparable existing approaches, and 2) shows low sequential auto-correlation, overcoming problems with convergence of these conditional regression statistical models. Conclusions Our new method, H|H, provides a consistently more robust signal associated with cis-eQTLs compared to existing methods. We interpret this to indicate that some cis-eQTLs are under positive selection compared to their surrounding genes. Conditional entropy indicative of a selective sweep is an especially strong predictor of eQTLs for genes in several biological processes of medical interest. Where conditional entropy is a weak or negative predictor of eQTLs, such as innate immune genes, this would be consistent with balancing selection acting on such eQTLs over long time periods. Different measures of selection may be needed for variant prioritization under other modes of evolutionary selection. PMID:26111110

Conditions for the return and simulation of the recovery of burrowing mayflies in western Lake Erie

USGS Publications Warehouse

Kolar, Cynthia S.; Hudson, Patrick L.; Savino, Jacqueline F.

1997-01-01

In the 1950s, burrowing mayflies, Hexagenia spp. (H. Limbata and H. Rigida), were virtually eliminated from the western basin of Lake Erie (a 3300 kmA? area) because of eutrophication and pollution. We develop and present a deterministic model for the recolonization of the western basin by Hexagenia to pre-1953 densities. The model was based on the logistic equation describing the population growth of Hexagenia and a presumed competitor, Chironomus (dipteran larvae). Other parameters (immigration, low oxygen, toxic sediments, competition with Chironomus, and fish predation) were then individually added to the logistic model to determine their effect at different growth rates. The logistic model alone predicts 10-41 yr for Hexagenia to recolonize western Lake Erie. Immigration reduced the recolonization time by 2-17 yr. One low-oxygen event during the first 20 yr increased recovery time by 5-17 yr. Contaminated sediments added 5-11 yr to the recolonization time. Competition with Chironomus added 8-19 yr to recovery. Fish predators added 4-47 yr to the time required for recolonization. The full model predicted 48-81 yr for Hexagenia to reach a carrying capacity of approximately 350 nymphs/mA?, or not until around the year 2038 if the model is started in 1990. The model was verified by changing model parameters to those present in 1970, beginning the model in 1970 and running it through 1990. Predicted densities overlapped almost completely with actual estimated densities of Hexagenia nymphs present in the western basin in Lake Erie in 1990. The model suggests that recovery of large aquatic ecosystems may lag substantially behind remediation efforts.

Bifurcation approach to a logistic elliptic equation with a homogeneous incoming flux boundary condition

NASA Astrophysics Data System (ADS)

Umezu, Kenichiro

In this paper, we consider a semilinear elliptic boundary value problem in a smooth bounded domain, having the so-called logistic nonlinearity that originates from population dynamics, with a nonlinear boundary condition. Although the logistic nonlinearity has an absorption effect in the problem, the nonlinear boundary condition is induced by the homogeneous incoming flux on the boundary. The objective of our study is to analyze the existence of a bifurcation component of positive solutions from trivial solutions and its asymptotic behavior and stability. We perform this analysis using the method developed by Lyapunov and Schmidt, based on a scaling argument.

Association between bullous pemphigoid and neurologic diseases: a case-control study.

PubMed

Casas-de-la-Asunción, E; Ruano-Ruiz, J; Rodríguez-Martín, A M; Vélez García-Nieto, A; Moreno-Giménez, J C

2014-11-01

In the past 10 years, bullous pemphigoid has been associated with other comorbidities and neurologic and psychiatric conditions in particular. Case series, small case-control studies, and large population-based studies in different Asian populations, mainland Europe, and the United Kingdom have confirmed this association. However, no data are available for the Spanish population. This was an observational, retrospective, case-control study with 1:2 matching. Fifty-four patients with bullous pemphigoid were selected. We compared the percentage of patients in each group with concurrent neurologic conditions, ischemic heart disease, diabetes, chronic obstructive pulmonary disease, and solid tumors using univariate logistic regression. An association model was constructed with conditional multiple logistic regression. The case group had a significantly higher percentage of patients with cerebrovascular accident and/or transient ischemic attack (odds ratio [OR], 3.06; 95% CI, 1.19-7.87], dementia (OR, 5.52; 95% CI, 2.19-13.93), and Parkinson disease (OR, 5; 95% CI, 1.57-15.94). A significantly higher percentage of cases had neurologic conditions (OR, 6.34; 95% CI, 2.89-13.91). Dementia and Parkinson disease were independently associated with bullous pemphigoid in the multivariate analysis. Patients with bullous pemphigoid have a higher frequency of neurologic conditions. Copyright © 2013 Elsevier España, S.L.U. and AEDV. All rights reserved.

Health-related productivity losses increase when the health condition is co-morbid with psychological distress: findings from a large cross-sectional sample of working Australians.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Ware, Robert S; Vecchio, Nerina; Whiteford, Harvey A

2011-05-31

The health condition of workers is known to impact on productivity outcomes. The relationship between health and productivity is of increasing interest amid the need to increase productivity to meet global financial challenges. Prevalence of psychological distress is also of growing concern in Australia with a two-fold increase in the prevalence of psychological distress in Australia from 1997-2005. We used the cross-sectional data set from the Australian Work Outcomes Research Cost-benefit (WORC) study to explore the impacts of health conditions with and without co-morbid psychological distress, compared to those with neither condition, in a sample of approximately 78,000 working Australians. The World Health Organisation Health and Performance Questionnaire was used which provided data on demographic characteristics, health condition and working conditions. Data were analysed using negative binomial logistic regression and multinomial logistic regression models for absenteeism and presenteeism respectively. For both absenteeism and presenteeism productivity measures there was a greater risk of productivity loss associated when health conditions were co-morbid with psychological distress. For some conditions this risk was much greater for those with co-morbid psychological distress compared to those without. Co-morbid psychological distress demonstrates an increased risk of productivity loss for a range of health conditions. These findings highlight the need for further research to determine whether co-morbid psychological distress potentially exacerbates lost productivity.

A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization

NASA Technical Reports Server (NTRS)

Aziz, Jonathan D.; Scheeres, Daniel J.; Parker, Jeffrey S.; Englander, Jacob A.

2017-01-01

Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.

Analysing biomass torrefaction supply chain costs.

PubMed

Svanberg, Martin; Olofsson, Ingemar; Flodén, Jonas; Nordin, Anders

2013-08-01

The objective of the present work was to develop a techno-economic system model to evaluate how logistics and production parameters affect the torrefaction supply chain costs under Swedish conditions. The model consists of four sub-models: (1) supply system, (2) a complete energy and mass balance of drying, torrefaction and densification, (3) investment and operating costs of a green field, stand-alone torrefaction pellet plant, and (4) distribution system to the gate of an end user. The results show that the torrefaction supply chain reaps significant economies of scale up to a plant size of about 150-200 kiloton dry substance per year (ktonDS/year), for which the total supply chain costs accounts to 31.8 euro per megawatt hour based on lower heating value (€/MWhLHV). Important parameters affecting total cost are amount of available biomass, biomass premium, logistics equipment, biomass moisture content, drying technology, torrefaction mass yield and torrefaction plant capital expenditures (CAPEX). Copyright © 2013 Elsevier Ltd. All rights reserved.

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

NASA Astrophysics Data System (ADS)

Shishebori, Davood; Babadi, Abolghasem Yousefi

2018-03-01

This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.

Current Risk Adjustment and Comorbidity Index Underperformance in Predicting Post-Acute Utilization and Hospital Readmissions After Joint Replacements: Implications for Comprehensive Care for Joint Replacement Model.

PubMed

Kumar, Amit; Karmarkar, Amol; Downer, Brian; Vashist, Amit; Adhikari, Deepak; Al Snih, Soham; Ottenbacher, Kenneth

2017-11-01

To compare the performances of 3 comorbidity indices, the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, and the Centers for Medicare & Medicaid Services (CMS) risk adjustment model, Hierarchical Condition Category (HCC), in predicting post-acute discharge settings and hospital readmission for patients after joint replacement. A retrospective study of Medicare beneficiaries with total knee replacement (TKR) or total hip replacement (THR) discharged from hospitals in 2009-2011 (n = 607,349) was performed. Study outcomes were post-acute discharge setting and unplanned 30-, 60-, and 90-day hospital readmissions. Logistic regression models were built to compare the performance of the 3 comorbidity indices using C statistics. The base model included patient demographics and hospital use. Subsequent models included 1 of the 3 comorbidity indices. Additional multivariable logistic regression models were built to identify individual comorbid conditions associated with high risk of hospital readmissions. The 30-, 60-, and 90-day unplanned hospital readmission rates were 5.3%, 7.2%, and 8.5%, respectively. Patients were most frequently discharged to home health (46.3%), followed by skilled nursing facility (40.9%) and inpatient rehabilitation facility (12.7%). The C statistics for the base model in predicting post-acute discharge setting and 30-, 60-, and 90-day readmission in TKR and THR were between 0.63 and 0.67. Adding the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, or HCC increased the C statistic minimally from the base model for predicting both discharge settings and hospital readmission. The health conditions most frequently associated with hospital readmission were diabetes mellitus, pulmonary disease, arrhythmias, and heart disease. The comorbidity indices and CMS-HCC demonstrated weak discriminatory ability to predict post-acute discharge settings and hospital readmission following joint replacement. © 2017, American College of Rheumatology.

GIS-based spatial decision support system for grain logistics management

NASA Astrophysics Data System (ADS)

Zhen, Tong; Ge, Hongyi; Jiang, Yuying; Che, Yi

2010-07-01

Grain logistics is the important component of the social logistics, which can be attributed to frequent circulation and the great quantity. At present time, there is no modern grain logistics distribution management system, and the logistics cost is the high. Geographic Information Systems (GIS) have been widely used for spatial data manipulation and model operations and provide effective decision support through its spatial database management capabilities and cartographic visualization. In the present paper, a spatial decision support system (SDSS) is proposed to support policy makers and to reduce the cost of grain logistics. The system is composed of two major components: grain logistics goods tracking model and vehicle routing problem optimization model and also allows incorporation of data coming from external sources. The proposed system is an effective tool to manage grain logistics in order to increase the speed of grain logistics and reduce the grain circulation cost.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

ERIC Educational Resources Information Center

Bolfarine, Heleno; Bazan, Jorge Luis

2010-01-01

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric…

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.

Parameter Recovery and Classification Accuracy under Conditions of Testlet Dependency: A Comparison of the Traditional 2PL, Testlet, and Bi-Factor Models

ERIC Educational Resources Information Center

Koziol, Natalie A.

2016-01-01

Testlets, or groups of related items, are commonly included in educational assessments due to their many logistical and conceptual advantages. Despite their advantages, testlets introduce complications into the theory and practice of educational measurement. Responses to items within a testlet tend to be correlated even after controlling for…

Association between age and high-risk human papilloma virus in Mexican oral cancer patients.

PubMed

González-Ramírez, I; Irigoyen-Camacho, M E; Ramírez-Amador, V; Lizano-Soberón, M; Carrillo-García, A; García-Carrancá, A; Sánchez-Pérez, Y; Méndez-Martínez, R; Granados-García, M; Ruíz-Godoy, Lm; García-Cuellar, Cm

2013-11-01

Studies reporting low prevalence of HPV in OSCC with declining age at presentation are increasing. The aim of this study was to determine the prevalence of HPV in a group of OSCC cases and controls in a Mexican population. The matched case-control study included 80 OSCC cases and 320 controls. HPV/DNA presence was evaluated through PCR amplification using three sets of consensus primers for the L1 gene. A conditional logistic regression analysis was carried out for the matched OSCC cases and controls. Interactions between risk factors and OCSS were tested in the construction process of the models. HPV prevalence was 5% in OSCC cases and 2.5% in controls. HPV-detected types were 16, 18 and 56. According to conditional logistics regression model, an association was detected between HR-HPV and OSCC. All HR-HPV-positive OSCC cases corresponded to young patients (<45 years), non-smokers and non-alcohol drinkers. The HR-HPV can be a contributing factor to oral carcinogenesis, especially in younger individuals without known risk factors such as tobacco and alcohol. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparing the Discrete and Continuous Logistic Models

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2008-01-01

The solutions of the discrete logistic growth model based on a difference equation and the continuous logistic growth model based on a differential equation are compared and contrasted. The investigation is conducted using a dynamic interactive spreadsheet. (Contains 5 figures.)

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

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

Zhang, Dezhi; Zhan, Qingwen; Chen, Yuche

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed modelmore » is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.« less

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

DOE PAGES

Zhang, Dezhi; Zhan, Qingwen; Chen, Yuche; ...

2016-03-14

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed modelmore » is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.« less

Logistics Modeling for Lunar Exploration Systems

NASA Technical Reports Server (NTRS)

Andraschko, Mark R.; Merrill, R. Gabe; Earle, Kevin D.

2008-01-01

The extensive logistics required to support extended crewed operations in space make effective modeling of logistics requirements and deployment critical to predicting the behavior of human lunar exploration systems. This paper discusses the software that has been developed as part of the Campaign Manifest Analysis Tool in support of strategic analysis activities under the Constellation Architecture Team - Lunar. The described logistics module enables definition of logistics requirements across multiple surface locations and allows for the transfer of logistics between those locations. A key feature of the module is the loading algorithm that is used to efficiently load logistics by type into carriers and then onto landers. Attention is given to the capabilities and limitations of this loading algorithm, particularly with regard to surface transfers. These capabilities are described within the context of the object-oriented software implementation, with details provided on the applicability of using this approach to model other human exploration scenarios. Some challenges of incorporating probabilistics into this type of logistics analysis model are discussed at a high level.

Equal Area Logistic Estimation for Item Response Theory

NASA Astrophysics Data System (ADS)

Lo, Shih-Ching; Wang, Kuo-Chang; Chang, Hsin-Li

2009-08-01

Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

Evolution Model and Simulation of Profit Model of Agricultural Products Logistics Financing

NASA Astrophysics Data System (ADS)

Yang, Bo; Wu, Yan

2018-03-01

Agricultural products logistics financial warehousing business mainly involves agricultural production and processing enterprises, third-party logistics enterprises and financial institutions tripartite, to enable the three parties to achieve win-win situation, the article first gives the replication dynamics and evolutionary stability strategy between the three parties in business participation, and then use NetLogo simulation platform, using the overall modeling and simulation method of Multi-Agent, established the evolutionary game simulation model, and run the model under different revenue parameters, finally, analyzed the simulation results. To achieve the agricultural products logistics financial financing warehouse business to participate in tripartite mutually beneficial win-win situation, thus promoting the smooth flow of agricultural products logistics business.

Environmental factors and flow paths related to Escherichia coli concentrations at two beaches on Lake St. Clair, Michigan, 2002–2005

USGS Publications Warehouse

Holtschlag, David J.; Shively, Dawn; Whitman, Richard L.; Haack, Sheridan K.; Fogarty, Lisa R.

2008-01-01

Regression analyses and hydrodynamic modeling were used to identify environmental factors and flow paths associated with Escherichia coli (E. coli) concentrations at Memorial and Metropolitan Beaches on Lake St. Clair in Macomb County, Mich. Lake St. Clair is part of the binational waterway between the United States and Canada that connects Lake Huron with Lake Erie in the Great Lakes Basin. Linear regression, regression-tree, and logistic regression models were developed from E. coli concentration and ancillary environmental data. Linear regression models on log10 E. coli concentrations indicated that rainfall prior to sampling, water temperature, and turbidity were positively associated with bacteria concentrations at both beaches. Flow from Clinton River, changes in water levels, wind conditions, and log10 E. coli concentrations 2 days before or after the target bacteria concentrations were statistically significant at one or both beaches. In addition, various interaction terms were significant at Memorial Beach. Linear regression models for both beaches explained only about 30 percent of the variability in log10 E. coli concentrations. Regression-tree models were developed from data from both Memorial and Metropolitan Beaches but were found to have limited predictive capability in this study. The results indicate that too few observations were available to develop reliable regression-tree models. Linear logistic models were developed to estimate the probability of E. coli concentrations exceeding 300 most probable number (MPN) per 100 milliliters (mL). Rainfall amounts before bacteria sampling were positively associated with exceedance probabilities at both beaches. Flow of Clinton River, turbidity, and log10 E. coli concentrations measured before or after the target E. coli measurements were related to exceedances at one or both beaches. The linear logistic models were effective in estimating bacteria exceedances at both beaches. A receiver operating characteristic (ROC) analysis was used to determine cut points for maximizing the true positive rate prediction while minimizing the false positive rate. A two-dimensional hydrodynamic model was developed to simulate horizontal current patterns on Lake St. Clair in response to wind, flow, and water-level conditions at model boundaries. Simulated velocity fields were used to track hypothetical massless particles backward in time from the beaches along flow paths toward source areas. Reverse particle tracking for idealized steady-state conditions shows changes in expected flow paths and traveltimes with wind speeds and directions from 24 sectors. The results indicate that three to four sets of contiguous wind sectors have similar effects on flow paths in the vicinity of the beaches. In addition, reverse particle tracking was used for transient conditions to identify expected flow paths for 10 E. coli sampling events in 2004. These results demonstrate the ability to track hypothetical particles from the beaches, backward in time, to likely source areas. This ability, coupled with a greater frequency of bacteria sampling, may provide insight into changes in bacteria concentrations between source and sink areas.

Supply Chain Engineering and the Use of a Supporting Knowledge Management Application

NASA Astrophysics Data System (ADS)

Laakmann, Frank

The future competition in markets will happen between logistics networks and no longer between enterprises. A new approach for supporting the engineering of logistics networks is developed by this research as a part of the Collaborative Research Centre (SFB) 559: "Modeling of Large Networks in Logistics" at the University of Dortmund together with the Fraunhofer-Institute of Material Flow and Logistics founded by Deutsche Forschungsgemeinschaft (DFG). Based on a reference model for logistics processes, the process chain model, a guideline for logistics engineers is developed to manage the different types of design tasks of logistics networks. The technical background of this solution is a collaborative knowledge management application. This paper will introduce how new Internet-based technologies support supply chain design projects.

76 FR 42119 - 36(b)(1) Arms Sales Notification

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-18

.... Government and contractor engineering and logistics support services, and other related elements of logistics... documentation, U.S. Government and contractor engineering and logistics support services, and other related... range of adverse battlefield conditions. The hardware itself is Unclassified. The engineering design and...

Study on optimization method of test conditions for fatigue crack detection using lock-in vibrothermography

NASA Astrophysics Data System (ADS)

Min, Qing-xu; Zhu, Jun-zhen; Feng, Fu-zhou; Xu, Chao; Sun, Ji-wei

2017-06-01

In this paper, the lock-in vibrothermography (LVT) is utilized for defect detection. Specifically, for a metal plate with an artificial fatigue crack, the temperature rise of the defective area is used for analyzing the influence of different test conditions, i.e. engagement force, excitation intensity, and modulated frequency. The multivariate nonlinear and logistic regression models are employed to estimate the POD (probability of detection) and POA (probability of alarm) of fatigue crack, respectively. The resulting optimal selection of test conditions is presented. The study aims to provide an optimized selection method of the test conditions in the vibrothermography system with the enhanced detection ability.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

The use of the logistic model in space motion sickness prediction

NASA Technical Reports Server (NTRS)

Lin, Karl K.; Reschke, Millard F.

1987-01-01

The one-equation and the two-equation logistic models were used to predict subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. The results show that the logistic models correctly predicted substantially more cases (an average of 13 percent) in the data subset used for model building. Overall, the logistic models ranged from 53 to 65 percent predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65 percent correct for the cross validation sample.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

NASA Astrophysics Data System (ADS)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015

USGS Publications Warehouse

Foster, Guy M.; Graham, Jennifer L.

2016-04-06

The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Source-water supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial events, and indicating changes in water-quality conditions that may affect drinking-water treatment processes.

Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling.

PubMed

Cohen, Mark E; Dimick, Justin B; Bilimoria, Karl Y; Ko, Clifford Y; Richards, Karen; Hall, Bruce Lee

2009-12-01

Although logistic regression has commonly been used to adjust for risk differences in patient and case mix to permit quality comparisons across hospitals, hierarchical modeling has been advocated as the preferred methodology, because it accounts for clustering of patients within hospitals. It is unclear whether hierarchical models would yield important differences in quality assessments compared with logistic models when applied to American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) data. Our objective was to evaluate differences in logistic versus hierarchical modeling for identifying hospitals with outlying outcomes in the ACS-NSQIP. Data from ACS-NSQIP patients who underwent colorectal operations in 2008 at hospitals that reported at least 100 operations were used to generate logistic and hierarchical prediction models for 30-day morbidity and mortality. Differences in risk-adjusted performance (ratio of observed-to-expected events) and outlier detections from the two models were compared. Logistic and hierarchical models identified the same 25 hospitals as morbidity outliers (14 low and 11 high outliers), but the hierarchical model identified 2 additional high outliers. Both models identified the same eight hospitals as mortality outliers (five low and three high outliers). The values of observed-to-expected events ratios and p values from the two models were highly correlated. Results were similar when data were permitted from hospitals providing < 100 patients. When applied to ACS-NSQIP data, logistic and hierarchical models provided nearly identical results with respect to identification of hospitals' observed-to-expected events ratio outliers. As hierarchical models are prone to implementation problems, logistic regression will remain an accurate and efficient method for performing risk adjustment of hospital quality comparisons.

Lessons learned from a colocation model using psychiatrists in urban primary care settings.

PubMed

Weiss, Meredith; Schwartz, Bruce J

2013-07-01

Comorbid psychiatric illness has been identified as a major driver of health care costs. The colocation of psychiatrists in primary care practices has been proposed as a model to improve mental health and medical care as well as a model to reduce health care costs. Financial models were developed to determine the sustainability of colocation. We found that the population studied had substantial psychiatric and medical burdens, and multiple practice logistical issues were identified. The providers found the experience highly rewarding and colocation was financially sustainable under certain conditions. The colocation model was effective in identifying and treating psychiatric comorbidities.

Modeling nitrate at domestic and public-supply well depths in the Central Valley, California

USGS Publications Warehouse

Nolan, Bernard T.; Gronberg, JoAnn M.; Faunt, Claudia C.; Eberts, Sandra M.; Belitz, Ken

2014-01-01

Aquifer vulnerability models were developed to map groundwater nitrate concentration at domestic and public-supply well depths in the Central Valley, California. We compared three modeling methods for ability to predict nitrate concentration >4 mg/L: logistic regression (LR), random forest classification (RFC), and random forest regression (RFR). All three models indicated processes of nitrogen fertilizer input at the land surface, transmission through coarse-textured, well-drained soils, and transport in the aquifer to the well screen. The total percent correct predictions were similar among the three models (69–82%), but RFR had greater sensitivity (84% for shallow wells and 51% for deep wells). The results suggest that RFR can better identify areas with high nitrate concentration but that LR and RFC may better describe bulk conditions in the aquifer. A unique aspect of the modeling approach was inclusion of outputs from previous, physically based hydrologic and textural models as predictor variables, which were important to the models. Vertical water fluxes in the aquifer and percent coarse material above the well screen were ranked moderately high-to-high in the RFR models, and the average vertical water flux during the irrigation season was highly significant (p < 0.0001) in logistic regression.

Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium.

PubMed

Ngo, Long H; Inouye, Sharon K; Jones, Richard N; Travison, Thomas G; Libermann, Towia A; Dillon, Simon T; Kuchel, George A; Vasunilashorn, Sarinnapha M; Alsop, David C; Marcantonio, Edward R

2017-06-06

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

32 CFR 231.7 - Procedures-domestic credit unions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... of logistical support and space arrangements may be made through the Secretary of the Military... improvements and restore the land to its original condition. (d) Use of space, logistical support, and military...) Logistical support. When available, custodial and janitorial services to include garbage disposal and outdoor...

32 CFR 231.7 - Procedures-domestic credit unions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of logistical support and space arrangements may be made through the Secretary of the Military... improvements and restore the land to its original condition. (d) Use of space, logistical support, and military...) Logistical support. When available, custodial and janitorial services to include garbage disposal and outdoor...

32 CFR 231.7 - Procedures-domestic credit unions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... of logistical support and space arrangements may be made through the Secretary of the Military... improvements and restore the land to its original condition. (d) Use of space, logistical support, and military...) Logistical support. When available, custodial and janitorial services to include garbage disposal and outdoor...

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions.

PubMed

Tay, Richard

2016-03-01

The binary logistic model has been extensively used to analyze traffic collision and injury data where the outcome of interest has two categories. However, the assumption of a symmetric distribution may not be a desirable property in some cases, especially when there is a significant imbalance in the two categories of outcome. This study compares the standard binary logistic model with the skewed logistic model in two cases in which the symmetry assumption is violated in one but not the other case. The differences in the estimates, and thus the marginal effects obtained, are significant when the assumption of symmetry is violated. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

Taking Wave Prediction to New Levels: Wavewatch 3

DTIC Science & Technology

2016-01-01

features such as surf and rip currents , conditions that affect special operations, amphibious assaults, and logistics over the shore. Changes in...The Navy’s current version of WAVEWATCH Ill features the capability of operating with gridded domains of multiple resolution simultaneously, ranging...Netherlands. Its current form, WAVEWATCH Ill, was developed at NOAA’s National Center for Environmental Prediction. The model is free and open source

Scheduling Operational Operational-Level Courses of Action

DTIC Science & Technology

2003-10-01

Process modelling and analysis – process synchronisation techniques Information and knowledge management – Collaborative planning systems – Workflow...logistics – Some tasks may consume resources The military user may wish to impose synchronisation constraints among tasks A military end state can be...effects, – constrained with resource and synchronisation considerations, and – lead to the achievement of conditions set in the end state. The COA is

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.

PubMed

Austin, Peter C; Steyerberg, Ewout W

2012-06-20

When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.

Anaerobic digestion of amine-oxide-based surfactants: biodegradation kinetics and inhibitory effects.

PubMed

Ríos, Francisco; Lechuga, Manuela; Fernández-Arteaga, Alejandro; Jurado, Encarnación; Fernández-Serrano, Mercedes

2017-08-01

Recently, anaerobic degradation has become a prevalent alternative for the treatment of wastewater and activated sludge. Consequently, the anaerobic biodegradability of recalcitrant compounds such as some surfactants require a thorough study to avoid their presence in the environment. In this work, the anaerobic biodegradation of amine-oxide-based surfactants, which are toxic to several organisms, was studied by measuring of the biogas production in digested sludge. Three amine-oxide-based surfactants with structural differences in their hydrophobic alkyl chain were tested: Lauramine oxide (AO-R 12 ), Myristamine oxide (AO-R 14 ) and Cocamidopropylamine oxide (AO-cocoamido). Results show that AO-R 12 and AO-R 14 inhibit biogas production, inhibition percentages were around 90%. AO-cocoamido did not cause inhibition and it was biodegraded until reaching a percentage of 60.8%. Otherwise, we fitted the production of biogas to two kinetic models, to a pseudo first-order model and to a logistic model. Production of biogas during the anaerobic biodegradation of AO-cocoamido was pretty good adjusted to the logistics model. Kinetic parameters were also determined. This modelling is useful to predict their behaviour in wastewater treatment plants and under anaerobic conditions in the environment.

Rainfall-induced Landslide Susceptibility assessment at the Longnan county

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Zhang, Ying

2017-04-01

Landslides are a serious disaster in Longnan county, China. Therefore landslide susceptibility assessment is useful tool for government or decision making. The main objective of this study is to investigate and compare the frequency ratio, support vector machines, and logistic regression. The Longnan county (Jiangxi province, China) was selected as the case study. First, the landslide inventory map with 354 landslide locations was constructed. Then landslide locations were then randomly divided into a ratio of 70/30 for the training and validating the models. Second, fourteen landslide conditioning factors were prepared such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Using the frequency ratio, support vector machines, and logistic regression, a total of three landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using the Receiver operating characteristic (ROC) curve technique. The result showed that the support vector machines model is the best model in the study area. The success rate is 88.39 %; and prediction rate is 84.06 %.

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

NASA Astrophysics Data System (ADS)

Ozdemir, Adnan

2011-07-01

SummaryThe purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model was found to be in strong agreement with the available groundwater spring test data. Hence, this method can be used routinely in groundwater exploration under favourable conditions.

Application of statistical distribution theory to launch-on-time for space construction logistic support

NASA Technical Reports Server (NTRS)

Morgenthaler, George W.

1989-01-01

The ability to launch-on-time and to send payloads into space has progressed dramatically since the days of the earliest missile and space programs. Causes for delay during launch, i.e., unplanned 'holds', are attributable to several sources: weather, range activities, vehicle conditions, human performance, etc. Recent developments in space program, particularly the need for highly reliable logistic support of space construction and the subsequent planned operation of space stations, large unmanned space structures, lunar and Mars bases, and the necessity of providing 'guaranteed' commercial launches have placed increased emphasis on understanding and mastering every aspect of launch vehicle operations. The Center of Space Construction has acquired historical launch vehicle data and is applying these data to the analysis of space launch vehicle logistic support of space construction. This analysis will include development of a better understanding of launch-on-time capability and simulation of required support systems for vehicle assembly and launch which are necessary to support national space program construction schedules. In this paper, the author presents actual launch data on unscheduled 'hold' distributions of various launch vehicles. The data have been supplied by industrial associate companies of the Center for Space Construction. The paper seeks to determine suitable probability models which describe these historical data and that can be used for several purposes such as: inputs to broader simulations of launch vehicle logistic space construction support processes and the determination of which launch operations sources cause the majority of the unscheduled 'holds', and hence to suggest changes which might improve launch-on-time. In particular, the paper investigates the ability of a compound distribution probability model to fit actual data, versus alternative models, and recommends the most productive avenues for future statistical work.

Logistics in a low carbon concept: Connotation and realization way

NASA Astrophysics Data System (ADS)

Zheng, Chaocheng; Qiu, Xiaoying; Mao, Jiarong

2017-01-01

Low-carbon logistics has become a trend for the logistics industry-as a high-energy consumption industry, continuation of its previous operating mode has been significantly behind the times. So logistics industry must release lower carbon emissions. This paper sort out the literature home and abroad from three aspects, that is, the definition of low-carbon logistics, low-carbon logistics implementation mechanisms or measures, and low carbon design quantitative models. The research shows: low-carbon logistics needed to implemented both in enterprise' macro and micro level, which means the government should provide relevant policy support and micro enterprises should be actively sought from all sectors of the logistics in energy saving. In practice, low-carbon logistics optimization models are effective tools for enterprises to implement emission reduction.

Stability and Hopf Bifurcation in a Reaction-Diffusion Model with Chemotaxis and Nonlocal Delay Effect

NASA Astrophysics Data System (ADS)

Li, Dong; Guo, Shangjiang

Chemotaxis is an observed phenomenon in which a biological individual moves preferentially toward a relatively high concentration, which is contrary to the process of natural diffusion. In this paper, we study a reaction-diffusion model with chemotaxis and nonlocal delay effect under Dirichlet boundary condition by using Lyapunov-Schmidt reduction and the implicit function theorem. The existence, multiplicity, stability and Hopf bifurcation of spatially nonhomogeneous steady state solutions are investigated. Moreover, our results are illustrated by an application to the model with a logistic source, homogeneous kernel and one-dimensional spatial domain.

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Satellite rainfall retrieval by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Description of Aspergillus flavus growth under the influence of different factors (water activity, incubation temperature, protein and fat concentration, pH, and cinnamon essential oil concentration) by kinetic, probability of growth, and time-to-detection models.

PubMed

Kosegarten, Carlos E; Ramírez-Corona, Nelly; Mani-López, Emma; Palou, Enrique; López-Malo, Aurelio

2017-01-02

A Box-Behnken design was used to determine the effect of protein concentration (0, 5, or 10g of casein/100g), fat (0, 3, or 6g of corn oil/100g), a w (0.900, 0.945, or 0.990), pH (3.5, 5.0, or 6.5), concentration of cinnamon essential oil (CEO, 0, 200, or 400μL/kg) and incubation temperature (15, 25, or 35°C) on the growth of Aspergillus flavus during 50days of incubation. Mold response under the evaluated conditions was modeled by the modified Gompertz equation, logistic regression, and time-to-detection model. The obtained polynomial regression models allow the significant coefficients (p<0.05) for linear, quadratic and interaction effects for the Gompertz equation's parameters to be identified, which adequately described (R 2 >0.967) the studied mold responses. After 50days of incubation, every tested model system was classified according to the observed response as 1 (growth) or 0 (no growth), then a binary logistic regression was utilized to model A. flavus growth interface, allowing to predict the probability of mold growth under selected combinations of tested factors. The time-to-detection model was utilized to estimate the time at which A. flavus visible growth begins. Water activity, temperature, and CEO concentration were the most important factors affecting fungal growth. It was observed that there is a range of possible combinations that may induce growth, such that incubation conditions and the amount of essential oil necessary for fungal growth inhibition strongly depend on protein and fat concentrations as well as on the pH of studied model systems. The probabilistic model and the time-to-detection models constitute another option to determine appropriate storage/processing conditions and accurately predict the probability and/or the time at which A. flavus growth occurs. Copyright © 2016 Elsevier B.V. All rights reserved.

Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Relationships of physical job tasks and living conditions with occupational injuries in coal miners.

PubMed

Bhattacherjee, Ashis; Bertrand, Jean-Pierre; Meyer, Jean-Pierre; Benamghar, Lahoucine; Otero Sierra, Carmen; Michaely, Jean-Pierre; Ghosh, Apurna Kumar; d'Houtaud, Alphonse; Mur, Jean-Marie; Chau, Nearkasen

2007-04-01

This study assessed the relationships of job tasks and living conditions with occupational injuries among coal miners. The sample included randomly selected 516 underground workers. They completed a standardized self-administred questionnaire. The data were analyzed via logistic regression method. The rate of injuries in the past two years was 29.8%. The job tasks with significant crude relative risks were: power hammer, vibrating hand tools, pneumatic tools, bent trunk, awkward work posture, heat, standing about and walking, job tasks for trunk and upper/lower limbs, pain caused by work, and muscular tiredness. Logistic model shows a strong relationship between the number of job tasks (JT) and injuries (adjusted ORs vs. JT 0-1: 2.21, 95%CI 1.27-3.86 for JT 2-6 and 3.82, 2.14-6.82 for JT>or=7), and significant ORs>or=1.71 for face work, not-good-health-status, and psychotropic drug use. Musculoskeletal disorders and certain personality traits were also significant in univariate analysis. Therefore job tasks and living conditions strongly increase the injuries, and occupational physicians could help workers to find remedial measures.

A model for field toxicity tests

USGS Publications Warehouse

Kaiser, Mark S.; Finger, Susan E.

1996-01-01

Toxicity tests conducted under field conditions present an interesting challenge for statistical modelling. In contrast to laboratory tests, the concentrations of potential toxicants are not held constant over the test. In addition, the number and identity of toxicants that belong in a model as explanatory factors are not known and must be determined through a model selection process. We present one model to deal with these needs. This model takes the record of mortalities to form a multinomial distribution in which parameters are modelled as products of conditional daily survival probabilities. These conditional probabilities are in turn modelled as logistic functions of the explanatory factors. The model incorporates lagged values of the explanatory factors to deal with changes in the pattern of mortalities over time. The issue of model selection and assessment is approached through the use of generalized information criteria and power divergence goodness-of-fit tests. These model selection criteria are applied in a cross-validation scheme designed to assess the ability of a model to both fit data used in estimation and predict data deleted from the estimation data set. The example presented demonstrates the need for inclusion of lagged values of the explanatory factors and suggests that penalized likelihood criteria may not provide adequate protection against overparameterized models in model selection.

Hopf and Bautin Bifurcation in a Tritrophic Food Chain Model with Holling Functional Response Types III and IV

NASA Astrophysics Data System (ADS)

Castellanos, Víctor; Castillo-Santos, Francisco Eduardo; Dela-Rosa, Miguel Angel; Loreto-Hernández, Iván

In this paper, we analyze the Hopf and Bautin bifurcation of a given system of differential equations, corresponding to a tritrophic food chain model with Holling functional response types III and IV for the predator and superpredator, respectively. We distinguish two cases, when the prey has linear or logistic growth. In both cases we guarantee the existence of a limit cycle bifurcating from an equilibrium point in the positive octant of ℝ3. In order to do so, for the Hopf bifurcation we compute explicitly the first Lyapunov coefficient, the transversality Hopf condition, and for the Bautin bifurcation we also compute the second Lyapunov coefficient and verify the regularity conditions.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Asymptotic behavior of degenerate logistic equations

NASA Astrophysics Data System (ADS)

Arrieta, José M.; Pardo, Rosa; Rodríguez-Bernal, Aníbal

2015-12-01

We analyze the asymptotic behavior of positive solutions of parabolic equations with a class of degenerate logistic nonlinearities of the type λu - n (x)uρ. An important characteristic of this work is that the region where the logistic term n (ṡ) vanishes, that is K0 = { x : n (x) = 0 }, may be non-smooth. We analyze conditions on λ, ρ, n (ṡ) and K0 guaranteeing that the solution starting at a positive initial condition remains bounded or blows up as time goes to infinity. The asymptotic behavior may not be the same in different parts of K0.

A Note on the Item Information Function of the Four-Parameter Logistic Model

ERIC Educational Resources Information Center

Magis, David

2013-01-01

This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…

A Capacity Forecast Model for Volatile Data in Maintenance Logistics

NASA Astrophysics Data System (ADS)

Berkholz, Daniel

2009-05-01

Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

PubMed

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions

PubMed Central

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition. PMID:25679957

Compendium of Operations Research and Economic Analysis Studies.

DTIC Science & Technology

1987-09-01

robots , conveyor belts, etc. are modeled as well. Simulation results indicate that the IMC design for receiving and packing is feasible from a system...Logistics Services Center (DLSC) operation. Benef its were evaluated through analysis of a sample of data from recipients as to the use being made of...Modernization of the DPSC Manufacturing Facility (August 1969) This study explored the feasibility of a proposal to air condition and modernize the

Spectral analysis of a two-species competition model: Determining the effects of extreme conditions on the color of noise generated from simulated time series

NASA Astrophysics Data System (ADS)

Golinski, M. R.

2006-07-01

Ecologists have observed that environmental noise affects population variance in the logistic equation for one-species growth. Interactions between deterministic and stochastic dynamics in a one-dimensional system result in increased variance in species population density over time. Since natural populations do not live in isolation, the present paper simulates a discrete-time two-species competition model with environmental noise to determine the type of colored population noise generated by extreme conditions in the long-term population dynamics of competing populations. Discrete Fourier analysis is applied to the simulation results and the calculated Hurst exponent ( H) is used to determine how the color of population noise for the two species corresponds to extreme conditions in population dynamics. To interpret the biological meaning of the color of noise generated by the two-species model, the paper determines the color of noise generated by three reference models: (1) A two-dimensional discrete-time white noise model (0⩽ H<1/2); (2) A two-dimensional fractional Brownian motion model (H=1/2); and (3) A two-dimensional discrete-time model with noise for unbounded growth of two uncoupled species (1/2< H⩽1).

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

Treesearch

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...

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

USGS Publications Warehouse

Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam

2012-01-01

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.

Genetic variation of the androgen receptor and risk of myocardial infarction and ischemic stroke in women.

PubMed

Rexrode, Kathryn M; Ridker, Paul M; Hegener, Hillary H; Buring, Julie E; Manson, JoAnn E; Zee, Robert Y L

2008-05-01

Androgen receptors (AR) are expressed in endothelial cells and vascular smooth-muscle cells. Some studies suggest an association between AR gene variation and risk of cardiovascular disease (CVD) in men; however, the relationship has not been examined in women. Six haplotype block-tagging single nucleotide polymorphisms (rs962458, rs6152, rs1204038, rs2361634, rs1337080, rs1337082), as well as the cysteine, adenine, guanine (CAG) microsatellite in exon 1, of the AR gene were evaluated among 300 white postmenopausal women who developed CVD (158 myocardial infarctions and 142 ischemic strokes) and an equal number of matched controls within the Women's Health Study. Genotype distributions were similar between cases and controls, and genotypes were not significantly related to risk of CVD, myocardial infarctions or ischemic stroke in conditional logistic regression models. Seven common haplotypes were observed, but distributions did not differ between cases and controls nor were significant associations observed in logistic regression analysis. The median CAG repeat length was 21. In conditional logistic regression, there was no association between the number of alleles with CAG repeat length >or=21 (or >or=22) and risk of CVD, myocardial infarctions or ischemic stroke. No association between AR genetic variation, as measured by haplotype-tagging single nucleotide polymorphisms and CAG repeat number, and risk of CVD was observed in women.

The Use of Logistics n the Quality Parameters Control System of Material Flow

ERIC Educational Resources Information Center

Karpova, Natalia P.; Toymentseva, Irina A.; Shvetsova, Elena V.; Chichkina, Vera D.; Chubarkova, Elena V.

2016-01-01

The relevance of the research problem is conditioned on the need to justify the use of the logistics methodologies in the quality parameters control process of material flows. The goal of the article is to develop theoretical principles and practical recommendations for logistical system control in material flows quality parameters. A leading…

Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048

Using phenomenological models for forecasting the 2015 Ebola challenge.

PubMed

Pell, Bruce; Kuang, Yang; Viboud, Cecile; Chowell, Gerardo

2018-03-01

The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points. Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Two-echelon logistics service supply chain decision game considering quality supervision

NASA Astrophysics Data System (ADS)

Shi, Jiaying

2017-10-01

Due to the increasing importance of supply chain logistics service, we established the Stackelberg game model between single integrator and single subcontractors under decentralized and centralized circumstances, and found that logistics services integrators as a leader prefer centralized decision-making but logistics service subcontractors tend to the decentralized decision-making. Then, we further analyzed why subcontractor chose to deceive and rebuilt a principal-agent game model to monitor the logistics services quality of them. Mixed Strategy Nash equilibrium and related parameters were discussed. The results show that strengthening the supervision and coordination can improve the quality level of logistics service supply chain.

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Treesearch

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Differentially private distributed logistic regression using private and public data.

PubMed

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model.

PubMed

Guo, Xiaopeng; Ren, Dongfang; Shi, Jiaxing

2016-12-01

This paper studies the relationship among carbon emissions, GDP, and logistics by using a panel data model and a combination of statistics and econometrics theory. The model is based on the historical data of 10 typical provinces and cities in China during 2005-2014. The model in this paper adds the variability of logistics on the basis of previous studies, and this variable is replaced by the freight turnover of the provinces. Carbon emissions are calculated by using the annual consumption of coal, oil, and natural gas. GDP is the gross domestic product. The results showed that the amount of logistics and GDP have a contribution to carbon emissions and the long-term relationships are different between different cities in China, mainly influenced by the difference among development mode, economic structure, and level of logistic development. After the testing of panel model setting, this paper established a variable coefficient model of the panel. The influence of GDP and logistics on carbon emissions is obtained according to the influence factors among the variables. The paper concludes with main findings and provides recommendations toward rational planning of urban sustainable development and environmental protection for China.

Associations between physical activity parenting practices and adolescent girls' self-perceptions and physical activity intentions.

PubMed

Sebire, Simon J; Haase, Anne M; Montgomery, Alan A; McNeill, Jade; Jago, Russ

2014-05-01

The current study investigated cross-sectional associations between maternal and paternal logistic and modeling physical activity support and the self-efficacy, self-esteem, and physical activity intentions of 11- to 12-year-old girls. 210 girls reported perceptions of maternal and paternal logistic and modeling support and their self-efficacy, self-esteem and intention to be physically active. Data were analyzed using multivariable regression models. Maternal logistic support was positively associated with participants' self-esteem, physical activity self-efficacy, and intention to be active. Maternal modeling was positively associated with self-efficacy. Paternal modeling was positively associated with self-esteem and self-efficacy but there was no evidence that paternal logistic support was associated with the psychosocial variables. Activity-related parenting practices were associated with psychosocial correlates of physical activity among adolescent girls. Logistic support from mothers, rather than modeling support or paternal support may be a particularly important target when designing interventions aimed at preventing the age-related decline in physical activity among girls.

Regional Logistics Information Resources Integration Patterns and Countermeasures

NASA Astrophysics Data System (ADS)

Wu, Hui; Shangguan, Xu-ming

Effective integration of regional logistics information resources can provide collaborative services in information flow, business flow and logistics for regional logistics enterprises, which also can reduce operating costs and improve market responsiveness. First, this paper analyzes the realistic significance on the integration of regional logistics information. Second, this paper brings forward three feasible patterns on the integration of regional logistics information resources, These three models have their own strengths and the scope of application and implementation, which model is selected will depend on the specific business and the regional distribution of enterprises. Last, this paper discusses the related countermeasures on the integration of regional logistics information resources, because the integration of regional logistics information is a systems engineering, when the integration is advancing, the countermeasures should pay close attention to the current needs and long-term development of regional enterprises.

Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

NASA Astrophysics Data System (ADS)

Domínguez-Cuesta, María José; Jiménez-Sánchez, Montserrat; Berrezueta, Edgar

2007-09-01

A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278 km 2 area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10 0 to 10 1 m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between - 6 and - 0.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of "zoom method" allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.

Temporal trends in maternal medical conditions and stillbirth.

PubMed

Patel, Emily M; Goodnight, William H; James, Andra H; Grotegut, Chad A

2015-05-01

The objective of this study was to estimate the prevalence and temporal trends of medical conditions among women with stillbirth and to determine the effect of medical comorbidities on the trend of stillbirth. The Nationwide Inpatient Sample (NIS) for the years 2008-2010 was first queried for all delivery-related discharges. A multivariable logistic regression model was constructed with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) calculated for medical conditions among women with stillbirth. The NIS was then queried for the years 2000-2010, and the effect of maternal medical conditions on the stillbirth rate was estimated. From 2008 to 2010, there were 51,080 deliveries to women with stillbirth, giving a rate of 4.08 per 1000 live births. Women with stillbirth were more likely to be African American (OR, 2.12; 95% CI, 2.07-2.17), with an age less than 25 years (OR, 1.19; 95% CI, 1.16-1.22) or older than 35 years (OR, 1.40; 95% CI, 1.37-1.44) compared with women without stillbirth. Medical conditions such as cardiac, rheumatological, and renal disorders; hypertension; diabetes; thrombophilia; and drug, alcohol and tobacco use, were independent predictors of fetal demise in multivariable logistic regression modeling. From 2000 to 2010, despite an increase in the total number of births to women with comorbidities, there was a significant decrease in the stillbirth rate, which was more pronounced among women with comorbidities compared with women without comorbidities (P=.021). From 2000 to 2010, there was a significantly greater decrease in the stillbirth rate among women with maternal medical conditions than there was among women without comorbidities. These findings occurred despite an overall increase in the number of pregnancies to women with medical comorbidities over the time period. Because the NIS does not include information on gestational age, birthweight, or whether subjects had antepartum testing, we are not able to determine the effect of these variables on the observed outcomes. Copyright © 2015 Elsevier Inc. All rights reserved.

[Efficiency of preventive dental care in school dentistry system].

PubMed

Avraamova, O G; Kolesnik, A G; Kulazhenko, T V; Zadapaeva, S V; Shevchenko, S S

2014-01-01

Ways of development of Russian school dentistry are defined and justified based on the analysis according to logistics, personnel, legal, financial and economic basis for the reorientation of the service for preventive direction, which should be a priority in the current conditions. The implemented model of school dental care based on team work of the dentist and dental hygienist proved to be highly efficient and may be recommended for wide introduction in practice.

On estimating probability of presence from use-availability or presence-background data.

PubMed

Phillips, Steven J; Elith, Jane

2013-06-01

A fundamental ecological modeling task is to estimate the probability that a species is present in (or uses) a site, conditional on environmental variables. For many species, available data consist of "presence" data (locations where the species [or evidence of it] has been observed), together with "background" data, a random sample of available environmental conditions. Recently published papers disagree on whether probability of presence is identifiable from such presence-background data alone. This paper aims to resolve the disagreement, demonstrating that additional information is required. We defined seven simulated species representing various simple shapes of response to environmental variables (constant, linear, convex, unimodal, S-shaped) and ran five logistic model-fitting methods using 1000 presence samples and 10 000 background samples; the simulations were repeated 100 times. The experiment revealed a stark contrast between two groups of methods: those based on a strong assumption that species' true probability of presence exactly matches a given parametric form had highly variable predictions and much larger RMS error than methods that take population prevalence (the fraction of sites in which the species is present) as an additional parameter. For six species, the former group grossly under- or overestimated probability of presence. The cause was not model structure or choice of link function, because all methods were logistic with linear and, where necessary, quadratic terms. Rather, the experiment demonstrates that an estimate of prevalence is not just helpful, but is necessary (except in special cases) for identifying probability of presence. We therefore advise against use of methods that rely on the strong assumption, due to Lele and Keim (recently advocated by Royle et al.) and Lancaster and Imbens. The methods are fragile, and their strong assumption is unlikely to be true in practice. We emphasize, however, that we are not arguing against standard statistical methods such as logistic regression, generalized linear models, and so forth, none of which requires the strong assumption. If probability of presence is required for a given application, there is no panacea for lack of data. Presence-background data must be augmented with an additional datum, e.g., species' prevalence, to reliably estimate absolute (rather than relative) probability of presence.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

2017-06-01

We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

A development of logistics management models for the Space Transportation System

NASA Technical Reports Server (NTRS)

Carrillo, M. J.; Jacobsen, S. E.; Abell, J. B.; Lippiatt, T. F.

1983-01-01

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support.

A new model for simulating microbial cyanide production and optimizing the medium parameters for recovering precious metals from waste printed circuit boards.

PubMed

Yuan, Zhihui; Ruan, Jujun; Li, Yaying; Qiu, Rongliang

2018-04-10

Bioleaching is a green recycling technology for recovering precious metals from waste printed circuit boards (WPCBs). However, this technology requires increasing cyanide production to obtain desirable recovery efficiency. Luria-Bertani medium (LB medium, containing tryptone 10 g/L, yeast extract 5 g/L, NaCl 10 g/L) was commonly used in bioleaching of precious metal. In this study, results showed that LB medium did not produce highest yield of cyanide. Under optimal culture conditions (25 °C, pH 7.5), the maximum cyanide yield of the optimized medium (containing tryptone 6 g/L and yeast extract 5 g/L) was 1.5 times as high as that of LB medium. In addition, kinetics and relationship of cell growth and cyanide production was studied. Data of cell growth fitted logistics model well. Allometric model was demonstrated effective in describing relationship between cell growth and cyanide production. By inserting logistics equation into allometric equation, we got a novel hybrid equation containing five parameters. Kinetic data for cyanide production were well fitted to the new model. Model parameters reflected both cell growth and cyanide production process. Copyright © 2018 Elsevier B.V. All rights reserved.

Evidence from animal models on the pathogenesis of PCOS.

PubMed

Walters, K A; Bertoldo, M J; Handelsman, D J

2018-06-01

Polycystic ovarian syndrome (PCOS) is the most common endocrine condition in women, and is characterized by reproductive, endocrine and metabolic features. However, there is no simple unequivocal diagnostic test for PCOS, its etiology remains unknown and there is no cure. Hence, the management of PCOS is suboptimal as it relies on the ad hoc empirical management of its symptoms only. Decisive studies are required to unravel the origins of PCOS, but due to ethical and logistical reasons these are not possible in humans. Experimental animal models for PCOS have been established which have enhanced our understanding of the mechanisms underlying PCOS and propose novel mechanism-based therapies to treat the condition. This review examines the findings from various animal models to reveal the current knowledge of the mechanisms underpinning the development of PCOS, and also provides insights into the implications from these studies for improved clinical management of this disorder. Copyright © 2018 Elsevier Ltd. All rights reserved.

Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.

Physical-enhanced secure strategy in an OFDM-PON.

PubMed

Zhang, Lijia; Xin, Xiangjun; Liu, Bo; Yu, Jianjun

2012-01-30

The physical layer of optical access network is vulnerable to various attacks. As the dramatic increase of users and network capacity, the issue of physical-layer security becomes more and more important. This paper proposes a physical-enhanced secure strategy for orthogonal frequency division multiplexing passive optical network (OFDM-PON) by employing frequency domain chaos scrambling. The Logistic map is adopted for the chaos mapping. The chaos scrambling strategy can dynamically allocate the scrambling matrices for different OFDM frames according to the initial condition, which enhance the confidentiality of the physical layer. A mathematical model of this secure system is derived firstly, which achieves a secure transmission at physical layer in OFDM-PON. The results from experimental implementation using Logistic mapped chaos scrambling are also given to further demonstrate the efficiency of this secure strategy. An 10.125 Gb/s 64QAM-OFDM data with Logistic mapped chaos scrambling are successfully transmitted over 25-km single mode fiber (SMF), and the experimental results show that proposed security scheme can protect the system from eavesdropper and attacker, while keep a good performance for the legal ONU.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

A multimodal logistics service network design with time windows and environmental concerns

PubMed Central

Zhang, Dezhi; He, Runzhong; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained. PMID:28934272

A multimodal logistics service network design with time windows and environmental concerns.

PubMed

Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Impact of wearing fixed orthodontic appliances on quality of life among adolescents: Case-control study.

PubMed

Costa, Andréa A; Serra-Negra, Júnia M; Bendo, Cristiane B; Pordeus, Isabela A; Paiva, Saul M

2016-01-01

To investigate the impact of wearing a fixed orthodontic appliance on oral health-related quality of life (OHRQoL) among adolescents. A case-control study (1 ∶ 2) was carried out with a population-based randomized sample of 327 adolescents aged 11 to 14 years enrolled at public and private schools in the City of Brumadinho, southeast of Brazil. The case group (n  =  109) was made up of adolescents with a high negative impact on OHRQoL, and the control group (n  =  218) was made up of adolescents with a low negative impact. The outcome variable was the impact on OHRQoL measured by the Brazilian version of the Child Perceptions Questionnaire (CPQ 11-14) - Impact Short Form (ISF:16). The main independent variable was wearing fixed orthodontic appliances. Malocclusion and the type of school were identified as possible confounding variables. Bivariate and multiple conditional logistic regressions were employed in the statistical analysis. A multiple conditional logistic regression model demonstrated that adolescents wearing fixed orthodontic appliances had a 4.88-fold greater chance of presenting high negative impact on OHRQoL (95% CI: 2.93-8.13; P < .001) than those who did not wear fixed orthodontic appliances. A bivariate conditional logistic regression demonstrated that malocclusion was significantly associated with OHRQoL (P  =  .017), whereas no statistically significant association was found between the type of school and OHRQoL (P  =  .108). Adolescents who wore fixed orthodontic appliances had a greater chance of reporting a negative impact on OHRQoL than those who did not wear such appliances.

Differentially private distributed logistic regression using private and public data

PubMed Central

2014-01-01

Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries.

PubMed

Cunningham, Marc; Bock, Ariella; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-09-01

Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. © Cunningham et al.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries

PubMed Central

Cunningham, Marc; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-01-01

Background: Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Methods: Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. Results: For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Conclusions: Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. PMID:26374805

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields.

PubMed

Jahandideh, Samad; Abdolmaleki, Parviz; Movahedi, Mohammad Mehdi

2010-02-01

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes. (c) 2009 Wiley-Liss, Inc.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Anesthesia Care Transitions and Risk of Postoperative Complications.

PubMed

Hyder, Joseph A; Bohman, J Kyle; Kor, Daryl J; Subramanian, Arun; Bittner, Edward A; Narr, Bradly J; Cima, Robert R; Montori, Victor M

2016-01-01

A patient undergoing surgery may receive anesthesia care from several anesthesia providers. The safety of anesthesia care transitions has not been evaluated. Using unconditional and conditional multivariable logistic regression models, we tested whether the number of attending anesthesiologists involved in an operation was associated with postoperative complications. In a cohort of patients undergoing elective colorectal surgical in an academic tertiary care center with a stable anesthesia care team model participating in the American College of Surgeons National Surgical Quality Improvement Program, using unconditional and conditional multivariable logistic regression models, we tested adjusted associations between numbers of attending anesthesiologists and occurrence of death or a major complication (acute renal failure, bleeding that required a transfusion of 4 units or more of red blood cells within 72 hours after surgery, cardiac arrest requiring cardiopulmonary resuscitation, coma of 24 hours or longer, myocardial infarction, unplanned intubation, ventilator use for 48 hours or more, pneumonia, stroke, wound disruption, deep or organ-space surgical-site infection, superficial surgical-site infection, sepsis, septic shock, systemic inflammatory response syndrome). We identified 927 patients who underwent elective colectomy of comparable surgical intensity. In all, 71 (7.7%) patients had major nonfatal complications or death. One anesthesiologist provided care for 530 (57%) patients, 2 anesthesiologists for 287 (31%), and 3 or more for 110 (12%). The number of attending anesthesiologists was associated with increased odds of postoperative complication (unadjusted odds ratio [OR] = 1.52, 95% confidence interval [CI] 1.18-1.96, P = 0.0013; adjusted OR = 1.44, 95% CI 1.09-1.91, P = 0.0106). In sensitivity analyses, occurrence of a complication was significantly associated with the number of in-room providers, defined as anesthesia residents and nurse anesthetists (adjusted OR = 1.39, 95% CI 1.01-1.92, P = 0.0446) and for all anesthesia providers (adjusted OR = 1.58, 95%CI 1.20-2.08, P = 0.0012). Findings persisted across multiple, alternative adjustments, sensitivity analyses, and conditional logistic regression with matching on operative duration. In our study, care by additional attending anesthesiologists and in-room providers was independently associated with an increased odds of postoperative complications. These findings challenge the assumption that anesthesia transitions are care neutral and not contributory to surgical outcomes.

Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Applying Simulation and Logistics Modeling to Transportation Issues

DOT National Transportation Integrated Search

1995-08-15

This paper describes an application where transportation logistics and simulation tools are integrated to create a modeling environment for transportation planning. The Transportation Planning Model (TPM) is a tool developed for the Department of Ene...

Comparative Assessment and Decision Support System for Strategic Military Airlift Capability

NASA Technical Reports Server (NTRS)

Salmon, John; Iwata, Curtis; Mavris, Dimitri; Weston, Neil; Fahringer, Philip

2011-01-01

The Lockheed Martin Aeronautics Company has been awarded several programs to modernize the aging C-5 military transport fleet. In order to ensure its continuation amidst budget cuts, it was important to engage the decision makers by providing an environment to analyze the benefits of the modernization program. This paper describes an interface that allows the user to change inputs such as the scenario airfields, take-off conditions, and reliability characteristics. The underlying logistics surrogate model was generated using data from a discrete-event simulation. Various visualizations such as intercontinental flight paths illustrated in 3D, have been created to aid the user in analyzing scenarios and performing comparative assessments for various output logistics metrics. The capability to rapidly and dynamically evaluate and compare scenarios was developed enabling real time strategy exploration and trade-offs.

Stochastic dynamics and logistic population growth

NASA Astrophysics Data System (ADS)

Méndez, Vicenç; Assaf, Michael; Campos, Daniel; Horsthemke, Werner

2015-06-01

The Verhulst model is probably the best known macroscopic rate equation in population ecology. It depends on two parameters, the intrinsic growth rate and the carrying capacity. These parameters can be estimated for different populations and are related to the reproductive fitness and the competition for limited resources, respectively. We investigate analytically and numerically the simplest possible microscopic scenarios that give rise to the logistic equation in the deterministic mean-field limit. We provide a definition of the two parameters of the Verhulst equation in terms of microscopic parameters. In addition, we derive the conditions for extinction or persistence of the population by employing either the momentum-space spectral theory or the real-space Wentzel-Kramers-Brillouin approximation to determine the probability distribution function and the mean time to extinction of the population. Our analytical results agree well with numerical simulations.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei

2017-01-01

Background: We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. Methods: We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. Results: For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Conclusion: Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. PMID:28828316

Quantitative Analysis of Land Loss in Coastal Louisiana Using Remote Sensing

NASA Astrophysics Data System (ADS)

Wales, P. M.; Kuszmaul, J.; Roberts, C.

2005-12-01

For the past thirty-five years the land loss along the Louisiana Coast has been recognized as a growing problem. One of the clearest indicators of this land loss is that in 2000 smooth cord grass (spartina alterniflora) was turning brown well before its normal hibernation period. Over 100,000 acres of marsh were affected by the 2000 browning. In 2001 data were collected using low altitude helicopter based transects of the coast, with 7,400 data points being collected by researchers at the USGS, National Wetlands Research Center, and Louisiana Department of Natural Resources. The surveys contained data describing the characteristics of the marsh, including latitude, longitude, marsh condition, marsh color, percent vegetated, and marsh die-back. Creating a model that combines remote sensing images, field data, and statistical analysis to develop a methodology for estimating the margin of error in measurements of coastal land loss (erosion) is the ultimate goal of the study. A model was successfully created using a series of band combinations (used as predictive variables). The most successful band combinations or predictive variables were the braud value [(Sum Visible TM Bands - Sum Infrared TM Bands)/(Sum Visible TM Bands + Sum Infrared TM Bands)], TM band 7/ TM band 2, brightness, NDVI, wetness, vegetation index, and a 7x7 autocovariate nearest neighbor floating window. The model values were used to generate the logistic regression model. A new image was created based on the logistic regression probability equation where each pixel represents the probability of finding water or non-water at that location in each image. Pixels within each image that have a high probability of representing water have a value close to 1 and pixels with a low probability of representing water have a value close to 0. A logistic regression model is proposed that uses seven independent variables. This model yields an accurate classification in 86.5% of the locations considered in the 1997 and 2001 survey locations. When the logistic regression was modeled to the satellite imagery of the entire Louisiana Coast study area a statewide loss was estimated to be 358 mi2 to 368 mi2, from 1997 to 2001, using two different methods for estimating land loss.

Sample size estimation for alternating logistic regressions analysis of multilevel randomized community trials of under-age drinking.

PubMed

Reboussin, Beth A; Preisser, John S; Song, Eun-Young; Wolfson, Mark

2012-07-01

Under-age drinking is an enormous public health issue in the USA. Evidence that community level structures may impact on under-age drinking has led to a proliferation of efforts to change the environment surrounding the use of alcohol. Although the focus of these efforts is to reduce drinking by individual youths, environmental interventions are typically implemented at the community level with entire communities randomized to the same intervention condition. A distinct feature of these trials is the tendency of the behaviours of individuals residing in the same community to be more alike than that of others residing in different communities, which is herein called 'clustering'. Statistical analyses and sample size calculations must account for this clustering to avoid type I errors and to ensure an appropriately powered trial. Clustering itself may also be of scientific interest. We consider the alternating logistic regressions procedure within the population-averaged modelling framework to estimate the effect of a law enforcement intervention on the prevalence of under-age drinking behaviours while modelling the clustering at multiple levels, e.g. within communities and within neighbourhoods nested within communities, by using pairwise odds ratios. We then derive sample size formulae for estimating intervention effects when planning a post-test-only or repeated cross-sectional community-randomized trial using the alternating logistic regressions procedure.

Game Theory and U-Boats in the Bay of Biscay

DTIC Science & Technology

2003-03-01

a necessary condition for optimality in this case. Baston and Bostock (1989) approach a one-dimensional helicopter versus submarine game, modeled as...given number of bombs with which to attack the submarine, and the payoff is whether or not the submarine is destroyed. Baston and Bostock solve the...323 (March-April 2002). Baston , V. J. and F. A. Bostock. “A One-Dimensional Helicopter-Submarine Game,” Naval Research Logistics, Vol. 36: 479-490

Modeling Population Growth and Extinction

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2009-01-01

The exponential growth model and the logistic model typically introduced in the mathematics curriculum presume that a population grows exclusively. In reality, species can also die out and more sophisticated models that take the possibility of extinction into account are needed. In this article, two extensions of the logistic model are considered,…

Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

PubMed

Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

2016-12-01

The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

PubMed

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

PubMed Central

2012-01-01

Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. PMID:22716998

Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

NASA Astrophysics Data System (ADS)

Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

2013-08-01

Several mathematical models are used to predict the spatial distribution characteristics of landslides to mitigate damage caused by landslide disasters. Although some studies have achieved excellent results around the world, few studies take the inter-relationship of the selected points (training points) into account. In this paper, we present the Fuzzy c-means (FCM) algorithm as an optimal method for choosing the appropriate input landslide points as training data. Based on different combinations of the Fuzzy exponent (m) and the number of clusters (c), five groups of sampling points were derived from formal seed cells points and applied to analyze the landslide susceptibility in Mizunami City, Gifu Prefecture, Japan. A logistic regression model is applied to create the models of the relationships between landslide-conditioning factors and landslide occurrence. The pre-existing landslide bodies and the area under the relative operative characteristic (ROC) curve were used to evaluate the performance of all the models with different m and c. The results revealed that Model no. 4 (m=1.9, c=4) and Model no. 5 (m=1.9, c=5) have significantly high classification accuracies, i.e., 90.0%. Moreover, over 30% of the landslide bodies were grouped under the very high susceptibility zone. Otherwise, Model no. 4 and Model no. 5 had higher area under the ROC curve (AUC) values, which were 0.78 and 0.79, respectively. Therefore, Model no. 4 and Model no. 5 offer better model results for landslide susceptibility mapping. Maps derived from Model no. 4 and Model no. 5 would offer the local authorities crucial information for city planning and development.

Transport spatial model for the definition of green routes for city logistics centers

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

Pamučar, Dragan, E-mail: dpamucar@gmail.com; Gigović, Ljubomir, E-mail: gigoviclj@gmail.com; Ćirović, Goran, E-mail: cirovic@sezampro.rs

This paper presents a transport spatial decision support model (TSDSM) for carrying out the optimization of green routes for city logistics centers. The TSDSM model is based on the integration of the multi-criteria method of Weighted Linear Combination (WLC) and the modified Dijkstra algorithm within a geographic information system (GIS). The GIS is used for processing spatial data. The proposed model makes it possible to plan routes for green vehicles and maximize the positive effects on the environment, which can be seen in the reduction of harmful gas emissions and an increase in the air quality in highly populated areas.more » The scheduling of delivery vehicles is given as a problem of optimization in terms of the parameters of: the environment, health, use of space and logistics operating costs. Each of these input parameters was thoroughly examined and broken down in the GIS into criteria which further describe them. The model presented here takes into account the fact that logistics operators have a limited number of environmentally friendly (green) vehicles available. The TSDSM was tested on a network of roads with 127 links for the delivery of goods from the city logistics center to the user. The model supports any number of available environmentally friendly or environmentally unfriendly vehicles consistent with the size of the network and the transportation requirements. - Highlights: • Model for routing light delivery vehicles in urban areas. • Optimization of green routes for city logistics centers. • The proposed model maximizes the positive effects on the environment. • The model was tested on a real network.« less

Stressful working conditions and poor self-rated health among financial services employees.

PubMed

Silva, Luiz Sérgio; Barreto, Sandhi Maria

2012-06-01

To assess the association between exposure to adverse psychosocial working conditions and poor self-rated health among bank employees. A cross-sectional study including a sample of 2,054 employees of a government bank was conducted in 2008. Self-rated health was assessed by a single question: "In general, would you say your health is (...)." Exposure to adverse psychosocial working conditions was evaluated by the effort-reward imbalance model and the demand-control model. Information on other independent variables was obtained through a self-administered semi-structured questionnaire. A multiple logistic regression analysis was performed and odds ratio calculated to assess independent associations between adverse psychosocial working conditions and poor self-rated health. The overall prevalence of poor self-rated health was 9%, with no significant gender difference. Exposure to high demand and low control environment at work was associated with poor self-rated health. Employees with high effort-reward imbalance and overcommitment also reported poor self-rated health, with a dose-response relationship. Social support at work was inversely related to poor self-rated health, with a dose-response relationship. Exposure to adverse psychosocial work factors assessed based on the effort-reward imbalance model and the demand-control model is independently associated with poor self-rated health among the workers studied.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Research on reverse logistics location under uncertainty environment based on grey prediction

NASA Astrophysics Data System (ADS)

Zhenqiang, Bao; Congwei, Zhu; Yuqin, Zhao; Quanke, Pan

This article constructs reverse logistic network based on uncertain environment, integrates the reverse logistics network and distribution network, and forms a closed network. An optimization model based on cost is established to help intermediate center, manufacturing center and remanufacturing center make location decision. A gray model GM (1, 1) is used to predict the product holdings of the collection points, and then prediction results are carried into the cost optimization model and a solution is got. Finally, an example is given to verify the effectiveness and feasibility of the model.

Humanitarian response: improving logistics to save lives.

PubMed

McCoy, Jessica

2008-01-01

Each year, millions of people worldwide are affected by disasters, underscoring the importance of effective relief efforts. Many highly visible disaster responses have been inefficient and ineffective. Humanitarian agencies typically play a key role in disaster response (eg, procuring and distributing relief items to an affected population, assisting with evacuation, providing healthcare, assisting in the development of long-term shelter), and thus their efficiency is critical for a successful disaster response. The field of disaster and emergency response modeling is well established, but the application of such techniques to humanitarian logistics is relatively recent. This article surveys models of humanitarian response logistics and identifies promising opportunities for future work. Existing models analyze a variety of preparation and response decisions (eg, warehouse location and the distribution of relief supplies), consider both natural and manmade disasters, and typically seek to minimize cost or unmet demand. Opportunities to enhance the logistics of humanitarian response include the adaptation of models developed for general disaster response; the use of existing models, techniques, and insights from the literature on commercial supply chain management; the development of working partnerships between humanitarian aid organizations and private companies with expertise in logistics; and the consideration of behavioral factors relevant to a response. Implementable, realistic models that support the logistics of humanitarian relief can improve the preparation for and the response to disasters, which in turn can save lives.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

Predicting the geographical distribution of two invasive termite species from occurrence data.

PubMed

Tonini, Francesco; Divino, Fabio; Lasinio, Giovanna Jona; Hochmair, Hartwig H; Scheffrahn, Rudolf H

2014-10-01

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.

A decision support model for investment on P2P lending platform.

PubMed

Zeng, Xiangxiang; Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace-Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.

A decision support model for investment on P2P lending platform

PubMed Central

Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone. PMID:28877234

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking.

PubMed

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.

What does the Cantril Ladder measure in adolescence?

PubMed

Mazur, Joanna; Szkultecka-Dębek, Monika; Dzielska, Anna; Drozd, Mariola; Małkowska-Szkutnik, Agnieszka

2018-01-01

The Cantril Scale (CS) is a simple visual scale which makes it possible to assess general life satisfaction. The result may depend on the health, living, and studying conditions, and quality of social relations. The objective of this study is to identify key factors influencing the CS score in Polish adolescents. The survey comprised 1,423 parent-child pairs (54% girls; age range: 10-17; 67.3% urban inhabitants; 89.4% of parents were mothers). Linear and logistic models were estimated; the latter used alternative divisions into "satisfied" and "dissatisfied" with life. In addition to age and gender, child-reported KIDSCREEN-52 quality of life indexes were taken into account, along with some information provided by parents - child physical (CSHCN) and mental (SDQ) health, and family socio-economic conditions. According to the linear model, nine independent predictors, including six dimensions of KIDSCREEN-52, explain 47.2% of the variability of life satisfaction on the Cantril Scale. Self-perception was found to have a dominating influence (Δ R 2 = 0.301, p < 0.001). Important CS predictors also included Psychological Well-being (Δ R 2 = 0.088, p < 0.001) and Parent Relations (Δ R 2 = 0.041, p < 0.001). The impact of socioeconomic factors was more visible in boys and in older adolescents. According to logistic models, the key factors enhancing the chance of higher life satisfaction are Moods and Emotions (cut-off point CS > 5) and School Environment (CS > 8 points). None of the models indicated a relationship between the CS and physical health. The Cantril Scale can be considered a useful measurement tool in a broad approach to psychosocial adolescent health.

Development of a Bayesian model to estimate health care outcomes in the severely wounded

PubMed Central

Stojadinovic, Alexander; Eberhardt, John; Brown, Trevor S; Hawksworth, Jason S; Gage, Frederick; Tadaki, Douglas K; Forsberg, Jonathan A; Davis, Thomas A; Potter, Benjamin K; Dunne, James R; Elster, E A

2010-01-01

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations. PMID:21197361

The prevalence of self-reported chronic conditions among Arab, Chaldean, and African Americans in southeast Michigan.

PubMed

Jamil, Hikmet; Dallo, Florence; Fakhouri, Monty; Templin, Thomas; Khoury, Radwan; Fakhouri, Haifa

2009-01-01

While there is a plethora of research on the prevalence of individual chronic conditions, studies that examine the clustering of these conditions are lacking, especially among immigrant, minority groups. Cross-sectional, convenience sample. A self-administered survey was distributed at churches, mosques, and small businesses. Arabs (n = 1383), Chaldeans (n = 868), Blacks (n = 809) and Whites (n = 220) in southeast Michigan. We estimated the prevalence of hypertension, high cholesterol, heart disease, diabetes, asthma, and depression. Using a logistic regression model, we estimated odds ratios and 95% confidence intervals for the association between ethnicity and reporting one or more chronic conditions before and after adjusting for demographic, socioeconomic status, health care, chronic conditions, and health behavior variables. The overall age and sex-adjusted prevalence of having one or more chronic conditions was 44%. Estimates were lower for Chaldeans (32%) compared to Arabs (44%), Whites and Blacks (50% for each group). In the fully adjusted model, Chaldeans were less likely (OR = 0.62; 95% CI = 0.43-0.89) to report having one more chronic conditions compared to Whites. Future studies should employ probability samples, and should collect more detailed sociodemographic and acculturation data, which influence the relationship between race/ethnicity and the prevalence of chronic conditions.

Linear Logistic Test Modeling with R

ERIC Educational Resources Information Center

Baghaei, Purya; Kubinger, Klaus D.

2015-01-01

The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Logistics of a Lunar Based Solar Power Satellite Scenario

NASA Technical Reports Server (NTRS)

Melissopoulos, Stefanos

1995-01-01

A logistics system comprised of two orbital stations for the support of a 500 GW space power satellite scenario in a geostationary orbit was investigated in this study. A subsystem mass model, a mass flow model and a life cycle cost model were developed. The results regarding logistics cost and burden rates show that the transportation cost contributed the most (96%) to the overall cost of the scenario. The orbital stations at a geostationary and at a lunar orbit contributed 4 % to that cost.

Factors associated with inadequate work ability among women in the clothing industry.

PubMed

Augusto, Viviane Gontijo; Sampaio, Rosana Ferreira; Ferreira, Fabiane Ribeiro; Kirkwood, Renata Noce; César, Cibele Comini

2015-01-01

Work ability depends on a balance between individual resources and work demands. This study evaluated factors that are associated with inadequate work ability among workers in the clothing industry. We conducted a cross-sectional observational study of 306 workers in 40 small and medium-sized enterprises. We assessed work ability, individual resources, physical and psychosocial demands, and aspects of life outside work using a binary logistic regression model with hierarchical data entry. The mean work ability was 42.5 (SD=3.5); when adjusted for age, only 11% of the workers showed inadequate work ability. The final model revealed that smoking, high isometric physical load, and poor physical environmental conditions were the most significant predictors of inadequate work ability. Good working conditions and worker education must be implemented to eliminate factors that can be changed and that have a negative impact on work ability. These initiatives include anti-smoking measures, improved postures at work, and better physical environmental conditions.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Panic anxiety, under the weather?

NASA Astrophysics Data System (ADS)

Bulbena, A.; Pailhez, G.; Aceña, R.; Cunillera, J.; Rius, A.; Garcia-Ribera, C.; Gutiérrez, J.; Rojo, C.

2005-03-01

The relationship between weather conditions and psychiatric disorders has been a continuous subject of speculation due to contradictory findings. This study attempts to further clarify this relationship by focussing on specific conditions such as panic attacks and non-panic anxiety in relation to specific meteorological variables. All psychiatric emergencies attended at a general hospital in Barcelona (Spain) during 2002 with anxiety as main complaint were classified as panic or non-panic anxiety according to strict independent and retrospective criteria. Both groups were assessed and compared with meteorological data (wind speed and direction, daily rainfall, temperature, humidity and solar radiation). Seasons and weekend days were also included as independent variables. Non-parametric statistics were used throughout since most variables do not follow a normal distribution. Logistic regression models were applied to predict days with and without the clinical condition. Episodes of panic were three times more common with the poniente wind (hot wind), twice less often with rainfall, and one and a half times more common in autumn than in other seasons. These three trends (hot wind, rainfall and autumn) were accumulative for panic episodes in a logistic regression formula. Significant reduction of episodes on weekends was found only for non-panic episodes. Panic attacks, unlike other anxiety episodes, in a psychiatric emergency department in Barcelona seem to show significant meteorotropism. Assessing specific disorders instead of overall emergencies or other variables of a more general quality could shed new light on the relationship between weather conditions and behaviour.

An integrative fuzzy Kansei engineering and Kano model for logistics services

NASA Astrophysics Data System (ADS)

Hartono, M.; Chuan, T. K.; Prayogo, D. N.; Santoso, A.

2017-11-01

Nowadays, customer emotional needs (known as Kansei) in product and especially in services become a major concern. One of the emerging services is the logistics services. In obtaining a global competitive advantage, logistics services should understand and satisfy their customer affective impressions (Kansei). How to capture, model and analyze the customer emotions has been well structured by Kansei Engineering, equipped with Kano model to strengthen its methodology. However, its methodology lacks of the dynamics of customer perception. More specifically, there is a criticism of perceived scores on user preferences, in both perceived service quality and Kansei response, whether they represent an exact numerical value. Thus, this paper is proposed to discuss an approach of fuzzy Kansei in logistics service experiences. A case study in IT-based logistics services involving 100 subjects has been conducted. Its findings including the service gaps accompanied with prioritized improvement initiatives are discussed.

Logistics, electronic commerce, and the environment

NASA Astrophysics Data System (ADS)

Sarkis, Joseph; Meade, Laura; Talluri, Srinivas

2002-02-01

Organizations realize that a strong supporting logistics or electronic logistics (e-logistics) function is important from both commercial and consumer perspectives. The implications of e-logistics models and practices cover the forward and reverse logistics functions of organizations. They also have direct and profound impact on the natural environment. This paper will focus on a discussion of forward and reverse e-logistics and their relationship to the natural environment. After discussion of the many pertinent issues in these areas, directions of practice and implications for study and research are then described.

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

Not Available

Greenland's Mineral Resources Administration (MRA) plans a series of licensing rounds off western Greenland. Meanwhile, the MRA has declared the Jameson Land basin of east central Greenland as open acreage. Greenland Geological Survey (GGU), Copenhagen, has prepared a report on the geographical conditions, logistics, exploration history, and geological development of Jameson Land. The article emphasizes source and reservoir rocks, conceptual play types with six seismic examples, and thermal history with basin modeling. It also includes two interpreted regional seismic lines, a geological and an aeromagnetic map, depth structure, and isopach maps of selected formations.

Chronic condition combinations and health care expenditures and out-of-pocket spending burden among adults, Medical Expenditure Panel Survey, 2009 and 2011.

PubMed

Meraya, Abdulkarim M; Raval, Amit D; Sambamoorthi, Usha

2015-01-29

Little is known about how combinations of chronic conditions in adults affect total health care expenditures. Our objective was to estimate the annual average total expenditures and out-of-pocket spending burden among US adults by combinations of conditions. We conducted a cross-sectional study using 2009 and 2011 data from the Medical Expenditure Panel Survey. The sample consisted of 9,296 adults aged 21 years or older with at least 2 of the following 4 highly prevalent chronic conditions: arthritis, diabetes mellitus, heart disease, and hypertension. Unadjusted and adjusted regression techniques were used to examine the association between chronic condition combinations and log-transformed total expenditures. Logistic regressions were used to analyze the relationship between chronic condition combinations and high out-of-pocket spending burden. Among adults with chronic conditions, adults with all 4 conditions had the highest average total expenditures ($20,016), whereas adults with diabetes/hypertension had the lowest annual total expenditures ($7,116). In adjusted models, adults with diabetes/hypertension and hypertension/arthritis had lower health care expenditures than adults with diabetes/heart disease (P < .001). In adjusted models, adults with all 4 conditions had higher expenditures compared with those with diabetes and heart disease. However, the difference was only marginally significant (P = .04). Among adults with arthritis, diabetes, heart disease, and hypertension, total health care expenditures differed by type of chronic condition combinations. For individuals with multiple chronic conditions, such as heart disease and diabetes, new models of care management are needed to reduce the cost burden on the payers.

Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

PubMed

Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

2017-04-01

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.

Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning

ERIC Educational Resources Information Center

MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R.

2015-01-01

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…

Some Observations on the Identification and Interpretation of the 3PL IRT Model

ERIC Educational Resources Information Center

Azevedo, Caio Lucidius Naberezny

2009-01-01

The paper by Maris, G., & Bechger, T. (2009) entitled, "On the Interpreting the Model Parameters for the Three Parameter Logistic Model," addressed two important questions concerning the three parameter logistic (3PL) item response theory (IRT) model (and in a broader sense, concerning all IRT models). The first one is related to the model…

A High Resolution Ammunition Resupply Model.

DTIC Science & Technology

1982-03-01

LOU ............... 104 3. Requests for Resupply . . ........ 108 a. Weapon Systems . . . . . . . . . . . . 108 b. Platoon . ... 109 c. Company...essence, the fundamental question, "Can it be done?", is never adequately answered. B. LOGISTICS MODELS Current logistics models then, although...19 .._ " Development of a detailed model that responds to requests for ammunition resupply, maintains a simplified stockage system , and models the

Modeling particulate matter emissions during mineral loading process under weak wind simulation.

PubMed

Zhang, Xiaochun; Chen, Weiping; Ma, Chun; Zhan, Shuifen

2013-04-01

The quantification of particulate matter emissions from mineral handling is an important problem for the quantification of global emissions on industrial sites. Mineral particulate matter emissions could adversely impact environmental quality in mining regions, transport regions, and even on a global scale. Mineral loading is an important process contributing to mineral particulate matter emissions, especially under weak wind conditions. Mathematical models are effective ways to evaluate particulate matter emissions during the mineral loading process. The currently used empirical models based on the form of a power function do not predict particulate matter emissions accurately under weak wind conditions. At low particulate matter emissions, the models overestimated, and at high particulate matter emissions, the models underestimated emission factors. We conducted wind tunnel experiments to evaluate the particulate matter emission factors for the mineral loading process. A new approach based on the mathematical form of a logistical function was developed and tested. It provided a realistic depiction of the particulate matter emissions during the mineral loading process, accounting for fractions of fine mineral particles, dropping height, and wind velocity. Copyright © 2013 Elsevier B.V. All rights reserved.

Reverse logistics system planning for recycling computers hardware: A case study

NASA Astrophysics Data System (ADS)

Januri, Siti Sarah; Zulkipli, Faridah; Zahari, Siti Meriam; Shamsuri, Siti Hajar

2014-09-01

This paper describes modeling and simulation of reverse logistics networks for collection of used computers in one of the company in Selangor. The study focuses on design of reverse logistics network for used computers recycling operation. Simulation modeling, presented in this work allows the user to analyze the future performance of the network and to understand the complex relationship between the parties involved. The findings from the simulation suggest that the model calculates processing time and resource utilization in a predictable manner. In this study, the simulation model was developed by using Arena simulation package.

Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

PubMed

Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians Postgraduate Press, Inc.

Factors associated with preventable infant death: a multiple logistic regression.

PubMed

Vidal E Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

2018-01-01

OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight.

Evaluation of Female Breast Cancer Risk Among the Betel Quid Chewer: A Bio-Statistical Assessment in Assam, India.

PubMed

Rajbongshi, Nijara; Mahanta, Lipi B; Nath, Dilip C

2015-06-01

Breast cancer is the most commonly diagnosed cancer among the female population of Assam, India. Chewing of betel quid with or without tobacco is common practice among female population of this region. Moreoverthe method of preparing the betel quid is different from other parts of the country.So matched case control study is conducted to analyse whetherbetel quid chewing plays a significant role in the high incidence of breast cancer occurrences in Assam. Here, controls are matched to the cases by age at diagnosis (±5 years), family income and place of residence with matching ratio 1:1. Conditional logistic regression models and odd ratios (OR) was used to draw conclusions. It is observed that cases are more habituated to chewing habits than the controls.Further the conditional logistic regression analysis reveals that betel quid chewer faces 2.353 times more risk having breast cancer than the non-chewer with p value 0.0003 (95% CI 1.334-4.150). Though the female population in Assam usually does not smoke, the addictive habits typical to this region have equal effect on the occurrence of breast cancer.

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

PubMed Central

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

An inexact reverse logistics model for municipal solid waste management systems.

PubMed

Zhang, Yi Mei; Huang, Guo He; He, Li

2011-03-01

This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM). Waste managers, suppliers, industries and distributors were involved in strategic planning and operational execution through reverse logistics management. All the parameters were assumed to be intervals to quantify the uncertainties in the optimization process and solutions in IRWM. To solve this model, a piecewise interval programming was developed to deal with Min-Min functions in both objectives and constraints. The application of the model was illustrated through a classical municipal solid waste management case. With different cost parameters for landfill and the WTE, two scenarios were analyzed. The IRWM could reflect the dynamic and uncertain characteristics of MSW management systems, and could facilitate the generation of desired management plans. The model could be further advanced through incorporating methods of stochastic or fuzzy parameters into its framework. Design of multi-waste, multi-echelon, multi-uncertainty reverse logistics model for waste management network would also be preferred. Copyright © 2010 Elsevier Ltd. All rights reserved.

A numerical study of biofilm growth in a microgravity environment

NASA Astrophysics Data System (ADS)

Aristotelous, A. C.; Papanicolaou, N. C.

2017-10-01

A mathematical model is proposed to investigate the effect of microgravity on biofilm growth. We examine the case of biofilm suspended in a quiescent aqueous nutrient solution contained in a rectangular tank. The bacterial colony is assumed to follow logistic growth whereas nutrient absorption is assumed to follow Monod kinetics. The problem is modeled by a coupled system of nonlinear partial differential equations in two spatial dimensions solved using the Discontinuous Galerkin Finite Element method. Nutrient and biofilm concentrations are computed in microgravity and normal gravity conditions. A preliminary quantitative relationship between the biofilm concentration and the gravity field intensity is derived.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

MESSOC capabilities and results. [Model for Estimating Space Station Opertions Costs

NASA Technical Reports Server (NTRS)

Shishko, Robert

1990-01-01

MESSOC (Model for Estimating Space Station Operations Costs) is the result of a multi-year effort by NASA to understand and model the mature operations cost of Space Station Freedom. This paper focuses on MESSOC's ability to contribute to life-cycle cost analyses through its logistics equations and databases. Together, these afford MESSOC the capability to project not only annual logistics costs for a variety of Space Station scenarios, but critical non-cost logistics results such as annual Station maintenance crewhours, upweight/downweight, and on-orbit sparing availability as well. MESSOC results using current logistics databases and baseline scenario have already shown important implications for on-orbit maintenance approaches, space transportation systems, and international operations cost sharing.

Requirement analysis for the one-stop logistics management of fresh agricultural products

NASA Astrophysics Data System (ADS)

Li, Jun; Gao, Hongmei; Liu, Yuchuan

2017-08-01

Issues and concerns for food safety, agro-processing, and the environmental and ecological impact of food production have been attracted many research interests. Traceability and logistics management of fresh agricultural products is faced with the technological challenges including food product label and identification, activity/process characterization, information systems for the supply chain, i.e., from farm to table. Application of one-stop logistics service focuses on the whole supply chain process integration for fresh agricultural products is studied. A collaborative research project for the supply and logistics of fresh agricultural products in Tianjin was performed. Requirement analysis for the one-stop logistics management information system is studied. The model-driven business transformation, an approach uses formal models to explicitly define the structure and behavior of a business, is applied for the review and analysis process. Specific requirements for the logistic management solutions are proposed. Development of this research is crucial for the solution of one-stop logistics management information system integration platform for fresh agricultural products.

Pesticide poisoning and respiratory disorders in Colorado farm residents.

PubMed

Beseler, C L; Stallones, L

2009-10-01

Respiratory hazards significantly contribute to the burden of occupational disease among farmers. Pesticide exposure has been linked to an increased prevalence of respiratory symptoms in several farming populations. The purpose of this study was to evaluate the association between respiratory symptoms and pesticide poisoning in a cross-sectional survey of farm residents. A total of 761 farm operators and their spouses, representing 479 farms in northeastern Colorado, were recruited from 1993 to 1997. A personal interview asked whether the resident had experienced a pesticide poisoning and several respiratory conditions including cough, allergy, wheeze, and organic dust toxic syndrome (ODTS). Spirometry testing was performed on 196 individuals. Logistic regression was used to model the association of pesticide poisoning with respiratory conditions, and linear regression was used to model the relationship of pesticide poisoning and forced vital capacity (FVC) and forced expiratory volume (FEV1). In unadjusted models, pesticide poisoning was associated with all four respiratory conditions, and stayed significant in adjusted models of allergies and cough in non-smokers. In age- and gender-adjusted models, pesticide poisoning was significantly associated with lower FVC and FEV1 in current smokers and in those who were not heavy drinkers. Although this study should be reproduced in a larger sample, it suggests that further evaluation of the respiratory effects of pesticide exposure is warranted.

Change of water consumption and its potential influential factors in Shanghai: A cross-sectional study

PubMed Central

2012-01-01

Background Different water choices affect access to drinking water with different quality. Previous studies suggested social-economic status may affect the choice of domestic drinking water. The aim of this study is to investigate whether recent social economic changes in China affect residents’ drinking water choices. Methods We conducted a cross-sectional survey to investigate residents’ water consumption behaviour in 2011. Gender, age, education, personal income, housing condition, risk perception and personal preference of a certain type of water were selected as potential influential factors. Univariate and backward stepwise logistic regression analyses were performed to analyse the relation between these factors and different drinking water choices. Basic information was compared with that of a historical survey in the same place in 2001. Self-reported drinking-water-related diarrhoea was found correlated with different water choices and water hygiene treatment using chi-square test. Results The percentage of tap water consumption remained relatively stable and a preferred choice, with 58.99% in 2001 and 58.25% in 2011. The percentage of bottled/barrelled water consumption was 36.86% in 2001 and decreased to 25.75% in 2011. That of household filtrated water was 4.15% in 2001 and increased to 16.00% in 2011. Logistic regression model showed strong correlation between one’s health belief and drinking water choices (P < 0.001). Age, personal income, education, housing condition, risk perception also played important roles (P < 0.05) in the models. Drinking-water-related diarrhoea was found in all types of water and improper water hygiene behaviours still existed among residents. Conclusions Personal health belief, housing condition, age, personal income, education, taste and if worm ever founded in tap water affected domestic drinking water choices in Shanghai. PMID:22708830

Change of water consumption and its potential influential factors in Shanghai: a cross-sectional study.

PubMed

Chen, Hanyi; Zhang, Yaying; Ma, Linlin; Liu, Fangmin; Zheng, Weiwei; Shen, Qinfeng; Zhang, Hongmei; Wei, Xiao; Tian, Dajun; He, Gengsheng; Qu, Weidong

2012-06-18

Different water choices affect access to drinking water with different quality. Previous studies suggested social-economic status may affect the choice of domestic drinking water. The aim of this study is to investigate whether recent social economic changes in China affect residents' drinking water choices. We conducted a cross-sectional survey to investigate residents' water consumption behaviour in 2011. Gender, age, education, personal income, housing condition, risk perception and personal preference of a certain type of water were selected as potential influential factors. Univariate and backward stepwise logistic regression analyses were performed to analyse the relation between these factors and different drinking water choices. Basic information was compared with that of a historical survey in the same place in 2001. Self-reported drinking-water-related diarrhoea was found correlated with different water choices and water hygiene treatment using chi-square test. The percentage of tap water consumption remained relatively stable and a preferred choice, with 58.99% in 2001 and 58.25% in 2011. The percentage of bottled/barrelled water consumption was 36.86% in 2001 and decreased to 25.75% in 2011. That of household filtrated water was 4.15% in 2001 and increased to 16.00% in 2011. Logistic regression model showed strong correlation between one's health belief and drinking water choices (P < 0.001). Age, personal income, education, housing condition, risk perception also played important roles (P < 0.05) in the models. Drinking-water-related diarrhoea was found in all types of water and improper water hygiene behaviours still existed among residents. Personal health belief, housing condition, age, personal income, education, taste and if worm ever founded in tap water affected domestic drinking water choices in Shanghai.

Participation and retention of youth with perinatal HIV infection in mental health research studies: the IMPAACT P1055 psychiatric comorbidity study.

PubMed

Williams, Paige L; Chernoff, Miriam; Angelidou, Konstantia; Brouwers, Pim; Kacanek, Deborah; Deygoo, Nagamah S; Nachman, Sharon; Gadow, Kenneth D

2013-07-01

Obtaining accurate estimates of mental health problems among youth perinatally infected with HIV (PHIV) helps clinicians develop targeted interventions but requires enrollment and retention of representative youth into research studies. The study design for IMPAACT P1055, a US-based, multisite prospective study of psychiatric symptoms among PHIV youth and uninfected controls aged 6 to 17 years old, is described. Participants were compared with nonparticipants by demographic characteristics and reasons were summarized for study refusal. Adjusted logistic regression models were used to evaluate the association of psychiatric symptoms and other factors with loss to follow-up (LTFU). Among 2281 youth screened between 2005 and 2006 at 29 IMPAACT research sites, 580 (25%) refused to participate, primarily because of time constraints. Among 1162 eligible youth approached, 582 (50%) enrolled (323 PHIV and 259 Control), with higher participation rates for Hispanic youth. Retention at 2 years was significantly higher for PHIV than Controls (84% vs 77%, P = 0.03). In logistic regression models adjusting for sociodemographic characteristics and HIV status, youth with any self-assessed psychiatric condition had higher odds of LTFU compared with those with no disorder (adjusted odds ratio = 1.56, 95% confidence interval: 1.00 to 2.43). Among PHIV youth, those with any psychiatric condition had 3-fold higher odds of LTFU (adjusted odds ratio = 3.11, 95% confidence interval: 1.61 to 6.01). Enrollment and retention of PHIV youth into mental health research studies is challenging for those with psychiatric conditions and may lead to underestimated risks for mental health problems. Creative approaches for engaging HIV-infected youth and their families are required for ensuring representative study populations.

Asymptotic proportionality (weak ergodicity) and conditional asymptotic equality of solutions to time-heterogeneous sublinear difference and differential equations

NASA Astrophysics Data System (ADS)

Thieme, Horst R.

The concept of asymptotic proportionality and conditional asymptotic equality which is presented here aims at making global asymptotic stability statements for time-heterogeneous difference and differential equations. For such non-autonomous problems (apart from special cases) no prominent special solutions (equilibra, periodic solutions) exist which are natural candidates for the asymptotic behaviour of arbitrary solutions. One way out of this dilemma consists in looking for conditions under which any two solutions to the problem (with different initial conditions) behave in a similar or even the same way as time tends to infinity. We study a general sublinear difference equation in an ordered Banach space and, for illustration, time-heterogeneous versions of several well-known differential equations modelling the spread of gonorrhea in a heterogeneous population, the spread of a vector-borne infectious disease, and the dynamics of a logistically growing spatially diffusing population.

An Extension of the Concept of Specific Objectivity.

ERIC Educational Resources Information Center

Irtel, Hans

1995-01-01

Comparisons of subjects are specifically objective if they do not depend on the items involved. Such comparisons are not restricted to the one-parameter logistic latent trait model but may also be defined within ordinal independence models and even within the two-parameter logistic model. (Author)

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

A Comparison of the One-and Three-Parameter Logistic Models on Measures of Test Efficiency.

ERIC Educational Resources Information Center

Benson, Jeri

Two methods of item selection were used to select sets of 40 items from a 50-item verbal analogies test, and the resulting item sets were compared for relative efficiency. The BICAL program was used to select the 40 items having the best mean square fit to the one parameter logistic (Rasch) model. The LOGIST program was used to select the 40 items…

Development of a protocol for improving the clinical utility of posturography as a fall-risk screening tool.

PubMed

Bigelow, Kimberly Edginton; Berme, Necip

2011-02-01

The usefulness of posturography in the clinical screening of older adults for fall risk has been limited by a lack of standardization in testing methodology and data reporting. This study determines which testing condition and postural sway measures best differentiate recurrent fallers and nonrecurrent fallers. One hundred and fifty older adults were categorized based on their fall status in the past year. Participants performed four quiet-standing tasks, eyes open and eyes closed in both comfortable and narrow stance, for 60 seconds while standing on a force-measuring platform. Traditional and fractal measures were calculated from the center of pressure data. Logistic regression was performed to determine the model for each condition that best discriminated between recurrent fallers and nonrecurrent fallers. The eyes closed comfortable stance condition, with its associated model, best differentiated recurrent fallers and nonrecurrent fallers. Medial-lateral sway velocity, anterior-posterior short-term α-scaling exponent, medial-lateral short-term α-scaling exponent, mean frequency, body mass index, and age were included in this model. Sensitivity of the model was 75%, and specificity was 94%. This resulting model demonstrates potential to differentiate recurrent fallers and nonrecurrent fallers in an eyes closed comfortable stance condition. The inclusion of traditional sway parameters, fractal measures, and personal characteristics in this model demonstrates the importance of considering multiple descriptions of postural stability together rather than using only a single measure to establish fall risk.

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

PubMed

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

PubMed Central

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults’ belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking. PMID:27853440

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Ability Estimation and Item Calibration Using the One and Three Parameter Logistic Models: A Comparative Study. Research Report 77-1.

ERIC Educational Resources Information Center

Reckase, Mark D.

Latent trait model calibration procedures were used on data obtained from a group testing program. The one-parameter model of Wright and Panchapakesan and the three-parameter logistic model of Wingersky, Wood, and Lord were selected for comparison. These models and their corresponding estimation procedures were compared, using actual and simulated…

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

Investigation of shipping accident injury severity and mortality.

PubMed

Weng, Jinxian; Yang, Dong

2015-03-01

Shipping movements are operated in a complex and high-risk environment. Fatal shipping accidents are the nightmares of seafarers. With ten years' worldwide ship accident data, this study develops a binary logistic regression model and a zero-truncated binomial regression model to predict the probability of fatal shipping accidents and corresponding mortalities. The model results show that both the probability of fatal accidents and mortalities are greater for collision, fire/explosion, contact, grounding, sinking accidents occurred in adverse weather conditions and darkness conditions. Sinking has the largest effects on the increment of fatal accident probability and mortalities. The results also show that the bigger number of mortalities is associated with shipping accidents occurred far away from the coastal area/harbor/port. In addition, cruise ships are found to have more mortalities than non-cruise ships. The results of this study are beneficial for policy-makers in proposing efficient strategies to prevent fatal shipping accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis.

PubMed

Xu, Di; Chai, Meiyun; Dong, Zhujun; Rahman, Md Maksudur; Yu, Xi; Cai, Junmeng

2018-06-04

The kinetic compensation effect in the logistic distributed activation energy model (DAEM) for lignocellulosic biomass pyrolysis was investigated. The sum of square error (SSE) surface tool was used to analyze two theoretically simulated logistic DAEM processes for cellulose and xylan pyrolysis. The logistic DAEM coupled with the pattern search method for parameter estimation was used to analyze the experimental data of cellulose pyrolysis. The results showed that many parameter sets of the logistic DAEM could fit the data at different heating rates very well for both simulated and experimental processes, and a perfect linear relationship between the logarithm of the frequency factor and the mean value of the activation energy distribution was found. The parameters of the logistic DAEM can be estimated by coupling the optimization method and isoconversional kinetic methods. The results would be helpful for chemical kinetic analysis using DAEM. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scenario analysis and disaster preparedness for port and maritime logistics risk management.

PubMed

Kwesi-Buor, John; Menachof, David A; Talas, Risto

2016-08-01

System Dynamics (SD) modelling is used to investigate the impacts of policy interventions on industry actors' preparedness to mitigate risks and to recover from disruptions along the maritime logistics and supply chain network. The model suggests a bi-directional relation between regulation and industry actors' behaviour towards Disaster Preparedness (DP) in maritime logistics networks. The model also showed that the level of DP is highly contingent on forecast accuracy, technology change, attitude to risk prevention, port activities, and port environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quality tracing in meat supply chains

PubMed Central

Mack, Miriam; Dittmer, Patrick; Veigt, Marius; Kus, Mehmet; Nehmiz, Ulfert; Kreyenschmidt, Judith

2014-01-01

The aim of this study was the development of a quality tracing model for vacuum-packed lamb that is applicable in different meat supply chains. Based on the development of relevant sensory parameters, the predictive model was developed by combining a linear primary model and the Arrhenius model as the secondary model. Then a process analysis was conducted to define general requirements for the implementation of the temperature-based model into a meat supply chain. The required hardware and software for continuous temperature monitoring were developed in order to use the model under practical conditions. Further on a decision support tool was elaborated in order to use the model as an effective tool in combination with the temperature monitoring equipment for the improvement of quality and storage management within the meat logistics network. Over the long term, this overall procedure will support the reduction of food waste and will improve the resources efficiency of food production. PMID:24797136

Quality tracing in meat supply chains.

PubMed

Mack, Miriam; Dittmer, Patrick; Veigt, Marius; Kus, Mehmet; Nehmiz, Ulfert; Kreyenschmidt, Judith

2014-06-13

The aim of this study was the development of a quality tracing model for vacuum-packed lamb that is applicable in different meat supply chains. Based on the development of relevant sensory parameters, the predictive model was developed by combining a linear primary model and the Arrhenius model as the secondary model. Then a process analysis was conducted to define general requirements for the implementation of the temperature-based model into a meat supply chain. The required hardware and software for continuous temperature monitoring were developed in order to use the model under practical conditions. Further on a decision support tool was elaborated in order to use the model as an effective tool in combination with the temperature monitoring equipment for the improvement of quality and storage management within the meat logistics network. Over the long term, this overall procedure will support the reduction of food waste and will improve the resources efficiency of food production.

Factors affecting choice between ureterostomy, ileal conduit and continent reservoir after radical cystectomy: Japanese series.

PubMed

Sugihara, Toru; Yasunaga, Hideo; Horiguchi, Hiromasa; Fujimura, Tetsuya; Fushimi, Kiyohide; Yu, Changhong; Kattan, Michael W; Homma, Yukio

2014-12-01

Little is known about the disparity of choices between three urinary diversions after radical cystectomy, focusing on patient and institutional factors. We identified urothelial carcinoma patients who received radical cystectomy with cutaneous ureterostomy, ileal conduit or continent reservoir using the Japanese Diagnosis Procedure Combination database from 2007 to 2012. Data comprised age, sex, comorbidities (converted into the Charlson index), TNM classification (converted into oncological stage), hospitals' academic status, hospital volume, bed volume and geographical region. Multivariate ordinal logistic regression analyses fitted with the proportional odds model were performed to analyze factors affecting urinary diversion choices. For dependent variables, the three diversions were converted into an ordinal variable in order of complexity: cutaneous ureterostomy (reference), ileal conduit and continent reservoir. Geographical variations were also examined by multivariate logistic regression models. A total of 4790 patients (1131 cutaneous ureterostomies [23.6 %], 2970 ileal conduits [62.0 %] and 689 continent reservoirs [14.4 %]) were included. Ordinal logistic regression analyses showed that male sex, lower age, lower Charlson index, early tumor stage, higher hospital volume (≥3.4 cases/year) and larger bed volume (≥450 beds) were significantly associated with the preference of more complex urinary diversion. Significant geographical disparity was also found. Good patient condition and early oncological status, as well as institutional factors, including high hospital volume, large bed volume and specific geographical regions, are independently related to the likelihood of choosing complex diversions. Recognizing this disparity would help reinforce the need for clinical practice uniformity.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Modeling, analysis, and simulation of the co-development of road networks and vehicle ownership

NASA Astrophysics Data System (ADS)

Xu, Mingtao; Ye, Zhirui; Shan, Xiaofeng

2016-01-01

A two-dimensional logistic model is proposed to describe the co-development of road networks and vehicle ownership. The endogenous interaction between road networks and vehicle ownership and how natural market forces and policies transformed into their co-development are considered jointly in this model. If the involved parameters satisfy a certain condition, the proposed model can arrive at a steady equilibrium level and the final development scale will be within the maximum capacity of an urban traffic system; otherwise, the co-development process will be unstable and even manifest chaotic behavior. Then sensitivity tests are developed to determine the proper values for a series of parameters in this model. Finally, a case study, using Beijing City as an example, is conducted to explore the applicability of the proposed model to the real condition. Results demonstrate that the proposed model can effectively simulate the co-development of road network and vehicle ownership for Beijing City. Furthermore, we can obtain that their development process will arrive at a stable equilibrium level in the years 2040 and 2045 respectively, and the equilibrium values are within the maximum capacity.

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

NASA Astrophysics Data System (ADS)

Johansen, Kasper; Grove, James; Denham, Robert; Phinn, Stuart

2013-01-01

Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R2=0.60, n=41), ground vegetation cover (R=0.43, n=41), bank width/height ratio (R=0.41, n=41), and valley confinement (producer's accuracy=100%, n=9). Cross-validation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

Strategies on the Implementation of China's Logistics Information Network

NASA Astrophysics Data System (ADS)

Dong, Yahui; Li, Wei; Guo, Xuwen

The economic globalization and trend of e-commerce network have determined that the logistics industry will be rapidly developed in the 21st century. In order to achieve the optimal allocation of resources, a worldwide rapid and sound customer service system should be established. The establishment of a corresponding modern logistics system is the inevitable choice of this requirement. It is also the inevitable choice for the development of modern logistics industry in China. The perfect combination of modern logistics and information network can better promote the development of the logistics industry. Through the analysis of Status of Logistics Industry in China, this paper summed up the domestic logistics enterprise logistics information system in the building of some common problems. According to logistics information systems planning methods and principles set out logistics information system to optimize the management model.

Development of Performance Assessments in Science: Conceptual, Practical, and Logistical Issues.

ERIC Educational Resources Information Center

Solano-Flores, Guillermo; Shavelson, Richard J.

1997-01-01

Conceptual, practical, and logistical issues in the development of science performance assessments (SPAs) are discussed. The conceptual framework identifies task, response format, and scoring system as components, and conceives of SPAs as tasks that attempt to recreate conditions in which scientists work. Developing SPAs is a sophisticated effort…

Ramsay-Curve Item Response Theory for the Three-Parameter Logistic Item Response Model

ERIC Educational Resources Information Center

Woods, Carol M.

2008-01-01

In Ramsay-curve item response theory (RC-IRT), the latent variable distribution is estimated simultaneously with the item parameters of a unidimensional item response model using marginal maximum likelihood estimation. This study evaluates RC-IRT for the three-parameter logistic (3PL) model with comparisons to the normal model and to the empirical…

On Interpreting the Model Parameters for the Three Parameter Logistic Model

ERIC Educational Resources Information Center

Maris, Gunter; Bechger, Timo

2009-01-01

This paper addresses two problems relating to the interpretability of the model parameters in the three parameter logistic model. First, it is shown that if the values of the discrimination parameters are all the same, the remaining parameters are nonidentifiable in a nontrivial way that involves not only ability and item difficulty, but also the…

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study.

PubMed

Komaroff, Marina

2016-01-01

The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06-2.66). The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation.

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study

PubMed Central

Komaroff, Marina

2016-01-01

Objective. The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. Methods. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. Results. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06–2.66). Conclusions. The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation. PMID:26953120

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Simulating Sustainment for an Unmanned Logistics System Concept of Operation in Support of Distributed Operations

DTIC Science & Technology

2017-06-01

designed experiment to model and explore a ship-to-shore logistics process supporting dispersed units via three types of ULSs, which vary primarily in...systems, simulation, discrete event simulation, design of experiments, data analysis, simplekit, nearly orthogonal and balanced designs 15. NUMBER OF... designed experiment to model and explore a ship-to-shore logistics process supporting dispersed units via three types of ULSs, which vary primarily

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Derivation of the linear-logistic model and Cox's proportional hazard model from a canonical system description.

PubMed

Voit, E O; Knapp, R G

1997-08-15

The linear-logistic regression model and Cox's proportional hazard model are widely used in epidemiology. Their successful application leaves no doubt that they are accurate reflections of observed disease processes and their associated risks or incidence rates. In spite of their prominence, it is not a priori evident why these models work. This article presents a derivation of the two models from the framework of canonical modeling. It begins with a general description of the dynamics between risk sources and disease development, formulates this description in the canonical representation of an S-system, and shows how the linear-logistic model and Cox's proportional hazard model follow naturally from this representation. The article interprets the model parameters in terms of epidemiological concepts as well as in terms of general systems theory and explains the assumptions and limitations generally accepted in the application of these epidemiological models.

Overloading among crash-involved vehicles in China: identification of factors associated with overloading and crash severity.

PubMed

Zhang, Guangnan; Li, Yanyan; King, Mark J; Zhong, Qiaoting

2018-03-21

Motor vehicle overloading is correlated with the possibility of road crash occurrence and severity. Although overloading of motor vehicles is pervasive in developing nations, few empirical analyses have been performed on factors that might influence the occurrence of overloading. This study aims to address this shortcoming by seeking evidence from several years of crash data from Guangdong province, China. Data on overloading and other factors are extracted for crash-involved vehicles from traffic crash records for 2006-2010 provided by the Traffic Management Bureau in Guangdong province. Logistic regression is applied to identify risk factors for overloading in crash-involved vehicles and within these crashes to identify factors contributing to greater crash severity. Driver, vehicle, road and environmental characteristics and violation types are considered in the regression models. In addition to the basic logistic models, association analysis is employed to identify the potential interactions among different risk factors during fitting the logistic models of overloading and severity. Crash-involved vehicles driven by males from rural households and in an unsafe condition are more likely to be overloaded and to be involved in higher severity overloaded vehicle crashes. If overloaded vehicles speed, the risk of severe traffic crash casualties increases. Young drivers (aged under 25 years) in mountainous areas are more likely to be involved in higher severity overloaded vehicle crashes. This study identifies several factors associated with overloading in crash-involved vehicles and with higher severity overloading crashes and provides an important reference for future research on those specific risk factors. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

NASA Astrophysics Data System (ADS)

Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

Research challenges in municipal solid waste logistics management.

PubMed

Bing, Xiaoyun; Bloemhof, Jacqueline M; Ramos, Tania Rodrigues Pereira; Barbosa-Povoa, Ana Paula; Wong, Chee Yew; van der Vorst, Jack G A J

2016-02-01

During the last two decades, EU legislation has put increasing pressure on member countries to achieve specified recycling targets for municipal household waste. These targets can be obtained in various ways choosing collection methods, separation methods, decentral or central logistic systems, etc. This paper compares municipal solid waste (MSW) management practices in various EU countries to identify the characteristics and key issues from a waste management and reverse logistics point of view. Further, we investigate literature on modelling municipal solid waste logistics in general. Comparing issues addressed in literature with the identified issues in practice result in a research agenda for modelling municipal solid waste logistics in Europe. We conclude that waste recycling is a multi-disciplinary problem that needs to be considered at different decision levels simultaneously. A holistic view and taking into account the characteristics of different waste types are necessary when modelling a reverse supply chain for MSW recycling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Maintenance and Logistics Support for the International Monitoring System Network of the CTBTO

NASA Astrophysics Data System (ADS)

Haslinger, F.; Brely, N.; Akrawy, M.

2007-05-01

The global network of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), once completed, will consist of 321 monitoring facilities of four different technologies: hydroacoustic, seismic, infrasonic, and radionuclide. As of today, about 65% of the installations are completed and contribute data to the products issued by the International Data Centre (IDC) of the CTBTO. In order to accomplish the task to reliably collect evidence for any potential nuclear test explosion anywhere on the planet, all stations are required to perform to very high data availability requirements (at least 98% data availability over a 12-month period). To enable reaching this requirement, a three-layer concept has been developed to allow efficient support of the IMS stations: Operations, Maintenance and Logistics, and Engineering. Within this concept Maintenance and Logistics provide second level support of the stations, whereby problems arising at the station are assigned through the IMS ticket system to Maintenance if they cannot be resolved on the Operations level. Maintenance will then activate the required resources to appropriately address and ultimately resolve the problem. These resources may be equipment support contracts, other third party contracts, or the dispatch of a maintenance team. Engineering Support will be activated if the problem requires redesign of the station or after catastrophic failures when a total rebuild of a station may be necessary. In this model, Logistics Support is responsible for parts replenishment and support contract management. Logistics Support also collects and analyzes relevant failure mode and effect information, develops supportability models, and has the responsibility for document management, obsolescence, risk & quality, and configuration management, which are key elements for efficient station support. Maintenance Support in addition is responsible for maintenance strategies, for planning and oversight of the execution of preventive maintenance programs by the Station Operators, and for review of operational troubleshooting procedures used in first level support. Particular challenges for the efficient and successful Maintenance and Logistics Support of the IMS network lie in the specific political boundary conditions regulating its implementation, in the fact that all IMS facilities and their equipment are owned by the respective host countries, and in finding the appropriate balance between outsourcing services and retaining essential in-house expertise.

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

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

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.« less

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

USGS Publications Warehouse

Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

2003-01-01

Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire that could substantially reduce habitat of chipmunks over a mountain range.

Application of wireless sensor network technology in logistics information system

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2017-04-01

This paper introduces the basic concepts of active RFID (WSN-ARFID) based on wireless sensor networks and analyzes the shortcomings of the existing RFID-based logistics monitoring system. Integrated wireless sensor network technology and the scrambling point of RFID technology. A new real-time logistics detection system based on WSN and RFID, a model of logistics system based on WSN-ARFID is proposed, and the feasibility of this technology applied to logistics field is analyzed.

Preserving Institutional Privacy in Distributed binary Logistic Regression.

PubMed

Wu, Yuan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.

The application of virtual reality systems as a support of digital manufacturing and logistics

NASA Astrophysics Data System (ADS)

Golda, G.; Kampa, A.; Paprocka, I.

2016-08-01

Modern trends in development of computer aided techniques are heading toward the integration of design competitive products and so-called "digital manufacturing and logistics", supported by computer simulation software. All phases of product lifecycle: starting from design of a new product, through planning and control of manufacturing, assembly, internal logistics and repairs, quality control, distribution to customers and after-sale service, up to its recycling or utilization should be aided and managed by advanced packages of product lifecycle management software. Important problems for providing the efficient flow of materials in supply chain management of whole product lifecycle, using computer simulation will be described on that paper. Authors will pay attention to the processes of acquiring relevant information and correct data, necessary for virtual modeling and computer simulation of integrated manufacturing and logistics systems. The article describes possibilities of use an applications of virtual reality software for modeling and simulation the production and logistics processes in enterprise in different aspects of product lifecycle management. The authors demonstrate effective method of creating computer simulations for digital manufacturing and logistics and show modeled and programmed examples and solutions. They pay attention to development trends and show options of the applications that go beyond enterprise.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Hydrologic Process-oriented Optimization of Electrical Resistivity Tomography

NASA Astrophysics Data System (ADS)

Hinnell, A.; Bechtold, M.; Ferre, T. A.; van der Kruk, J.

2010-12-01

Electrical resistivity tomography (ERT) is commonly used in hydrologic investigations. Advances in joint and coupled hydrogeophysical inversion have enhanced the quantitative use of ERT to construct and condition hydrologic models (i.e. identify hydrologic structure and estimate hydrologic parameters). However the selection of which electrical resistivity data to collect and use is often determined by a combination of data requirements for geophysical analysis, intuition on the part of the hydrogeophysicist and logistical constraints of the laboratory or field site. One of the advantages of coupled hydrogeophysical inversion is the direct link between the hydrologic model and the individual geophysical data used to condition the model. That is, there is no requirement to collect geophysical data suitable for independent geophysical inversion. The geophysical measurements collected can be optimized for estimation of hydrologic model parameters rather than to develop a geophysical model. Using a synthetic model of drip irrigation we evaluate the value of individual resistivity measurements to describe the soil hydraulic properties and then use this information to build a data set optimized for characterizing hydrologic processes. We then compare the information content in the optimized data set with the information content in a data set optimized using a Jacobian sensitivity analysis.

Fatigue and crashes: the case of freight transport in Colombia.

PubMed

Torregroza-Vargas, Nathaly M; Bocarejo, Juan Pablo; Ramos-Bonilla, Juan P

2014-11-01

Truck drivers have been involved in a significant number of road fatalities in Colombia. To identify variables that could be associated with crashes in which truck drivers are involved, a logistic regression model was constructed. The model had as the response variable a dichotomous variable that included the presence or absence of a crash during a specific trip. As independent variables the model included information regarding a driver's work shift, with variables that could be associated with driver's fatigue. The model also included potential confounders related with road conditions. With the model, it was possible to determine the odds ratio of a crash in relation to several variables, adjusting for confounding. To collect the information about the trips included in the model, a survey among truck drivers was conducted. The results suggest strong associations between crashes (i.e., some of them statistically significant) with the number of stops made during the trip, and the average time of each stop. Survey analysis allowed us to identify the practices that contribute to generating fatigue and unhealthy conditions on the road among professional drivers. A review of national regulations confirmed the lack of legislation on this topic. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Application of a Multidimensional Extension of the Two-Parameter Logistic Latent Trait Model.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A latent trait model is described that is appropriate for use with tests that measure more than one dimension, and its application to both real and simulated test data is demonstrated. Procedures for estimating the parameters of the model are presented. The research objectives are to determine whether the two-parameter logistic model more…

Graded Response Model Based on the Logistic Positive Exponent Family of Models for Dichotomous Responses

ERIC Educational Resources Information Center

Samejima, Fumiko

2008-01-01

Samejima ("Psychometrika "65:319--335, 2000) proposed the logistic positive exponent family of models (LPEF) for dichotomous responses in the unidimensional latent space. The objective of the present paper is to propose and discuss a graded response model that is expanded from the LPEF, in the context of item response theory (IRT). This…

Chronic Condition Combinations and Health Care Expenditures and Out-of-Pocket Spending Burden Among Adults, Medical Expenditure Panel Survey, 2009 and 2011

PubMed Central

Raval, Amit D.; Sambamoorthi, Usha

2015-01-01

Introduction Little is known about how combinations of chronic conditions in adults affect total health care expenditures. Our objective was to estimate the annual average total expenditures and out-of-pocket spending burden among US adults by combinations of conditions. Methods We conducted a cross-sectional study using 2009 and 2011 data from the Medical Expenditure Panel Survey. The sample consisted of 9,296 adults aged 21 years or older with at least 2 of the following 4 highly prevalent chronic conditions: arthritis, diabetes mellitus, heart disease, and hypertension. Unadjusted and adjusted regression techniques were used to examine the association between chronic condition combinations and log-transformed total expenditures. Logistic regressions were used to analyze the relationship between chronic condition combinations and high out-of-pocket spending burden. Results Among adults with chronic conditions, adults with all 4 conditions had the highest average total expenditures ($20,016), whereas adults with diabetes/hypertension had the lowest annual total expenditures ($7,116). In adjusted models, adults with diabetes/hypertension and hypertension/arthritis had lower health care expenditures than adults with diabetes/heart disease (P < .001). In adjusted models, adults with all 4 conditions had higher expenditures compared with those with diabetes and heart disease. However, the difference was only marginally significant (P = .04). Conclusion Among adults with arthritis, diabetes, heart disease, and hypertension, total health care expenditures differed by type of chronic condition combinations. For individuals with multiple chronic conditions, such as heart disease and diabetes, new models of care management are needed to reduce the cost burden on the payers. PMID:25633487

The impact of training and working conditions on junior doctors’ intention to leave clinical practice

PubMed Central

2014-01-01

Background The shortage of physicians is an evolving problem throughout the world. In this study we aimed to identify to what extent junior doctors’ training and working conditions determine their intention to leave clinical practice after residency training. Methods A prospective cohort study was conducted in 557 junior doctors undergoing residency training in German hospitals. Self-reported specialty training conditions, working conditions and intention to leave clinical practice were measured over three time points. Scales covering training conditions were assessed by structured residency training, professional support, and dealing with lack of knowledge; working conditions were evaluated by work overload, job autonomy and social support, based on the Demand–Control–Support model. Multivariate ordinal logistic regression analyses with random intercept for longitudinal data were applied to determine the odds ratio of having a higher level of intention to leave clinical practice. Results In the models that considered training and working conditions separately to predict intention to leave clinical practice we found significant baseline effects and change effects. After modelling training and working conditions simultaneously, we found evidence that the change effect of job autonomy (OR 0.77, p = .005) was associated with intention to leave clinical practice, whereas for the training conditions, only the baseline effects of structured residency training (OR 0.74, p = .017) and dealing with lack of knowledge (OR 0.74, p = .026) predicted intention to leave clinical practice. Conclusions Junior doctors undergoing specialty training experience high workload in hospital practice and intense requirements in terms of specialty training. Our study indicates that simultaneously improving working conditions over time and establishing a high standard of specialty training conditions may prevent junior doctors from considering leaving clinical practice after residency training. PMID:24942360

Locally Dependent Linear Logistic Test Model with Person Covariates

ERIC Educational Resources Information Center

Ip, Edward H.; Smits, Dirk J. M.; De Boeck, Paul

2009-01-01

The article proposes a family of item-response models that allow the separate and independent specification of three orthogonal components: item attribute, person covariate, and local item dependence. Special interest lies in extending the linear logistic test model, which is commonly used to measure item attributes, to tests with embedded item…

A Bayesian Semiparametric Item Response Model with Dirichlet Process Priors

ERIC Educational Resources Information Center

Miyazaki, Kei; Hoshino, Takahiro

2009-01-01

In Item Response Theory (IRT), item characteristic curves (ICCs) are illustrated through logistic models or normal ogive models, and the probability that examinees give the correct answer is usually a monotonically increasing function of their ability parameters. However, since only limited patterns of shapes can be obtained from logistic models…

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Effect of insurance parity on substance abuse treatment.

PubMed

Azzone, Vanessa; Frank, Richard G; Normand, Sharon-Lise T; Burnam, M Audrey

2011-02-01

This study examined the impact of insurance parity on the use, cost, and quality of substance abuse treatment. The authors compared substance abuse treatment spending and utilization from 1999 to 2002 for continuously enrolled beneficiaries covered by Federal Employees Health Benefit (FEHB) plans, which require parity coverage of mental health and substance use disorders, with spending and utilization among beneficiaries in a matched set of health plans without parity coverage. Logistic regression models estimated the probability of any substance abuse service use. Conditional on use, linear models estimated total and out-of-pocket spending. Logistic regression models for three quality indicators for substance abuse treatment were also estimated: identification of adult enrollees with a new substance abuse diagnosis, treatment initiation, and treatment engagement. Difference-in-difference estimates were computed as (postparity - preparity) differences in outcomes in plans without parity subtracted from those in FEHB plans. There were no significant differences between FEHB and non-FEHB plans in rates of change in average utilization of substance abuse services. Conditional on service utilization, the rate of substance abuse treatment out-of-pocket spending declined significantly in the FEHB plans compared with the non-FEHB plans (mean difference=-$101.09, 95% confidence interval [CI]=-$198.06 to -$4.12), whereas changes in total plan spending per user did not differ significantly. With parity, more patients had new diagnoses of a substance use disorder (difference-in-difference risk=.10%, CI=.02% to .19%). No statistically significant differences were found for rates of initiation and engagement in substance abuse treatment. Findings suggest that for continuously enrolled populations, providing parity of substance abuse treatment coverage improved insurance protection but had little impact on utilization, costs for plans, or quality of care.

Fish introductions reveal the temperature dependence of species interactions

PubMed Central

Hein, Catherine L.; Öhlund, Gunnar; Englund, Göran

2014-01-01

A major area of current research is to understand how climate change will impact species interactions and ultimately biodiversity. A variety of environmental conditions are rapidly changing owing to climate warming, and these conditions often affect both the strength and outcome of species interactions. We used fish distributions and replicated fish introductions to investigate environmental conditions influencing the coexistence of two fishes in Swedish lakes: brown trout (Salmo trutta) and pike (Esox lucius). A logistic regression model of brown trout and pike coexistence showed that these species coexist in large lakes (more than 4.5 km2), but not in small, warm lakes (annual air temperature more than 0.9–1.5°C). We then explored how climate change will alter coexistence by substituting climate scenarios for 2091–2100 into our model. The model predicts that brown trout will be extirpated from approximately half of the lakes where they presently coexist with pike and from nearly all 9100 lakes where pike are predicted to invade. Context dependency was critical for understanding pike–brown trout interactions, and, given the widespread occurrence of context-dependent species interactions, this aspect will probably be critical for accurately predicting climate impacts on biodiversity. PMID:24307673

Assessing the Role of Environmental Conditions on Efficacy Rates of Heterorhabditis indica (Nematoda: Heterorhabditidae) for Controlling Aethina tumida (Coleoptera: Nitidulidae) in Honey Bee (Hymenoptera: Apidae) Colonies: a Citizen Science Approach.

PubMed

Hill, Elizabeth S; Smythe, Ashleigh B; Delaney, Deborah A

2016-02-01

Certain species of entomopathogenic nematodes, such as Heterorhabditis indica Poinar, Karunakar & David, have the potential to be effective controls for Aethina tumida (Murray), or small hive beetles, when applied to the soil surrounding honey bee (Apis mellifera L.) hives. Despite the efficacy of H. indica, beekeepers have struggled to use them successfully as a biocontrol. It is believed that the sensitivity of H. indica to certain environmental conditions is the primary reason for this lack of success. Although research has been conducted to explore the impact of specific environmental conditions--such as soil moisture or soil temperature-on entomopathogenic nematode infectivity, no study to date has taken a comprehensive approach that considers the impact of multiple environmental conditions simultaneously. In exploring this, a multivariate logistic regression model was used to determine what environmental conditions resulted in reductions of A. tumida populations in honey bee colonies. To obtain the sample sizes necessary to run a multivariate logistic regression, this study utilized citizen scientist beekeepers and their hives from across the mid-Atlantic region of the United States. Results suggest that soil moisture, soil temperatures, sunlight exposure, and groundcover contribute to the efficacy of H. indica in reducing A. tumida populations in A. mellifera colonies. The results of this study offer direction for future research on the environmental preferences of H. indica and can be used to educate beekeepers about methods for better utilizing H. indica as a biological control. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

ERIC Educational Resources Information Center

Peng, Chao-Ying Joanne; Zhu, Jin

2008-01-01

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

A simple approximation of moments of the quasi-equilibrium distribution of an extended stochastic theta-logistic model with non-integer powers.

PubMed

Bhowmick, Amiya Ranjan; Bandyopadhyay, Subhadip; Rana, Sourav; Bhattacharya, Sabyasachi

2016-01-01

The stochastic versions of the logistic and extended logistic growth models are applied successfully to explain many real-life population dynamics and share a central body of literature in stochastic modeling of ecological systems. To understand the randomness in the population dynamics of the underlying processes completely, it is important to have a clear idea about the quasi-equilibrium distribution and its moments. Bartlett et al. (1960) took a pioneering attempt for estimating the moments of the quasi-equilibrium distribution of the stochastic logistic model. Matis and Kiffe (1996) obtain a set of more accurate and elegant approximations for the mean, variance and skewness of the quasi-equilibrium distribution of the same model using cumulant truncation method. The method is extended for stochastic power law logistic family by the same and several other authors (Nasell, 2003; Singh and Hespanha, 2007). Cumulant truncation and some alternative methods e.g. saddle point approximation, derivative matching approach can be applied if the powers involved in the extended logistic set up are integers, although plenty of evidence is available for non-integer powers in many practical situations (Sibly et al., 2005). In this paper, we develop a set of new approximations for mean, variance and skewness of the quasi-equilibrium distribution under more general family of growth curves, which is applicable for both integer and non-integer powers. The deterministic counterpart of this family of models captures both monotonic and non-monotonic behavior of the per capita growth rate, of which theta-logistic is a special case. The approximations accurately estimate the first three order moments of the quasi-equilibrium distribution. The proposed method is illustrated with simulated data and real data from global population dynamics database. Copyright © 2015 Elsevier Inc. All rights reserved.

Research and application of genetic algorithm in path planning of logistics distribution vehicle

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.

Analysis of Jingdong Mall Logistics Distribution Model

NASA Astrophysics Data System (ADS)

Shao, Kang; Cheng, Feng

In recent years, the development of electronic commerce in our country to speed up the pace. The role of logistics has been highlighted, more and more electronic commerce enterprise are beginning to realize the importance of logistics in the success or failure of the enterprise. In this paper, the author take Jingdong Mall for example, performing a SWOT analysis of their current situation of self-built logistics system, find out the problems existing in the current Jingdong Mall logistics distribution and give appropriate recommendations.

Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions

USGS Publications Warehouse

Austin, Samuel H.; Nelms, David L.

2017-01-01

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best-performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid-Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.

Assessing a cross-border logistics policy using a performance measurement system framework: the case of Hong Kong and the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Wong, David W. C.; Choy, K. L.; Chow, Harry K. H.; Lin, Canhong

2014-06-01

For the most rapidly growing economic entity in the world, China, a new logistics operation called the indirect cross-border supply chain model has recently emerged. The primary idea of this model is to reduce logistics costs by storing goods at a bonded warehouse with low storage cost in certain Chinese regions, such as the Pearl River Delta (PRD). This research proposes a performance measurement system (PMS) framework to assess the direct and indirect cross-border supply chain models. The PMS covers four categories including cost, time, quality and flexibility in the assessment of the performance of direct and indirect models. Furthermore, a survey was conducted to investigate the logistics performance of third party logistics (3PLs) at the PRD regions, including Guangzhou, Shenzhen and Hong Kong. The significance of the proposed PMS framework allows 3PLs accurately pinpoint the weakness and strengths of it current operations policy at four major performance measurement categories. Hence, this helps 3PLs further enhance the competitiveness and operations efficiency through better resources allocation at the area of warehousing and transportation.

An order insertion scheduling model of logistics service supply chain considering capacity and time factors.

PubMed

Liu, Weihua; Yang, Yi; Wang, Shuqing; Liu, Yang

2014-01-01

Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful.

Does Multi-morbidity Mediate the Effect of Socioeconomics on Self-rated Health? Cross-country Differences

PubMed Central

Assari, Shervin; Lankarani, Maryam Moghani

2015-01-01

Background: This study explored cross-country differences in how multi-morbidity explains the effects of socioeconomic characteristics on self-rated health. Methods: The study borrowed data from the Research on Early Life and Aging Trends and Effects. Participants were 44,530 individuals (age > 65 years) who were sampled from 15 countries (i.e. United States, China, India, Russia, Costa Rica, Puerto Rico, Mexico, Argentina, Barbados, Brazil, Chile, Cuba, Uruguay, Ghana and South Africa). Multi-morbidity was measured as number of chronic medical conditions. In Model I, main effects of socioeconomic factors on self-rated health were calculated using country-specific logistic regressions. In Model II, number of chronic conditions were also added to the models to find changes in coefficients for demographic and socioeconomic factors. Results: In the United States, number of chronic medical conditions explained the effect of income on subjective health. In Puerto Rico, number of chronic medical conditions explained the effect of marital status on subjective health. In Costa Rica, Argentina, Barbados, Cuba, and Uruguay, number of chronic medical conditions explained gender disparities in subjective health. In China, Mexico, Brazil, Russia, Chile, India, Ghana and South Africa, number of chronic medical conditions did not explain the effect of demographic or socioeconomic factors on subjective health. Conclusions: Multi-morbidity explains the effect of demographic and socioeconomic factors on subjective health in some but not other countries. Further research is needed. PMID:26445632

Logistics modelling: improving resource management and public information strategies in Florida.

PubMed

Walsh, Daniel M; Van Groningen, Chuck; Craig, Brian

2011-10-01

One of the most time-sensitive and logistically-challenging emergency response operations today is to provide mass prophylaxis to every man, woman and child in a community within 48 hours of a bioterrorism attack. To meet this challenge, federal, state and local public health departments in the USA have joined forces to develop, test and execute large-scale bioterrorism response plans. This preparedness and response effort is funded through the US Centers for Disease Control and Prevention's Cities Readiness Initiative, a programme dedicated to providing oral antibiotics to an entire population within 48 hours of a weaponised inhalation anthrax attack. This paper will demonstrate how the State of Florida used a logistics modelling tool to improve its CRI mass prophylaxis plans. Special focus will be on how logistics modelling strengthened Florida's resource management policies and validated its public information strategies.

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

Exact period-four solutions of a family of n-dimensional quadratic maps via harmonic balance and Gröbner bases.

PubMed

D'Amico, María Belén; Calandrini, Guillermo L

2015-11-01

Analytical solutions of the period-four orbits exhibited by a classical family of n-dimensional quadratic maps are presented. Exact expressions are obtained by applying harmonic balance and Gröbner bases to a single-input single-output representation of the system. A detailed study of a generalized scalar quadratic map and a well-known delayed logistic model is included for illustration. In the former example, conditions for the existence of bistability phenomenon are also introduced.

Exact period-four solutions of a family of n-dimensional quadratic maps via harmonic balance and Gröbner bases

NASA Astrophysics Data System (ADS)

D'Amico, María Belén; Calandrini, Guillermo L.

2015-11-01

Analytical solutions of the period-four orbits exhibited by a classical family of n-dimensional quadratic maps are presented. Exact expressions are obtained by applying harmonic balance and Gröbner bases to a single-input single-output representation of the system. A detailed study of a generalized scalar quadratic map and a well-known delayed logistic model is included for illustration. In the former example, conditions for the existence of bistability phenomenon are also introduced.

Environmental Determinants of the Distribution of Chagas Disease Vector Triatoma dimidiata in Colombia.

PubMed

Parra-Henao, Gabriel; Quirós-Gómez, Oscar; Jaramillo-O, Nicolas; Cardona, Ángela Segura

2016-04-01

Triatoma dimidiata (Hemiptera: Reduviidae) is a secondary vector of Trypanosoma cruzi in Colombia and represents an important epidemiological risk mainly in the central and oriental regions of the country where it occupies sylvatic, peridomestic, and intradomestic ecotopes, and because of this complex distribution, its distribution and abundance could be conditioned by environmental factors. In this work, we explored the relationship between T. dimidiata distribution and environmental factors in the northwest, northeast, and central zones of Colombia and developed predictive models of infestation in the country. The associations between the presence ofT. dimidiata and environmental variables were studied using logistic regression models and ecological niche modeling for a sample of villages in Colombia. The analysis was based on the information collected in field about the presence ofT. dimidiata and the environmental data for each village extracted from remote sensing images. The presence of Triatoma dimidiata(Latreille, 1811) was found to be significantly associated with the maximum vegetation index, minimum land surface temperature (LST), and the digital elevation for the statistical model. Temperature seasonality, annual precipitation, and vegetation index were the variables that most influenced the ecological niche model ofT. dimidiata distribution. The logistic regression model showed a good fit and predicted suitable habitats in the Andean and Caribbean regions, which agrees with the known distribution of the species, but predicted suitable habitats in the Pacific and Orinoco regions proposing new areas of research. Improved models to predict suitable habitats forT. dimidiata hold promise for spatial targeting of integrated vector management. © The American Society of Tropical Medicine and Hygiene.

Environmental Determinants of the Distribution of Chagas Disease Vector Triatoma dimidiata in Colombia

PubMed Central

Parra-Henao, Gabriel; Quirós-Gómez, Oscar; Jaramillo-O, Nicolas; Cardona, Ángela Segura

2016-01-01

Triatoma dimidiata (Hemiptera: Reduviidae) is a secondary vector of Trypanosoma cruzi in Colombia and represents an important epidemiological risk mainly in the central and oriental regions of the country where it occupies sylvatic, peridomestic, and intradomestic ecotopes, and because of this complex distribution, its distribution and abundance could be conditioned by environmental factors. In this work, we explored the relationship between T. dimidiata distribution and environmental factors in the northwest, northeast, and central zones of Colombia and developed predictive models of infestation in the country. The associations between the presence of T. dimidiata and environmental variables were studied using logistic regression models and ecological niche modeling for a sample of villages in Colombia. The analysis was based on the information collected in field about the presence of T. dimidiata and the environmental data for each village extracted from remote sensing images. The presence of Triatoma dimidiata (Latreille, 1811) was found to be significantly associated with the maximum vegetation index, minimum land surface temperature (LST), and the digital elevation for the statistical model. Temperature seasonality, annual precipitation, and vegetation index were the variables that most influenced the ecological niche model of T. dimidiata distribution. The logistic regression model showed a good fit and predicted suitable habitats in the Andean and Caribbean regions, which agrees with the known distribution of the species, but predicted suitable habitats in the Pacific and Orinoco regions proposing new areas of research. Improved models to predict suitable habitats for T. dimidiata hold promise for spatial targeting of integrated vector management. PMID:26856910

Risk Factors Predicting Infectious Lactational Mastitis: Decision Tree Approach versus Logistic Regression Analysis.

PubMed

Fernández, Leónides; Mediano, Pilar; García, Ricardo; Rodríguez, Juan M; Marín, María

2016-09-01

Objectives Lactational mastitis frequently leads to a premature abandonment of breastfeeding; its development has been associated with several risk factors. This study aims to use a decision tree (DT) approach to establish the main risk factors involved in mastitis and to compare its performance for predicting this condition with a stepwise logistic regression (LR) model. Methods Data from 368 cases (breastfeeding women with mastitis) and 148 controls were collected by a questionnaire about risk factors related to medical history of mother and infant, pregnancy, delivery, postpartum, and breastfeeding practices. The performance of the DT and LR analyses was compared using the area under the receiver operating characteristic (ROC) curve. Sensitivity, specificity and accuracy of both models were calculated. Results Cracked nipples, antibiotics and antifungal drugs during breastfeeding, infant age, breast pumps, familial history of mastitis and throat infection were significant risk factors associated with mastitis in both analyses. Bottle-feeding and milk supply were related to mastitis for certain subgroups in the DT model. The areas under the ROC curves were similar for LR and DT models (0.870 and 0.835, respectively). The LR model had better classification accuracy and sensitivity than the DT model, but the last one presented better specificity at the optimal threshold of each curve. Conclusions The DT and LR models constitute useful and complementary analytical tools to assess the risk of lactational infectious mastitis. The DT approach identifies high-risk subpopulations that need specific mastitis prevention programs and, therefore, it could be used to make the most of public health resources.

Modeling of chemical inhibition from amyloid protein aggregation kinetics.

PubMed

Vázquez, José Antonio

2014-02-27

The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. In this manuscript, experimental data of insulin, Aβ42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH = 7.4/T = 37°C) and taiwaniaflavone for Aβ42 were the compounds studied that shown the greatest inhibition capacity. An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

Synergistic effect of pulsed electric fields and CocoanOX 12% on the inactivation kinetics of Bacillus cereus in a mixed beverage of liquid whole egg and skim milk.

PubMed

Pina-Pérez, M C; Silva-Angulo, A B; Rodrigo, D; Martínez-López, A

2009-04-15

With a view to extending the shelf-life and enhancing the safety of liquid whole egg/skim milk (LWE-SM) mixed beverages, a study was conducted with Bacillus cereus vegetative cells inoculated in skim milk (SM) and LWE-SM beverages, with or without antimicrobial cocoa powder. The beverages were treated with Pulsed Electric Field (PEF) technology and then stored at 5 degrees C for 15 days. The kinetic results were modeled with the Bigelow model, Weibull distribution function, modified Gompertz equation, and Log-logistic models. Maximum inactivation registered a reduction of around 3 log cycles at 40 kV/cm, 360 micros, 20 degrees C in both the SM and LWE-SM beverages. By contrast, in the beverages supplemented with the aforementioned antimicrobial compound, higher inactivation levels were obtained under the same treatment conditions, reaching a 3.30 log(10) cycle reduction. The model affording the best fit for all four beverages was the four-parameter Log-logistic model. After 15 days of storage, the antimicrobial compound lowered Bacillus cereus survival rates in the samples supplemented with CocoanOX 12% by a 4 log cycle reduction, as compared to the untreated samples without CocoanOX 12%. This could indicate that the PEF-antimicrobial combination has a synergistic effect on the bacterial cells under study, increasing their sensitivity to subsequent refrigerated storage.

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988

Modelling the growth of plants with a uniform growth logistics.

PubMed

Kilian, H G; Bartkowiak, D; Kazda, M; Kaufmann, D

2014-05-21

The increment model has previously been used to describe the growth of plants in general. Here, we examine how the same logistics enables the development of different superstructures. Data from the literature are analyzed with the increment model. Increments are growth-invariant molecular clusters, treated as heuristic particles. This approach formulates the law of mass action for multi-component systems, describing the general properties of superstructures which are optimized via relaxation processes. The daily growth patterns of hypocotyls can be reproduced implying predetermined growth invariant model parameters. In various species, the coordinated formation and death of fine roots are modeled successfully. Their biphasic annual growth follows distinct morphological programs but both use the same logistics. In tropical forests, distributions of the diameter in breast height of trees of different species adhere to the same pattern. Beyond structural fluctuations, competition and cooperation within and between the species may drive optimization. All superstructures of plants examined so far could be reproduced with our approach. With genetically encoded growth-invariant model parameters (interaction with the environment included) perfect morphological development runs embedded in the uniform logistics of the increment model. Copyright © 2014 Elsevier Ltd. All rights reserved.

To Use or Not to Use--(The One- or Three-Parameter Logistic Model) That Is the Question.

ERIC Educational Resources Information Center

Reckase, Mark D.

Definition of the issues to the use of latent trait models, specifically one- and three-parameter logistic models, in conjunction with multi-level achievement batteries, forms the basis of this paper. Research results related to these issues are also documented in an attempt to provide a rational basis for model selection. The application of the…

Testing for gene-environment interaction under exposure misspecification.

PubMed

Sun, Ryan; Carroll, Raymond J; Christiani, David C; Lin, Xihong

2017-11-09

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties. © 2017, The International Biometric Society.

Examination of environmentally friendly "green" logistics behavior of managers in the pharmaceutical sector using the Theory of Planned Behavior.

PubMed

Arslan, Miray; Şar, Sevgi

2017-12-11

Logistics activities play a prominent role in enabling manufacturers, distribution channels, and pharmacies to work in harmony. Nowadays these activities have become increasingly striking in the pharmaceutical industry and seen as a development area for this sector. Additionally, green practices are beginning to be more attracting particularly in decreasing costs and increasing image of pharmaceutical companies. The main objective of this study was modeling green logistics (GL) behavior of the managers in the pharmaceutical sector in the theory of planned behavior (TPB) frame via structural equation modeling (SEM). A measurement tool was developed according to TPB. Exploratory factor analysis was conducted to determine subfactors of GL behavior. In the second step, confirmatory factor analysis (CFA) was conducted for confirming whether there is a relationship between the observed variables and their underlying latent constructs. Finally, structural equation model was conducted to specify the relationships between latent variables. In the proposed green logistics behavior (GLB) model, the positive effect of environmental attitude towards GL, perceived behavioral control related GL, and subjective norm about GL on intention towards GL were found statistically significant. Nevertheless, the effect of attitude towards costs of GL on intention towards GL was not found statistically significant. Intention towards GL has been found to have a positive statistically significant effect on the GL behavior. Based on the results of this study, it is possible to say that TPB is an appropriate theory for modeling green logistics behavior of managers. This model can be seen as a guide to the companies in the pharmaceutical sector to participate in green logistics. Copyright © 2017 Elsevier Inc. All rights reserved.

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Multiple logistic regression model of signalling practices of drivers on urban highways

NASA Astrophysics Data System (ADS)

Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana

2015-05-01

Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

Rotenone persistence model for montane streams

USGS Publications Warehouse

Brown, Peter J.; Zale, Alexander V.

2012-01-01

The efficient and effective use of rotenone is hindered by its unknown persistence in streams. Environmental conditions degrade rotenone, but current label instructions suggest fortifying the chemical along a stream based on linear distance or travel time rather than environmental conditions. Our objective was to develop models that use measurements of environmental conditions to predict rotenone persistence in streams. Detailed measurements of ultraviolet radiation, water temperature, dissolved oxygen, total dissolved solids (TDS), conductivity, pH, oxidation–reduction potential (ORP), substrate composition, amount of organic matter, channel slope, and travel time were made along stream segments located between rotenone treatment stations and cages containing bioassay fish in six streams. The amount of fine organic matter, biofilm, sand, gravel, cobble, rubble, small boulders, slope, pH, TDS, ORP, light reaching the stream, energy dissipated, discharge, and cumulative travel time were each significantly correlated with fish death. By using logistic regression, measurements of environmental conditions were paired with the responses of bioassay fish to develop a model that predicted the persistence of rotenone toxicity in streams. This model was validated with data from two additional stream treatment reaches. Rotenone persistence was predicted by a model that used travel time, rubble, and ORP. When this model predicts a probability of less than 0.95, those who apply rotenone can expect incomplete eradication and should plan on fortifying rotenone concentrations. The significance of travel time has been previously identified and is currently used to predict rotenone persistence. However, rubble substrate, which may be associated with the degradation of rotenone by adsorption and volatilization in turbulent environments, was not previously considered.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Research on the influencing factors of reverse logistics carbon footprint under sustainable development.

PubMed

Sun, Qiang

2017-10-01

With the concerns of ecological and circular economy along with sustainable development, reverse logistics has attracted the attention of enterprise. How to achieve sustainable development of reverse logistics has important practical significance of enhancing low carbon competitiveness. In this paper, the system boundary of reverse logistics carbon footprint is presented. Following the measurement of reverse logistics carbon footprint and reverse logistics carbon capacity is provided. The influencing factors of reverse logistics carbon footprint are classified into five parts such as intensity of reverse logistics, energy structure, energy efficiency, reverse logistics output, and product remanufacturing rate. The quantitative research methodology using ADF test, Johansen co-integration test, and impulse response is utilized to interpret the relationship between reverse logistics carbon footprint and the influencing factors more accurately. This research finds that energy efficiency, energy structure, and product remanufacturing rate are more capable of inhibiting reverse logistics carbon footprint. The statistical approaches will help practitioners in this field to structure their reverse logistics activities and also help academics in developing better decision models to reduce reverse logistics carbon footprint.

Are Child Passengers Bringing Up the Rear? Evidence For Differential Improvements in Injury Risk Between Drivers and their Child Passengers

PubMed Central

Winston, Flaura K; Xie, Dawei; Durbin, Dennis R; Elliott, Michael R

2007-01-01

Since nearly half of children fatally injured in automobile crashes were restrained, optimizing occupant protection systems for children is essential to reducing morbidity and mortality. Data from the Partners for Child Passenger Safety study were used to compare the differential injury risk between drivers and their child passengers in the same crash, with a focus on vehicle model year. A matched cohort design and conditional logistic regression model were used in the analyses. Overall, injury risk for drivers was higher than for children, but the risk difference was largest for the oldest model year vehicles, particularly for children aged 4–8 in seat belts. While drivers experienced significant benefits in safety with increasing model years, children restrained by safety belts alone derived less safety benefit from newer vehicles. PMID:18184488

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

[Calculating Pearson residual in logistic regressions: a comparison between SPSS and SAS].

PubMed

Xu, Hao; Zhang, Tao; Li, Xiao-song; Liu, Yuan-yuan

2015-01-01

To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

PubMed

Karp, Igor; Sylvestre, Marie-Pierre; Abrahamowicz, Michal; Leffondré, Karen; Siemiatycki, Jack

2016-11-01

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case-control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a 'non-nested' case-control study. We used data from a case-control study conducted in Montreal, Canada in 1996-2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and 'shrinkage.' The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting ('shrinkage' factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method's risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Factors associated with preventable infant death: a multiple logistic regression

PubMed Central

Vidal e Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

2018-01-01

ABSTRACT OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight. PMID:29723389

On the long time behavior of non-autonomous Lotka-Volterra models with diffusion via the sub-supertrajectory method

NASA Astrophysics Data System (ADS)

Langa, José A.; Rodríguez-Bernal, Aníbal; Suárez, Antonio

In this paper we study in detail the geometrical structure of global pullback and forwards attractors associated to non-autonomous Lotka-Volterra systems in all the three cases of competition, symbiosis or prey-predator. In particular, under some conditions on the parameters, we prove the existence of a unique nondegenerate global solution for these models, which attracts any other complete bounded trajectory. Thus, we generalize the existence of a unique strictly positive stable (stationary) solution from the autonomous case and we extend to Lotka-Volterra systems the result for scalar logistic equations. To this end we present the sub-supertrajectory tool as a generalization of the now classical sub-supersolution method. In particular, we also conclude pullback and forwards permanence for the above models.

Factors related to treatment refusal in Taiwanese cancer patients.

PubMed

Chiang, Ting-Yu; Wang, Chao-Hui; Lin, Yu-Fen; Chou, Shu-Lan; Wang, Ching-Ting; Juang, Hsiao-Ting; Lin, Yung-Chang; Lin, Mei-Hsiang

2015-01-01

Incidence and mortality rates for cancer have increased dramatically in the recent 30 years in Taiwan. However, not all patients receive treatment. Treatment refusal might impair patient survival and life quality. In order to improve this situation, we proposed this study to evaluate factors that are related to refusal of treatment in cancer patients via a cancer case manager system. This study analysed data from a case management system during the period from 2010 to 2012 at a medical center in Northern Taiwan. We enrolled a total of 14,974 patients who were diagnosed with cancer. Using the PRECEDE Model as a framework, we conducted logistic regression analysis to identify independent variables that are significantly associated with refusal of therapy in cancer patients. A multivariate logistic regression model was also applied to estimate adjusted the odds ratios (ORs) with 95% confidence intervals (95%CI). A total of 253 patients (1.69%) refused treatment. The multivariate logistic regression result showed that the high risk factors for refusal of treatment in cancer patient included: concerns about adverse effects (p<0.001), poor performance(p<0.001), changes in medical condition (p<0.001), timing of case manager contact (p=.026), the methods by which case manager contact patients (p<0.001) and the frequency that case managers contact patients (≥10times) (p=0.016). Cancer patients who refuse treatment have poor survival. The present study provides evidence of factors that are related to refusal of therapy and might be helpful for further application and improvement of cancer care.

Performance of the likelihood ratio difference (G2 Diff) test for detecting unidimensionality in applications of the multidimensional Rasch model.

PubMed

Harrell-Williams, Leigh; Wolfe, Edward W

2014-01-01

Previous research has investigated the influence of sample size, model misspecification, test length, ability distribution offset, and generating model on the likelihood ratio difference test in applications of item response models. This study extended that research to the evaluation of dimensionality using the multidimensional random coefficients multinomial logit model (MRCMLM). Logistic regression analysis of simulated data reveal that sample size and test length have a large effect on the capacity of the LR difference test to correctly identify unidimensionality, with shorter tests and smaller sample sizes leading to smaller Type I error rates. Higher levels of simulated misfit resulted in fewer incorrect decisions than data with no or little misfit. However, Type I error rates indicate that the likelihood ratio difference test is not suitable under any of the simulated conditions for evaluating dimensionality in applications of the MRCMLM.

Evaluation of the Logistic Model for GAC Performance in Water Treatment

EPA Science Inventory

Full-scale field measurement and rapid small-scale column test data from the Greater Cincinnati (Ohio) Water Works (GCWW) were used to calibrate and investigate the application of the logistic model for simulating breakthrough of total organic carbon (TOC) in granular activated c...

A multicriteria decision making approach based on fuzzy theory and credibility mechanism for logistics center location selection.

PubMed

Wang, Bowen; Xiong, Haitao; Jiang, Chengrui

2014-01-01

As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center.

A Multicriteria Decision Making Approach Based on Fuzzy Theory and Credibility Mechanism for Logistics Center Location Selection

PubMed Central

Wang, Bowen; Jiang, Chengrui

2014-01-01

As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center. PMID:25215319

A Collection of Technical Papers

NASA Technical Reports Server (NTRS)

1995-01-01

Papers presented at the 6th Space Logistics Symposium covered such areas as: The International Space Station; The Hubble Space Telescope; Launch site computer simulation; Integrated logistics support; The Baikonur Cosmodrome; Probabalistic tools for high confidence repair; A simple space station rescue vehicle; Integrated Traffic Model for the International Space Station; Packaging the maintenance shop; Leading edge software support; Storage information management system; Consolidated maintenance inventory logistics planning; Operation concepts for a single stage to orbit vehicle; Mission architecture for human lunar exploration; Logistics of a lunar based solar power satellite scenario; Just in time in space; NASA acquisitions/logistics; Effective transition management; Shuttle logistics; and Revitalized space operations through total quality control management.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Forest biomass supply logistics for a power plant using the discrete-event simulation approach

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

Mobini, Mahdi; Sowlati, T.; Sokhansanj, Shahabaddine

This study investigates the logistics of supplying forest biomass to a potential power plant. Due to the complexities in such a supply logistics system, a simulation model based on the framework of Integrated Biomass Supply Analysis and Logistics (IBSAL) is developed in this study to evaluate the cost of delivered forest biomass, the equilibrium moisture content, and carbon emissions from the logistics operations. The model is applied to a proposed case of 300 MW power plant in Quesnel, BC, Canada. The results show that the biomass demand of the power plant would not be met every year. The weighted averagemore » cost of delivered biomass to the gate of the power plant is about C$ 90 per dry tonne. Estimates of equilibrium moisture content of delivered biomass and CO2 emissions resulted from the processes are also provided.« less

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack-From a Green Operation Perspective.

PubMed

Xia, Yangkun; Fu, Zhuo; Tsai, Sang-Bing; Wang, Jiangtao

2018-05-10

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models

PubMed Central

Lin, Wei-Chun; Lin, Shu-Yuan; Wu, Li-Fu; Guo, Shih-Sian; Huang, Hsiang-Jui; Chao, Pei-Ju

2015-01-01

To develop the logistic and the probit models to analyse electromyographic (EMG) equivalent uniform voltage- (EUV-) response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG) signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS) 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP) models were established for the VAS score and EMG absolute voltage-time histograms (AVTH). TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27%) developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV), γ 50 = 0.84 (CI: 0.78–0.90) and TV50 = 155.6 mV (CI: 138.9–172.4 mV), m = 0.54 (CI: 0.49–0.59) for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow. PMID:26380281

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Organizational Learning, Strategic Flexibility and Business Model Innovation: An Empirical Research Based on Logistics Enterprises

NASA Astrophysics Data System (ADS)

Bao, Yaodong; Cheng, Lin; Zhang, Jian

Using the data of 237 Jiangsu logistics firms, this paper empirically studies the relationship among organizational learning capability, business model innovation, strategic flexibility. The results show as follows; organizational learning capability has positive impacts on business model innovation performance; strategic flexibility plays mediating roles on the relationship between organizational learning capability and business model innovation; interaction among strategic flexibility, explorative learning and exploitative learning play significant roles in radical business model innovation and incremental business model innovation.

Asymptotic Properties of Induced Maximum Likelihood Estimates of Nonlinear Models for Item Response Variables: The Finite-Generic-Item-Pool Case.

ERIC Educational Resources Information Center

Jones, Douglas H.

The progress of modern mental test theory depends very much on the techniques of maximum likelihood estimation, and many popular applications make use of likelihoods induced by logistic item response models. While, in reality, item responses are nonreplicate within a single examinee and the logistic models are only ideal, practitioners make…

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.

An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy.

PubMed

Day, Theodore Eugene; Ravi, Nathan; Xian, Hong; Brugh, Ann

2013-01-01

Agent-based models are valuable for examining systems where large numbers of discrete individuals interact with each other, or with some environment. Diabetic Veterans seeking eye care at a Veterans Administration hospital represent one such cohort. The objective of this study was to develop an agent-based template to be used as a model for a patient with diabetic retinopathy (DR). This template may be replicated arbitrarily many times in order to generate a large cohort which is representative of a real-world population, upon which in-silico experimentation may be conducted. Agent-based template development was performed in java-based computer simulation suite AnyLogic Professional 6.6. The model was informed by medical data abstracted from 535 patient records representing a retrospective cohort of current patients of the VA St. Louis Healthcare System Eye clinic. Logistic regression was performed to determine the predictors associated with advancing stages of DR. Predicted probabilities obtained from logistic regression were used to generate the stage of DR in the simulated cohort. The simulated cohort of DR patients exhibited no significant deviation from the test population of real-world patients in proportion of stage of DR, duration of diabetes mellitus (DM), or the other abstracted predictors. Simulated patients after 10 years were significantly more likely to exhibit proliferative DR (P<0.001). Agent-based modeling is an emerging platform, capable of simulating large cohorts of individuals based on manageable data abstraction efforts. The modeling method described may be useful in simulating many different conditions where course of disease is described in categorical stages.

Job stress models, depressive disorders and work performance of engineers in microelectronics industry.

PubMed

Chen, Sung-Wei; Wang, Po-Chuan; Hsin, Ping-Lung; Oates, Anthony; Sun, I-Wen; Liu, Shen-Ing

2011-01-01

Microelectronic engineers are considered valuable human capital contributing significantly toward economic development, but they may encounter stressful work conditions in the context of a globalized industry. The study aims at identifying risk factors of depressive disorders primarily based on job stress models, the Demand-Control-Support and Effort-Reward Imbalance models, and at evaluating whether depressive disorders impair work performance in microelectronics engineers in Taiwan. The case-control study was conducted among 678 microelectronics engineers, 452 controls and 226 cases with depressive disorders which were defined by a score 17 or more on the Beck Depression Inventory and a psychiatrist's diagnosis. The self-administered questionnaires included the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, demography, psychosocial factors, health behaviors and work performance. Hierarchical logistic regression was applied to identify risk factors of depressive disorders. Multivariate linear regressions were used to determine factors affecting work performance. By hierarchical logistic regression, risk factors of depressive disorders are high demands, low work social support, high effort/reward ratio and low frequency of physical exercise. Combining the two job stress models may have better predictive power for depressive disorders than adopting either model alone. Three multivariate linear regressions provide similar results indicating that depressive disorders are associated with impaired work performance in terms of absence, role limitation and social functioning limitation. The results may provide insight into the applicability of job stress models in a globalized high-tech industry considerably focused in non-Western countries, and the design of workplace preventive strategies for depressive disorders in Asian electronics engineering population.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

PubMed

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

PubMed Central

GUZZO, A.S.; MEGGIOLARO, A.; MANNOCCI, A.; TECCA, M.; SALOMONE, I.

2015-01-01

Summary Introduction. "Umberto I" Teaching Hospital adopted 'Conley scale' as internal procedure for fall risk assessment, with the aim of strengthening surveillance and improving prevention and management of impatient falls. Materials and methods. Case-control study was performed. Fall events from 1st March 2012 to 30th September 2013 were considered. Cases have been matched for gender, department and period of hospitalization with two or three controls when it is possible. A table including intrinsic and extrinsic 'fall risk' factors, not foreseen by Conley Scale, and setted up after a literature overview was built. Univariate analysis and conditional logistic regression model have been performed. Results. 50 cases and 102 controls were included. Adverse event 'fall' were associated with filled Conley scale at the admission to care unit (OR = 4.92, 95%CI = 2.34-10.37). Univariate analysis identified intrinsic factors increasing risk of falls: dizziness (OR = 3.22; 95%CI = 1.34-7.75), psychomotor agitation (OR = 2.61; 95%CI = 1.06-6.43); and use of means of restraint (OR = 5.05 95%CI = 1.77-14.43). Conditional logistic regression model revealed a significant association with the following variables: use of instruments of restraint (HR = 5.54, 95%CI = 1.2- 23.80), dizziness (OR = 3.97, 95%CI = 1.22-12.89). Discussion. Conley Scale must be filled at the access of patient to care unit. There were no significant differences between cases and controls with regard to risk factors provided by Conley, except for the use of means of restraint. Empowerment strategies for Conley compilation are needed. PMID:26789993

Preoperative chemotherapy for operable breast cancer is associated with better compliance with adjuvant therapy in matched stage II and IIIA patients.

PubMed

Komenaka, Ian K; Hsu, Chiu-Hsieh; Martinez, Maria Elena; Bouton, Marcia E; Low, Boo Ghee; Salganick, Jason A; Nodora, Jesse; Hibbard, Michael L; Jha, Chandra

2011-01-01

Preoperative chemotherapy (PC) for operable breast cancer has shown significant benefits in prospective trials. Many patients are treated in the community setting and some may question the applicability of PC outside the university setting. Retrospective review was performed of stage II and IIIA breast cancer patients treated from January 2002 to July 2009. Fifty-three of 57 patients who underwent PC were matched based on age, tumor size, and hormone receptor status with 53 patients who did not undergo PC. Differences in patient compliance with physician recommendations for all types of adjuvant therapy were evaluated. Crude odds ratios and adjusted odds ratios derived from conditional logistic regression models were calculated. There were 106 patients included. Patient compliance with chemotherapy was better in the PC group than in the adjuvant chemotherapy (AC) group (100% versus 70%; p = .0001). Similarly, more patients in the PC group completed radiation therapy (96% versus 65%; p = .0003) and initiated hormonal therapy (100% versus 62%; p = .0001). Conditional logistic regression revealed that higher pathologic stage and current cigarette smoking were associated with poorer compliance with chemotherapy. For radiation therapy, the univariate model revealed that compliance with chemotherapy and being employed were associated with completion of radiation, whereas current cigarette smoking and larger pathologic size were associated with poorer compliance with radiation. For hormonal therapy, current cigarette smokers were more likely to be noncompliant with initiation of hormonal therapy. PC for operable breast cancer can improve patient compliance with chemotherapy. Current cigarette smokers were more likely to be noncompliant with all types of adjuvant therapy.

Characterising invasive non-native Rhododendron ponticum spectra signatures with spectroradiometry in the laboratory and field: Potential for remote mapping

NASA Astrophysics Data System (ADS)

Taylor, Sarah L.; Hill, Ross A.; Edwards, Colin

2013-07-01

Compared with traditional ground surveys, remote sensing has the potential to map the spatial extent of non-native invasive species rapidly and reliably. This paper assesses the potential of spectroradiometry to distinguish and characterise the status of invasive non-native rhododendron (Rhododendron ponticum). Absolute reflectance of target plant material was measured with an ASD Fieldspec Pro System under standardised laboratory conditions and in the field to characterise spectral signatures in the winter, during leaf-off conditions for woodland overstory, and in the summer when mature rhododendrons are flowering. A logistic regression model of absolute reflectance at key wavelengths (490, 550, 610, 1040 and 1490 nm) was used to determine the success of discriminating rhododendron from three other shrubby species likely to be encountered in woodlands during the winter. The logistic regression model was highly significant (p < 0.001), with 93.5% of 246 leaf sets correctly identified as rhododendron or non-rhododendron (i.e. cherry laurel (Prunus laurocerasus), holly (Ilex aquifolium), and beech (Fagus sylvatica)). Rescaling the data to emulate the spectral resolution of airborne and satellite acquired data decreased the total success rate of correctly identifying rhododendron by only 0.4%; although this error rate will likely increase for airborne or satellite data as a result of atmospheric attenuation and reduced spatial resolution. This demonstrates the potential to map bush presence using hyperspectral data and indicates the optimum spectral wavelengths required. Such information is critical to the development of successful strategic management plans to eradicate rhododendron (and the associated Phytophthora ramorum pathogen) effectively from a site.

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Self-report of gingival problems and periodontitis in indigenous and non-indigenous populations in Chiapas, Mexico.

PubMed

García-Pérez, Álvaro; Borges-Yáñez, Socorro Aída; Jiménez-Corona, Aida; Jiménez-Corona, María Eugenia; Ponce-de-León, Samuel

2016-04-01

To estimate the prevalence of self-reported gingival and periodontal conditions and their association with smoking, oral hygiene, indigenous origin, diabetes and location (urban or rural) in indigenous and non-indigenous adults in Chiapas, Mexico. A cross-sectional study of 1,749 persons, ≥20 years of age, living in four rural and four urban marginal localities in Comitán (Chiapas, México). The variables investigated were: age; sex; indigenous origin; oral hygiene; halitosis; chewing ability; gingival conditions; periodontitis; smoking; alcoholism; diabetes; and location. Bivariate analysis and a logistic regression model were used to identify the association of periodontitis with the independent variables. In total, 762 (43.6%) indigenous and 987 (56.4%) non-indigenous persons were interviewed. Their mean age was 41 ± 14 years, 66.7% were women and 43.8% lived in rural locations. Gingival problems were reported by 68.5% and periodontitis by 8.7%. In total, 17.9% had used dental services during the previous year, 28.7% wore a removable partial or a complete dental prosthesis, 63.7% had lost at least one tooth, the prevalence of diabetes was 9.2% and the prevalence of smoking was 12.2%. The logistic regression model showed that age, diabetes and the interaction between rural location and indigenous origin were associated with the presence of periodontitis. Indigenous people living in rural areas are more likely to have periodontitis. It is necessary to promote oral health practices in indigenous and marginalised populations with a focus on community-oriented primary care. © 2016 FDI World Dental Federation.

Tea Consumption and Health-Related Quality of Life in Older Adults.

PubMed

Pan, C-W; Ma, Q; Sun, H-P; Xu, Y; Luo, N; Wang, P

2017-01-01

Although tea consumption has been reported to have various health benefits in humans, its association with health-related quality of life (HRQOL) has not been investigated directly. We aimed to examine the relationship between tea consumption and HRQOL among older Chinese adults. We analyzed community-based cross-sectional data of 5,557 older Chinese individuals aged 60 years or older who participated in the Weitang Geriatric Diseases study. Information on tea consumption and HRQOL assessed by the European Quality of Life-5 dimensions (EQ-5D) were collected by questionnaires. We estimated the relationship of tea consumption and the EQ-5D index score using linear regression models and the association between tea consumption and self-reported EQ-5D health problems using logistic regression models. The EQ-5D index score was higher for habitual tea drinkers than their counterparts. In multivariate linear analyses controlling for socio-demographic conditions, health conditions, and lifestyle habits, the differences in ED-5D index score between individuals with and without tea drinking habits was 0.012 (95% confidence interval, 0.006-0.017). In multivariate logistic analyses, habitual tea drinking was inversely associated with reporting of problems in EQ-5D dimensions mobility (odds ration [OR], 0.44; 95% CI: 0.23-0.84); pain/discomfort (OR, 0.74; 95% CI: 0.61-0.90); and anxiety/depression (OR, 0.60; 95% CI: 0.38-0.97). These associations were more evident for black or oolong tea than green tea. Habitual tea consumption was associated with better HRQOL in older adults.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

An Order Insertion Scheduling Model of Logistics Service Supply Chain Considering Capacity and Time Factors

PubMed Central

Yang, Yi; Wang, Shuqing; Liu, Yang

2014-01-01

Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful. PMID:25276851

An Evaluation of Hierarchical Bayes Estimation for the Two- Parameter Logistic Model.

ERIC Educational Resources Information Center

Kim, Seock-Ho

Hierarchical Bayes procedures for the two-parameter logistic item response model were compared for estimating item parameters. Simulated data sets were analyzed using two different Bayes estimation procedures, the two-stage hierarchical Bayes estimation (HB2) and the marginal Bayesian with known hyperparameters (MB), and marginal maximum…

Applying the Principles of Specific Objectivity and of Generalizability to the Measurement of Change.

ERIC Educational Resources Information Center

Fischer, Gerhard H.

1987-01-01

A natural parameterization and formalization of the problem of measuring change in dichotomous data is developed. Mathematically-exact definitions of specific objectivity are presented, and the basic structures of the linear logistic test model and the linear logistic model with relaxed assumptions are clarified. (SLD)

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Exploring factors related to metastasis free survival in breast cancer patients using Bayesian cure models.

PubMed

Jafari-Koshki, Tohid; Mansourian, Marjan; Mokarian, Fariborz

2014-01-01

Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.

Logistics engineering education from the point of view environment

NASA Astrophysics Data System (ADS)

Bányai, Ágota

2010-05-01

A new field of MSc programme offered by the Faculty of Mechanical Engineering and Informatics of the University of Miskolc is represented by the programme in logistics engineering. The Faculty has always laid great emphasis on assigning processes connected with environment protection and globalisation issues the appropriate weight in its programmes. This is based on the fact that the Faculty has initiated and been involved in a great number of research and development projects with a substantial emphasis on the fundamental principles of sustainable development. The objective of the programme of logistics engineering is to train engineers who, in possession of the science, engineering, economic, informatics and industrial, transportation technological knowledge related to the professional field of logistics, are able to analyse, design, organise, and control logistics processes and systems (freight transportation, materials handling, storage, commissioning, loading, purchasing, distribution and waste management) as well as to design and develop machinery and equipment as the elements of logistic systems and also to be involved in their manufacture and quality control and are able to control their operation. The programme prepares its students for performing the logistics management tasks in a company, for creative participation in solving research and development problems in logistics and for pursuing logistics studies in doctoral programmes. There are several laboratories available for practice-oriented training. The 'Integrated Logistics Laboratory' consists of various fixed and mobile, real industrial, i.e. not model-level equipment, the integration of which in one system facilitates not only the presentation, examination and development of the individual self-standing facilities, but the study of their interaction as well in terms of mechatronics, engineering, control engineering, informatics, identification technology and logistics. The state-of-the-art, reliable, automated mechatronics-material flow system with its single control engineering system provides the academic staff with up-to-date research facilities, and enables the students to study sophisticated equipment and systems that could also operate under industrial conditions, thus offering knowledge that can be efficiently utilised in the industry after graduation. The laboratory measurements of the programme in logistics engineering are performed in this laboratory, and they are supplemented by the theoretical and practical measurements in the ‘Robotic Technology Assembly Laboratory', the ‘Power Electronics Laboratory', the ‘Mechatronics Laboratory', the ‘CAD/CAM Laboratory' and the ‘Acoustics and Product Laboratory'. The bodies of knowledge connected with environment protection and sustainable development can be grouped around three large topic areas. In environmental economics the objective is to present the corporate-organisational aspects of environmental management. Putting environmental management in the focal point, the objective of the programme is to impart knowledge that can be utilised in practice which can be used to shift the relation between the organisation and its environment in the direction of sustainability. The tools include environmental controlling, environmental marketing and various solutions of environmental performance evaluation. The second large topic area is globalization and its logistic aspects. In the field of global logistics the following knowledge carries special weight: logistic challenges in a globalised world; the concept of global logistics, its conditions and effects; delayed manufacture, assembly, packaging; the economic investigation of delayed assembly; globalised purchase and distribution in logistics; the logistic features of the globalised production supply/distribution chain; meta-logistics systems; logistics-related EU harmonisation issues; the effect of e-commerce on the global logistic system; logistic centres, connecting virtual logistic companies in a network; the environmental harmonisation of international transportation. The third large area is recycling logistics. Here the bodies of knowledge are as follows: the concept of developing a ‘closed-loop economy'; stages in the progress of products after discarding, connections between the uses of waste collection, processing, selection, deposition or reuse processes; features of European recommendations (e.g. EMAS), harmonisation of national practices and global solutions; presenting the logistics part-processes of recycling; presenting process organisation procedures for the foundation of designing one-route, multi-route, replacement container waste collecting and distributing part systems; recycling strategies with consideration of logistically serving the separation and storage of waste to be deposited, the technological processing systems of recyclable materials; presenting dismantling and product and material identification technologies, presenting logistics part-tasks, analysis of technical solutions; IT solutions for identifying products and their elements to be distributed and withdrawn from distribution after use (e.g. RFID systems) and monitoring their material flow; methodology of using efficiency analyses and incentive systems in the decision making processes of recycling processes, risk analysis for evaluating typical part processes; the methodology of recycling-oriented product design for specific product groups. Graduates of the Master programmes are able to use and utilise the knowledge obtained in practice, use problem-solving techniques; process the information, new problems and new phenomena arising in the border areas of the professional experience gained the discipline; formulate substantial criticism and opinions as far as possible, make decisions and draw conclusions; comprehending and solving the problems arising, suggesting original ideas; plan and perform tasks independently at a high professional standard; improve themselves, develop their knowledge to higher levels; view the management of technical/engineering - economic - human resources in a complex way; design complex systems in a global way based on a system-oriented and process-oriented way of thinking; use integrated knowledge from the professional fields of transport, mobile machinery, process theory, industrial production processes, electronics and informatics; combine the part processes of logistics systems and the part units performing their physical realisation (materials handling equipment, sensors, actuators, control systems, and database systems, etc.); perform state evaluations depending on their specialisation, use them to elaborate evaluations and recommendations, develop complex logistic systems, design, organise and control them at the highest level. This work was implemented with support by the European Union and co-funding of the European Social Fund.

Empirical research on coordination evaluation and sustainable development mechanism of regional logistics and new-type urbanization: a panel data analysis from 2000 to 2015 for Liaoning Province in China.

PubMed

Sun, Qiang

2017-06-01

As the largest developing country in the world, China has witnessed fast-paced urbanization over the past three decades with rapid economic growth. In fact, urbanization has been not only shown to promote economic growth and improve the livelihood of people but also can increase demands of regional logistics. Therefore, a better understanding of the relationship between urbanization and regional logistics is important for China's future sustainable development. The development of urban residential area and heterogeneous, modern society as well regional logistics are running two abreast. The regional logistics can promote the development of new-type urbanization jointly by promoting industrial concentration and logistics demand, enhancing the residents' quality of life and improving the infrastructure and logistics technology. In this paper, the index system and evaluation model for evaluating the development of regional logistics and the new-type urbanization are constructed. Further, the econometric analysis is utilized such as correlation analysis, co-integration test, and error correction model to explore relationships of the new-type urbanization development and regional logistics development in Liaoning Province. The results showed that there was a long-term stable equilibrium relationship between the new-type urbanization and regional logistics. The findings have important implications for Chinese policymakers that on the path towards a sustainable urbanization and regional reverse, this must be taken into consideration. The paper concludes providing some strategies that might be helpful to the policymakers in formulating development policies for sustainable urbanization.

Physician Satisfaction in Treating Medically Unexplained Symptoms.

PubMed

Brauer, Simon G; Yoon, John D; Curlin, Farr A

2017-05-01

To determine whether treating conditions having medically unexplained symptoms is associated with lower physician satisfaction and higher ascribed patient responsibility, and to determine whether higher ascribed patient responsibility is associated with lower physician satisfaction in treating a given condition. We surveyed a nationally representative sample of 1504 US primary care physicians. Respondents were asked how responsible patients are for two conditions with more-developed medical explanations (depression and anxiety) and two conditions with less-developed medical explanations (chronic back pain and fibromyalgia), and how much satisfaction they experienced in treating each condition. We used Wald tests to compare mean satisfaction and ascribed patient responsibility between medically explained conditions and medically unexplained conditions. We conducted single-level and multilevel ordinal logistic models to test the relation between ascribed patient responsibility and physician satisfaction. Treating medically unexplained conditions elicited less satisfaction than treating medically explained conditions (Wald P < 0.001). Physicians attribute significantly more patient responsibility to the former (Wald P < 0.005), although the magnitude of the difference is small. Across all four conditions, physicians reported experiencing less satisfaction when treating symptoms that result from choices for which patients are responsible (multilevel odds ratio 0.57, P = 0.000). Physicians experience less satisfaction in treating conditions characterized by medically unexplained conditions and in treating conditions for which they believe the patient is responsible.

Gender Differences in the Effects of Job Control and Demands on the Health of Korean Manual Workers.

PubMed

Kim, HeeJoo; Kim, Ji Hye; Jang, Yeon Jin; Bae, Ji Young

2016-01-01

We used the job-demand-control model to answer our two research questions concerning the effects of working conditions on self-rated health and gender differences and the association between these working conditions and health among Korean manual workers. Since a disproportionate representation of women in nonstandard work positions is found in many countries, including Korea, it is important to examine how working conditions explain gender inequality in health. We used data from the 2008-2009 Korean National Health and Nutrition Examination Survey and analyzed a total sample of 1,482 men and 1,350 women using logistic regression. We found that job control was positively related to self-rated health, while both physical and mental job demands were negatively related to self-rated health. We also found significant interaction effects of job demands, control, and gender on health. Particularly, female workers' health was more vulnerable to mentally demanding job conditions. We discussed theoretical and practice implications based on these findings.

"You Should Have Seen the Look on Your Face…": Self-awareness of Facial Expressions.

PubMed

Qu, Fangbing; Yan, Wen-Jing; Chen, Yu-Hsin; Li, Kaiyun; Zhang, Hui; Fu, Xiaolan

2017-01-01

The awareness of facial expressions allows one to better understand, predict, and regulate his/her states to adapt to different social situations. The present research investigated individuals' awareness of their own facial expressions and the influence of the duration and intensity of expressions in two self-reference modalities, a real-time condition and a video-review condition. The participants were instructed to respond as soon as they became aware of any facial movements. The results revealed that awareness rates were 57.79% in the real-time condition and 75.92% in the video-review condition. The awareness rate was influenced by the intensity and (or) the duration. The intensity thresholds for individuals to become aware of their own facial expressions were calculated using logistic regression models. The results of Generalized Estimating Equations (GEE) revealed that video-review awareness was a significant predictor of real-time awareness. These findings extend understandings of human facial expression self-awareness in two modalities.

“You Should Have Seen the Look on Your Face…”: Self-awareness of Facial Expressions

PubMed Central

Qu, Fangbing; Yan, Wen-Jing; Chen, Yu-Hsin; Li, Kaiyun; Zhang, Hui; Fu, Xiaolan

2017-01-01

The awareness of facial expressions allows one to better understand, predict, and regulate his/her states to adapt to different social situations. The present research investigated individuals’ awareness of their own facial expressions and the influence of the duration and intensity of expressions in two self-reference modalities, a real-time condition and a video-review condition. The participants were instructed to respond as soon as they became aware of any facial movements. The results revealed that awareness rates were 57.79% in the real-time condition and 75.92% in the video-review condition. The awareness rate was influenced by the intensity and (or) the duration. The intensity thresholds for individuals to become aware of their own facial expressions were calculated using logistic regression models. The results of Generalized Estimating Equations (GEE) revealed that video-review awareness was a significant predictor of real-time awareness. These findings extend understandings of human facial expression self-awareness in two modalities. PMID:28611703

Psychosocial work conditions, unemployment and health locus of control: a population-based study.

PubMed

Sadiq Mohammad Ali; Lindström, Martin

2008-06-01

To investigate the association between psychosocial work conditions, unemployment and lack of belief in the possibility of influencing one's own health. The 2000 public health survey in Scania is a cross-sectional postal questionnaire study with a 59% participation rate. In total, 5180 persons aged 18-64 years who belonged to the workforce and the unemployed were included in this study. Logistic regression models were used to investigate the associations between psychosocial factors at work and unemployment, and lack of belief in the possibility of influencing one's own health (external locus of control). Psychosocial conditions at work were defined according to the Karasek-Theorell demand-control/decision latitudes into relaxed, active, passive, and job strain categories. The multivariate analyses included age, country of birth, education, economic stress, and social participation. In total, 26.6% of all men and 26.9% of all women lack an internal locus of control. The passive, job strain and unemployed categories have significantly higher odds ratios of lack of internal locus of control, as compared to the relaxed reference category. No such significant differences are observed for the active category. These patterns remain in the multivariate models, with the exception of the passive and unemployed categories among men, in which the significant differences disappear. Psychosocial work conditions and unemployment may affect health locus of control. The control dimension in the Karasek-Theorell model seems to be of greatest importance.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

PubMed

Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih

2016-10-01

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed. © 2016 Society for Risk Analysis.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

Wake Vortex Advisory System (WakeVAS) Evaluation of Impacts on the National Airspace System

NASA Technical Reports Server (NTRS)

Smith, Jeremy C.; Dollyhigh, Samuel M.

2005-01-01

This report is one of a series that describes an ongoing effort in high-fidelity modeling/simulation, evaluation and analysis of the benefits and performance metrics of the Wake Vortex Advisory System (WakeVAS) Concept of Operations being developed as part of the Virtual Airspace Modeling and Simulation (VAMS) project. A previous study, determined the overall increases in runway arrival rates that could be achieved at 12 selected airports due to WakeVAS reduced aircraft spacing under Instrument Meteorological Conditions. This study builds on the previous work to evaluate the NAS wide impacts of equipping various numbers of airports with WakeVAS. A queuing network model of the National Airspace System, built by the Logistics Management Institute, Mclean, VA, for NASA (LMINET) was used to estimate the reduction in delay that could be achieved by using WakeVAS under non-visual meteorological conditions for the projected air traffic demand in 2010. The results from LMINET were used to estimate the total annual delay reduction that could be achieved and from this, an estimate of the air carrier variable operating cost saving was made.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Logistic Achievement Test Scaling and Equating with Fixed versus Estimated Lower Asymptotes.

ERIC Educational Resources Information Center

Phillips, S. E.

This study compared the lower asymptotes estimated by the maximum likelihood procedures of the LOGIST computer program with those obtained via application of the Norton methodology. The study also compared the equating results from the three-parameter logistic model with those obtained from the equipercentile, Rasch, and conditional…

A Model for Logistics Systems Engineering Management Education in Europe.

ERIC Educational Resources Information Center

Naim, M.; Lalwani, C.; Fortuin, L.; Schmidt, T.; Taylor, J.; Aronsson, H.

2000-01-01

Presents the need for a systems and process perspective of logistics, and develops a template for a logistics education course. The template addresses functional, process, and supply chain needs and was developed by a number of university partners with core skills in different traditional disciplines. (Contains 31 references.) (Author/WRM)

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry.

PubMed

Tung, Feng-Cheng; Chang, Su-Chao; Chou, Chi-Min

2008-05-01

Ever since National Health Insurance was introduced in 1995, the number of insurants increased to over 96% from 50 to 60%, with a continuous satisfaction rating of about 70%. However, the premium accounted for 5.77% of GDP in 2001 and the Bureau of National Health Insurance had pressing financial difficulties, so it reformed its expenditure systems, such as fee for service, capitation, case payment and the global budget system in order to control the rising medical costs. Since the change in health insurance policy, most hospitals attempted to reduce their operating expenses and improve efficiency. Introducing the electronic logistics information system is one way of reducing the cost of the department of central warehouse and the nursing stations. Hence, the study proposes a technology acceptance research model and examines how nurses' acceptance of the e-logistics information system has been affected in the medical industry. This research combines innovation diffusion theory, technology acceptance model and added two research parameters, trust and perceived financial cost to propose a new hybrid technology acceptance model. Taking Taiwan's medical industry as an experimental example, this paper studies nurses' acceptance of the electronic logistics information system. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. The results of the survey strongly support the new hybrid technology acceptance model in predicting nurses' intention to use the electronic logistics information system. The study shows that 'compatibility', 'perceived usefulness', 'perceived ease of use', and 'trust' all have great positive influence on 'behavioral intention to use'. On the other hand 'perceived financial cost' has great negative influence on behavioral intention to use.

Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration.

PubMed

Koseki, Shige; Nonaka, Junko

2012-09-01

The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of B. cereus was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of B. cereus growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of B. cereus in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation.

dLOGIS: Disaster Logistics Information System

NASA Astrophysics Data System (ADS)

Koesuma, Sorja; Riantana, Rio; Siswanto, Budi; Aji Purnomo, Fendi; Lelono, Sarjoko

2017-11-01

There are three timing of disaster mitigation which is pre-disaster, emergency response and post-disaster. All of those is important in disaster mitigation, but emergency response is important when we are talking about time. Emergency response has limited time when we should give help. Rapid assessment of kind of logistic, the number of survivors, number children and old people, their gender and also for difable person. It should be done in emergency response time. Therefore we make a mobile application for logistics management system. The name of application is dLOGIS, i.e. Disaster Logistics Information System. The application is based on Android system for mobile phone. Otherwise there is also website version. The website version is for maintenance, data input and registration. So the people or government can use it directly when there is a disaster. After login in dLOGIS, there is five main menus. The first main menu shows disaster information, refugees conditions, logistics needed, available logistics stock and already accepted logistics. In the second menu is used for entering survivors data. The field coordinator can enter survivors data based on the rapid assessment in disaster location. The third menu is used for entering kind of logistic. Number and kind of logistics are based on the BNPB needed standard for the survivor. The fourth menu displays the logistics stock available in field coordinator. And the last menu displays the logistics help that already accepted and sent by donation. By using this application when a disaster happened, field coordinator or local government can use maintenance distribution of logistics base on their needs. Also for donor people who will give help to survivor, they can give logistics with the corresponding of survivor needs.

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

PubMed

Espino-Hernandez, Gabriela; Gustafson, Paul; Burstyn, Igor

2011-05-14

In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis. Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed. The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed. In individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Enhancement of the Logistics Battle Command Model: Architecture Upgrades and Attrition Module Development

DTIC Science & Technology

2017-01-05

module. 15. SUBJECT TERMS Logistics, attrition, discrete event simulation, Simkit, LBC 16. SECURITY CLASSIFICATION OF: Unclassified 17. LIMITATION...stochastics, and discrete event model programmed in Java building largely on the Simkit library. The primary purpose of the LBC model is to support...equations makes them incompatible with the discrete event construct of LBC. Bullard further advances this methodology by developing a stochastic

Use of Robust z in Detecting Unstable Items in Item Response Theory Models

ERIC Educational Resources Information Center

Huynh, Huynh; Meyer, Patrick

2010-01-01

The first part of this paper describes the use of the robust z[subscript R] statistic to link test forms using the Rasch (or one-parameter logistic) model. The procedure is then extended to the two-parameter and three-parameter logistic and two-parameter partial credit (2PPC) models. A real set of data was used to illustrate the extension. The…

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Psychiatric symptoms and antiretroviral nonadherence in US youth with perinatal HIV: a longitudinal study.

PubMed

Kacanek, Deborah; Angelidou, Konstantia; Williams, Paige L; Chernoff, Miriam; Gadow, Kenneth D; Nachman, Sharon

2015-06-19

The relationship of specific psychiatric conditions to adherence has not been examined in longitudinal studies of youth with perinatal HIV infection (PHIV). We examined associations between psychiatric conditions and antiretroviral nonadherence over 2 years. Longitudinal study in 294 PHIV youth, 6-17 years old, in the United States and Puerto Rico. We annually assessed three nonadherence outcomes: missed above 5% of doses in the past 3 days, missed a dose within the past month, and unsuppressed viral load (>400 copies/ml). We fit multivariable logistic models for nonadherence using Generalized Estimating Equations, and evaluated associations of psychiatric conditions (attention deficit hyperactivity disorder, disruptive behavior, depression, anxiety) at entry with incident nonadherence using multivariable logistic regression. Nonadherence prevalence at study entry was 14% (3-day recall), 32% (past month nonadherence), and 38% (unsuppressed viral load), remaining similar over time. At entry, 38% met symptom cut-off criteria for at least one psychiatric condition. Greater odds of 3-day recall nonadherence were observed at week 96 for those with depression [adjusted odds ratio (aOR) 4.14, 95% confidence interval (CI) 1.11-15.42] or disruptive behavior (aOR 3.36, 95% CI 1.02-11.10], but not at entry. Those with vs. without attention deficit hyperactivity disorder had elevated odds of unsuppressed viral load at weeks 48 (aOR 2.46, 95% CI 1.27-4.78) and 96 (aOR 2.35, 95% CI 1.01-5.45), but not at entry. Among 232 youth adherent at entry, 16% reported incident 3-day recall nonadherence. Disruptive behavior conditions at entry were associated with incident 3-day recall nonadherence (aOR 3.01, 95% CI 1.24-7.31). In PHIV youth, comprehensive adherence interventions that address psychiatric conditions throughout the transition to adult care are needed.

SpaceNet: Modeling and Simulating Space Logistics

NASA Technical Reports Server (NTRS)

Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen

2008-01-01

This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Scale-invariance underlying the logistic equation and its social applications

NASA Astrophysics Data System (ADS)

Hernando, A.; Plastino, A.

2013-01-01

On the basis of dynamical principles we i) advance a derivation of the Logistic Equation (LE), widely employed (among multiple applications) in the simulation of population growth, and ii) demonstrate that scale-invariance and a mean-value constraint are sufficient and necessary conditions for obtaining it. We also generalize the LE to multi-component systems and show that the above dynamical mechanisms underlie a large number of scale-free processes. Examples are presented regarding city-populations, diffusion in complex networks, and popularity of technological products, all of them obeying the multi-component logistic equation in an either stochastic or deterministic way.

Model comparison for Escherichia coli growth in pouched food.

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi

2006-06-01

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

The Effect of Message Frames on Public Attitudes Toward Criminal Justice Reform for Nonviolent Offenses

PubMed Central

Gottlieb, Aaron

2017-01-01

In recent years, the rhetoric surrounding criminal justice policy has increasingly emphasized reform, rather than being “tough on crime.” Although this change in rhetoric is aimed at building public support for reform, little is known about its efficacy. To test the efficacy of reform rhetoric, I conducted an Internet experiment using Amazon Mechanical Turk. Respondents were randomly assigned to one of six message conditions or to a control condition (no message) and then asked their views about eliminating the use of incarceration for select nonviolent offenses. Results from ordinal logistic regression models suggest that message frames that appeal to a respondent’s self-interest or emphasize the unfairness of the punishment (not who is punished) tend to be most effective. PMID:28943646

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions.

PubMed

Elhenawy, Mohammed; Jahangiri, Arash; Rakha, Hesham A; El-Shawarby, Ihab

2015-10-01

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only. Copyright © 2015 Elsevier Ltd. All rights reserved.

a Bounded Finite-Difference Discretization of a Two-Dimensional Diffusion Equation with Logistic Nonlinear Reaction

NASA Astrophysics Data System (ADS)

Macías-Díaz, J. E.

In the present manuscript, we introduce a finite-difference scheme to approximate solutions of the two-dimensional version of Fisher's equation from population dynamics, which is a model for which the existence of traveling-wave fronts bounded within (0,1) is a well-known fact. The method presented here is a nonstandard technique which, in the linear regime, approximates the solutions of the original model with a consistency of second order in space and first order in time. The theory of M-matrices is employed here in order to elucidate conditions under which the method is able to preserve the positivity and the boundedness of solutions. In fact, our main result establishes relatively flexible conditions under which the preservation of the positivity and the boundedness of new approximations is guaranteed. Some simulations of the propagation of a traveling-wave solution confirm the analytical results derived in this work; moreover, the experiments evince a good agreement between the numerical result and the analytical solutions.

Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige

Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

Interplanetary Supply Chain Risk Management

NASA Technical Reports Server (NTRS)

Galluzzi, Michael C.

2018-01-01

Emphasis on KSC ground processing operations, reduced spares up-mass lift requirements and campaign-level flexible path perspective for space systems support as Regolith-based ISM is achieved by; Network modeling for sequencing space logistics and in-space logistics nodal positioning to include feedstock. Economic modeling to assess ISM 3D printing adaption and supply chain risk.

Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

Flower Power: Sunflowers as a Model for Logistic Growth

ERIC Educational Resources Information Center

Fernandez, Eileen; Geist, Kristi A.

2011-01-01

Logistic growth displays an interesting pattern: It starts fast, exhibiting the rapid growth characteristic of exponential models. As time passes, it slows in response to constraints such as limited resources or reallocation of energy. The growth continues to slow until it reaches a limit, called capacity. When the growth describes a population,…

Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE). Volume 2: Mission payloads subsystem description

NASA Technical Reports Server (NTRS)

Dupnick, E.; Wiggins, D.

1980-01-01

The scheduling algorithm for mission planning and logistics evaluation (SAMPLE) is presented. Two major subsystems are included: The mission payloads program; and the set covering program. Formats and parameter definitions for the payload data set (payload model), feasible combination file, and traffic model are documented.

Automatic Generation of Customized, Model Based Information Systems for Operations Management.

DTIC Science & Technology

The paper discusses the need for developing a customized, model based system to support management decision making in the field of operations ... management . It provides a critique of the current approaches available, formulates a framework to classify logistics decisions, and suggests an approach for the automatic development of logistics systems. (Author)

Item Vector Plots for the Multidimensional Three-Parameter Logistic Model

ERIC Educational Resources Information Center

Bryant, Damon; Davis, Larry

2011-01-01

This brief technical note describes how to construct item vector plots for dichotomously scored items fitting the multidimensional three-parameter logistic model (M3PLM). As multidimensional item response theory (MIRT) shows promise of being a very useful framework in the test development life cycle, graphical tools that facilitate understanding…

Semiparametric Item Response Functions in the Context of Guessing

ERIC Educational Resources Information Center

Falk, Carl F.; Cai, Li

2016-01-01

We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood-based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally…

The Performance of the Linear Logistic Test Model When the Q-Matrix Is Misspecified: A Simulation Study

ERIC Educational Resources Information Center

MacDonald, George T.

2014-01-01

A simulation study was conducted to explore the performance of the linear logistic test model (LLTM) when the relationships between items and cognitive components were misspecified. Factors manipulated included percent of misspecification (0%, 1%, 5%, 10%, and 15%), form of misspecification (under-specification, balanced misspecification, and…

Resolution of an uncertain closed-loop logistics model: an application to fuzzy linear programs with risk analysis.

PubMed

Wang, Hsiao-Fan; Hsu, Hsin-Wei

2010-11-01

With the urgency of global warming, green supply chain management, logistics in particular, has drawn the attention of researchers. Although there are closed-loop green logistics models in the literature, most of them do not consider the uncertain environment in general terms. In this study, a generalized model is proposed where the uncertainty is expressed by fuzzy numbers. An interval programming model is proposed by the defined means and mean square imprecision index obtained from the integrated information of all the level cuts of fuzzy numbers. The resolution for interval programming is based on the decision maker (DM)'s preference. The resulting solution provides useful information on the expected solutions under a confidence level containing a degree of risk. The results suggest that the more optimistic the DM is, the better is the resulting solution. However, a higher risk of violation of the resource constraints is also present. By defining this probable risk, a solution procedure was developed with numerical illustrations. This provides a DM trade-off mechanism between logistic cost and the risk. Copyright 2010 Elsevier Ltd. All rights reserved.

Risk factors for disability discharge in enlisted active duty Army soldiers.

PubMed

Piccirillo, Amanda L; Packnett, Elizabeth R; Cowan, David N; Boivin, Michael R

2016-04-01

The rate of permanent disability retirement in U.S. Army soldiers and the prevalence of combat-related disabilities have significantly increased over time. Prior research on risk factors associated with disability retirement included soldiers retired prior to conflicts in Iraq and Afghanistan. To identify risk factors for disability discharge among soldiers enlisted in the U.S. Army during military operations in Iraq and Afghanistan. In this case-control study, cases included active duty soldiers evaluated for disability discharge. Controls, randomly selected from soldiers with no history of disability evaluation, were matched to cases based on enlistment year and sex. Conditional logistic regression models calculated odds of disability discharge. Attributable fractions estimated burden of disability for specific pre-existing condition categories. Poisson regression models compared risk of disability discharge related to common disability types by deployment and combat status. Characteristics at military enlistment with increased odds of disability discharge included a pre-existing condition, increased age or body mass index, white race, and being divorced. Musculoskeletal conditions and overweight contributed the largest proportion of disabilities. Deployment was protective against disability discharge or receiving a musculoskeletal-related disability, but significantly increased the risk of disability related to a psychiatric or neurological condition. Soldiers with a pre-existing condition at enlistment, particularly a musculoskeletal condition, had increased odds of disability discharge. Risk of disability was dependent on condition category when stratified by deployment and combat status. Additional research examining conditions during pre-disability hospitalizations could provide insight on specific conditions that commonly lead to disability discharge. Copyright © 2016 Elsevier Inc. All rights reserved.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Optimal assignment of workers to supporting services in a hospital

NASA Astrophysics Data System (ADS)

Sawik, Bartosz; Mikulik, Jerzy

2008-01-01

Supporting services play an important role in health care institutions such as hospitals. This paper presents an application of operations research model for optimal allocation of workers among supporting services in a public hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criterion of the problem is to minimize operations costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problem is formulated as an integer program in the literature known as the assignment problem, where the decision variables represent the assignment of people to various jobs. The results of some computational experiments modeled on a real data from a selected Polish hospital are reported.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-01-01

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-07-09

In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Country logistics performance and disaster impact.

PubMed

Vaillancourt, Alain; Haavisto, Ira

2016-04-01

The aim of this paper is to deepen the understanding of the relationship between country logistics performance and disaster impact. The relationship is analysed through correlation analysis and regression models for 117 countries for the years 2007 to 2012 with disaster impact variables from the International Disaster Database (EM-DAT) and logistics performance indicators from the World Bank. The results show a significant relationship between country logistics performance and disaster impact overall and for five out of six specific logistic performance indicators. These specific indicators were further used to explore the relationship between country logistic performance and disaster impact for three specific disaster types (epidemic, flood and storm). The findings enhance the understanding of the role of logistics in a humanitarian context with empirical evidence of the importance of country logistics performance in disaster response operations. © 2016 The Author(s). Disasters © Overseas Development Institute, 2016.

Analysis of Logistics in Support of a Human Lunar Outpost

NASA Technical Reports Server (NTRS)

Cirillo, William; Earle, Kevin; Goodliff, Kandyce; Reeves, j. D.; Andrashko, Mark; Merrill, R. Gabe; Stromgren, Chel

2008-01-01

Strategic level analysis of the integrated behavior of lunar transportation system and lunar surface system architecture options is performed to inform NASA Constellation Program senior management on the benefit, viability, affordability, and robustness of system design choices. This paper presents an overview of the approach used to perform the campaign (strategic) analysis, with an emphasis on the logistics modeling and the impacts of logistics resupply on campaign behavior. An overview of deterministic and probabilistic analysis approaches is provided, with a discussion of the importance of each approach to understanding the integrated system behavior. The logistics required to support lunar surface habitation are analyzed from both 'macro-logistics' and 'micro-logistics' perspectives, where macro-logistics focuses on the delivery of goods to a destination and micro-logistics focuses on local handling of re-supply goods at a destination. An example campaign is provided to tie the theories of campaign analysis to results generation capabilities.

Comparison of Centralized-Manual, Centralized-Computerized, and Decentralized-Computerized Order and Management Information Models for the Turkish Air Force Logistics System.

DTIC Science & Technology

1986-09-01

differentiation between the systems. This study will investigate an appropriate Order Processing and Management Information System (OP&MIS) to link base-level...methodology: 1. Reviewed the current order processing and information model of the TUAF Logistics System. (centralized-manual model) 2. Described the...RDS program’s order processing and information system. (centralized-computerized model) 3. Described the order irocessing and information system of

Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection.

PubMed

Kayano, Mitsunori; Matsui, Hidetoshi; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2016-04-01

High-throughput time course expression profiles have been available in the last decade due to developments in measurement techniques and devices. Functional data analysis, which treats smoothed curves instead of originally observed discrete data, is effective for the time course expression profiles in terms of dimension reduction, robustness, and applicability to data measured at small and irregularly spaced time points. However, the statistical method of differential analysis for time course expression profiles has not been well established. We propose a functional logistic model based on elastic net regularization (F-Logistic) in order to identify the genes with dynamic alterations in case/control study. We employ a mixed model as a smoothing method to obtain functional data; then F-Logistic is applied to time course profiles measured at small and irregularly spaced time points. We evaluate the performance of F-Logistic in comparison with another functional data approach, i.e. functional ANOVA test (F-ANOVA), by applying the methods to real and synthetic time course data sets. The real data sets consist of the time course gene expression profiles for long-term effects of recombinant interferon β on disease progression in multiple sclerosis. F-Logistic distinguishes dynamic alterations, which cannot be found by competitive approaches such as F-ANOVA, in case/control study based on time course expression profiles. F-Logistic is effective for time-dependent biomarker detection, diagnosis, and therapy. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Morphometric-based sexual determination of Bananaquits (Coereba flaveola)

USGS Publications Warehouse

Bibles, Brent D.; Boal, Clint W.

2012-01-01

The Bananaquit (Coereba flaveola) is a common passerine throughout the tropics and has been a convenient species for ecological studies. This species has sexually monomorphic plumage and cannot be reliably sexed unless in breeding condition. This is problematic for demographic and comparative studies, which are contingent upon accurately aging and sexing individuals. Although male Bananaquits are larger than females, there is overlap in both wing chord and mass. We used morphometric data collected over eight years to develop a predictive model based on logistic regression to assign adult Bananaquits to sex. Our model classified 96% of validation individuals to the correct sex. We suggest that this approach may enhance ecological studies of the species by facilitating correct sex determination independent of breeding status. We believe our modeling approach is applicable elsewhere but, because there may be geographical variation across the species distribution, models will need to be customized to local populations.

Temporal association between the influenza virus and respiratory syncytial virus (RSV): RSV as a predictor of seasonal influenza.

PubMed

Míguez, A; Iftimi, A; Montes, F

2016-09-01

Epidemiologists agree that there is a prevailing seasonality in the presentation of epidemic waves of respiratory syncytial virus (RSV) infections and influenza. The aim of this study is to quantify the potential relationship between the activity of RSV, with respect to the influenza virus, in order to use the RSV seasonal curve as a predictor of the evolution of an influenza virus epidemic wave. Two statistical tools, logistic regression and time series, are used for predicting the evolution of influenza. Both logistic models and time series of influenza consider RSV information from previous weeks. Data consist of influenza and confirmed RSV cases reported in Comunitat Valenciana (Spain) during the period from week 40 (2010) to week 8 (2014). Binomial logistic regression models used to predict the two states of influenza wave, basal or peak, result in a rate of correct classification higher than 92% with the validation set. When a finer three-states categorization is established, basal, increasing peak and decreasing peak, the multinomial logistic model performs well in 88% of cases of the validation set. The ARMAX model fits well for influenza waves and shows good performance for short-term forecasts up to 3 weeks. The seasonal evolution of influenza virus can be predicted a minimum of 4 weeks in advance using logistic models based on RSV. It would be necessary to study more inter-pandemic seasons to establish a stronger relationship between the epidemic waves of both viruses.

Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators.

PubMed

Adde, Antoine; Roucou, Pascal; Mangeas, Morgan; Ardillon, Vanessa; Desenclos, Jean-Claude; Rousset, Dominique; Girod, Romain; Briolant, Sébastien; Quenel, Philippe; Flamand, Claude

2016-04-01

Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana. Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991-2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014-2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted. These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.

Systems engineering studies of on-orbit assembly operation

NASA Technical Reports Server (NTRS)

Morgenthaler, George W.

1991-01-01

While the practice of construction has a long history, the underlying theory of construction is relatively young. Very little has been documented as to techniques of logistic support, construction planning, construction scheduling, construction testing, and inspection. The lack of 'systems approaches' to construction processes is certainly one of the most serious roadblocks to the construction of space structures. System engineering research efforts at CSC are aimed at developing concepts and tools which contribute to a systems theory of space construction. The research is also aimed at providing means for trade-offs of design parameters for other research areas in CSC. Systems engineering activity at CSC has divided space construction into the areas of orbital assembly, lunar base construction, interplanetary transport vehicle construction, and Mars base construction. A brief summary of recent results is given. Several models for 'launch-on-time' were developed. Launch-on-time is a critical concept to the assembly of such Earth-orbiting structures as the Space Station Freedom, and to planetary orbiters such as the Mars transfer vehicle. CSC has developed a launch vehicle selection model which uses linear programming to find optimal combinations of launch vehicles of various sizes (Atlas, Titan, Shuttles, HLLV's) to support SEI missions. Recently, the Center developed a cost trade-off model for studying on orbit assembly logistics. With this model it was determined that the most effective size of the HLLV would be in the range of 120 to 200 metric tons to LEO, which is consistent with the choices of General Stafford's Synthesis Group Report. A second-generation Dynamic Construction Activities Model ('DYCAM') process model has been under development, based on our past results in interruptability and our initial DYCAM model. This second-generation model is built on the paradigm of knowledge-based expert systems. It is aimed at providing answers to two questions: (1) what are some necessary or sufficient conditions for judging conceptual designs of spacecraft?, and (2) can a methodology be formulated such that these conditions may be used to provide computer-aided tools for evaluating conceptual designs and planning for space assembly sequences? Early simulation results indicate that the DYCAM model has a clear ability to emulate and simulate human orbital construction processes.

Ergonomics, automation and logistics: practical and effective combination of working methods, a case study of a baking company.

PubMed

Quintana, Leonardo; Arias, Claudia; Cordoba, Jorge; Moroy, Magda; Pulido, Jean; Ramirez, Angela

2012-01-01

The aim of this study was to combine three different analytical methods from three different disciplines to diagnose the ergonomic conditions, manufacturing and supply chain operation of a baking company. The study explores a summary of comprehensive working methods that combines the ergonomics, automation and logistics study methods in the diagnosis of working conditions and productivity. The participatory approach of this type of study that involves the feelings and first-hand knowledge of workers of the operation are determining factors in defining points of action and ergonomic interventions, as well as defining opportunities in the automation of manufacturing and logistics, to cope with the needs of the company. The study identified an ergonomic situation (high prevalence of wrist-hand pain), and the combination of interdisciplinary techniques applied allowed to improve this condition in the company. This type of study allows a primary basis of the opportunities presented by the combination of specialized methods of different disciplines, for the definition of comprehensive action plans for the company. Additionally, it outlines opportunities for improvement and recommendations to mitigate the burden associated with occupational diseases and as an end result improve the quality of life and productivity of workers.

What Are the Odds of that? A Primer on Understanding Logistic Regression

ERIC Educational Resources Information Center

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

Stability of Intercellular Exchange of Biochemical Substances Affected by Variability of Environmental Parameters

NASA Astrophysics Data System (ADS)

Mihailović, Dragutin T.; Budinčević, Mirko; Balaž, Igor; Mihailović, Anja

Communication between cells is realized by exchange of biochemical substances. Due to internal organization of living systems and variability of external parameters, the exchange is heavily influenced by perturbations of various parameters at almost all stages of the process. Since communication is one of essential processes for functioning of living systems it is of interest to investigate conditions for its stability. Using previously developed simplified model of bacterial communication in a form of coupled difference logistic equations we investigate stability of exchange of signaling molecules under variability of internal and external parameters.

Dietary Fiber Intake Is Inversely Associated with Periodontal Disease among US Adults.

PubMed

Nielsen, Samara Joy; Trak-Fellermeier, Maria Angelica; Joshipura, Kaumudi; Dye, Bruce A

2016-12-01

Approximately 47% of adults in the United States have periodontal disease. Dietary guidelines recommend a diet providing adequate fiber. Healthier dietary habits, particularly an increased fiber intake, may contribute to periodontal disease prevention. Our objective was to evaluate the relation of dietary fiber intake and its sources with periodontal disease in the US adult population (≥30 y of age). Data from 6052 adults participating in NHANES 2009-2012 were used. Periodontal disease was defined (according to the CDC/American Academy of Periodontology) as severe, moderate, mild, and none. Intake was assessed by 24-h dietary recalls. The relation between periodontal disease and dietary fiber, whole-grain, and fruit and vegetable intakes were evaluated by using multivariate models, adjusting for sociodemographic characteristics and dentition status. In the multivariate logistic model, the lowest quartile of dietary fiber was associated with moderate-severe periodontitis (compared with mild-none) compared with the highest dietary fiber intake quartile (OR: 1.30; 95% CI: 1.00, 1.69). In the multivariate multinomial logistic model, intake in the lowest quartile of dietary fiber was associated with higher severity of periodontitis than dietary fiber intake in the highest quartile (OR: 1.27; 95% CI: 1.00, 1.62). In the adjusted logistic model, whole-grain intake was not associated with moderate-severe periodontitis. However, in the adjusted multinomial logistic model, adults consuming whole grains in the lowest quartile were more likely to have more severe periodontal disease than were adults consuming whole grains in the highest quartile (OR: 1.32; 95% CI: 1.08, 1.62). In fully adjusted logistic and multinomial logistic models, fruit and vegetable intake was not significantly associated with periodontitis. We found an inverse relation between dietary fiber intake and periodontal disease among US adults ≥30 y old. Periodontal disease was associated with low whole-grain intake but not with low fruit and vegetable intake. © 2016 American Society for Nutrition.

Large scale landslide susceptibility assessment using the statistical methods of logistic regression and BSA - study case: the sub-basin of the small Niraj (Transylvania Depression, Romania)

NASA Astrophysics Data System (ADS)

Roşca, S.; Bilaşco, Ş.; Petrea, D.; Fodorean, I.; Vescan, I.; Filip, S.; Măguţ, F.-L.

2015-11-01

The existence of a large number of GIS models for the identification of landslide occurrence probability makes difficult the selection of a specific one. The present study focuses on the application of two quantitative models: the logistic and the BSA models. The comparative analysis of the results aims at identifying the most suitable model. The territory corresponding to the Niraj Mic Basin (87 km2) is an area characterised by a wide variety of the landforms with their morphometric, morphographical and geological characteristics as well as by a high complexity of the land use types where active landslides exist. This is the reason why it represents the test area for applying the two models and for the comparison of the results. The large complexity of input variables is illustrated by 16 factors which were represented as 72 dummy variables, analysed on the basis of their importance within the model structures. The testing of the statistical significance corresponding to each variable reduced the number of dummy variables to 12 which were considered significant for the test area within the logistic model, whereas for the BSA model all the variables were employed. The predictability degree of the models was tested through the identification of the area under the ROC curve which indicated a good accuracy (AUROC = 0.86 for the testing area) and predictability of the logistic model (AUROC = 0.63 for the validation area).

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A

2017-09-30

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The Effects of Race, Ethnicity, and Mood/Anxiety Disorders on the Chronic Physical Health Conditions of Men From a National Sample

PubMed Central

Johnson-Lawrence, Vicki; Griffith, Derek M.; Watkins, Daphne C.

2013-01-01

Racial/ethnic differences in health are evident among men. Previous work suggests associations between mental and physical health but few studies have examined how mood/anxiety disorders and chronic physical health conditions covary by age, race, and ethnicity among men. Using data from 1,277 African American, 629 Caribbean Black, and 371 non-Hispanic White men from the National Survey of American Life, we examined associations between race/ethnicity and experiencing one or more chronic physical health conditions in logistic regression models stratified by age and 12-month mood/anxiety disorder status. Among men <45 years without mood/anxiety disorders, Caribbean Blacks had lower odds of chronic physical health conditions than Whites. Among men aged 45+ years with mood/anxiety disorders, African Americans had greater odds of chronic physical health conditions than Whites. Future studies should explore the underlying causes of such variation and how studying mental and chronic physical health problems together may help identify mechanisms that underlie racial disparities in life expectancy among men. PMID:23609347

Structure-preserving model reduction of large-scale logistics networks. Applications for supply chains

NASA Astrophysics Data System (ADS)

Scholz-Reiter, B.; Wirth, F.; Dashkovskiy, S.; Makuschewitz, T.; Schönlein, M.; Kosmykov, M.

2011-12-01

We investigate the problem of model reduction with a view to large-scale logistics networks, specifically supply chains. Such networks are modeled by means of graphs, which describe the structure of material flow. An aim of the proposed model reduction procedure is to preserve important features within the network. As a new methodology we introduce the LogRank as a measure for the importance of locations, which is based on the structure of the flows within the network. We argue that these properties reflect relative importance of locations. Based on the LogRank we identify subgraphs of the network that can be neglected or aggregated. The effect of this is discussed for a few motifs. Using this approach we present a meta algorithm for structure-preserving model reduction that can be adapted to different mathematical modeling frameworks. The capabilities of the approach are demonstrated with a test case, where a logistics network is modeled as a Jackson network, i.e., a particular type of queueing network.

Development of the Integrated Biomass Supply Analysis and Logistics Model (IBSAL)

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

Sokhansanj, Shahabaddine; Webb, Erin; Turhollow Jr, Anthony F

2008-06-01

The Integrated Biomass Supply & Logistics (IBSAL) model is a dynamic (time dependent) model of operations that involve collection, harvest, storage, preprocessing, and transportation of feedstock for use at a biorefinery. The model uses mathematical equations to represent individual unit operations. These unit operations can be assembled by the user to represent the working rate of equipment and queues to represent storage at facilities. The model calculates itemized costs, energy input, and carbon emissions. It estimates resource requirements and operational characteristics of the entire supply infrastructure. Weather plays an important role in biomass management and thus in IBSAL, dictating themore » moisture content of biomass and whether or not it can be harvested on a given day. The model calculates net biomass yield based on a soil conservation allowance (for crop residue) and dry matter losses during harvest and storage. This publication outlines the development of the model and provides examples of corn stover harvest and logistics.« less

Relationship between Sensory Perception and Frailty in a Community-Dwelling Elderly Population.

PubMed

Somekawa, S; Mine, T; Ono, K; Hayashi, N; Obuchi, S; Yoshida, H; Kawai, H; Fujiwara, Y; Hirano, H; Kojima, M; Ihara, K; Kim, H

2017-01-01

Aging anorexia, defined as loss of appetite and/or reduced food intake, has been postulated as a risk factor for frailty. Impairments of taste and smell perception in elderly people can lead to reduced enjoyment of food and contribute to the anorexia of aging. To evaluate the relationship between frailty and taste and smell perception in elderly people living in urban areas. Data from the baseline evaluation of 768 residents aged ≥ 65 years who enrolled in a comprehensive geriatric health examination survey was analyzed. Fourteen out of 29-items of Appetite, Hunger, Sensory Perception questionnaire (AHSP), frailty, age, sex, BMI, chronic conditions and IADL were evaluated. AHSP was analyzed as the total score of 8 taste items (T) and 6 smell items (S). Frailty was diagnosed using a modified Fried's frailty criteria. The area under the receiver operator curves for detection of frailty demonstrated that T (0.715) had moderate accuracy, but S (0.657) had low accuracy. The cutoffs, sensitivity, specificity and Youden Index (YI) values for each perception were T: Cutoff 26.5 (YI: 0.350, sensitivity: 0.639, specificity: 0.711) and S: Cutoff 18.5 (YI: 0.246, sensitivity: 0.690, specificity: 0.556). Results from multiple logistic regression models, after adjusting for age, sex, IADL and chronic conditions showed that participants under the T cutoff were associated with exhaustion and those below the S cutoff were associated with slow walking speed. The adjusted logistic models for age, sex, IADL and chronic conditions showed significant association between T and frailty (OR 2.81, 95% CI 1.29-6.12), but not between S and frailty (OR 1.73, 95% CI 0.83-3.63). Taste and smell perception, particularly taste perception, were associated with a greater risk of frailty in community-dwelling elderly people. These results suggest that lower taste and smell perception may be an indicator of frailty in old age.

Applications of Evolutionary Technology to Manufacturing and Logistics Systems : State-of-the Art Survey

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin

Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems. In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

PubMed

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty

PubMed Central

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method. PMID:26417946

A Numerical Study of New Logistic Map

NASA Astrophysics Data System (ADS)

Khmou, Youssef

In this paper, we propose a new logistic map based on the relation of the information entropy, we study the bifurcation diagram comparatively to the standard logistic map. In the first part, we compare the obtained diagram, by numerical simulations, with that of the standard logistic map. It is found that the structures of both diagrams are similar where the range of the growth parameter is restricted to the interval [0,e]. In the second part, we present an application of the proposed map in traffic flow using macroscopic model. It is found that the bifurcation diagram is an exact model of the Greenberg’s model of traffic flow where the growth parameter corresponds to the optimal velocity and the random sequence corresponds to the density. In the last part, we present a second possible application of the proposed map which consists of random number generation. The results of the analysis show that the excluded initial values of the sequences are (0,1).