Synthesis and cytotoxic studies of a new series of pyridinoxymethylcoumarins

A series of 3-​(pyridin-​3-​yloxymethyl)​-​chromen-​2-​ones were synthesized by the reaction of substituted 4-​bromomethylcoumarins with 3-​hydroxypyridines. The synthesized compds. were screened for their cytotoxic activities against Dalton's ascitic lymphoma (DAL) and Ehrlich ascites carcinoma (EAC) cell lines. The 2-​(2-​Methyl-​pyridin-​3-​yloxymethyl)​-​benzo[f]​chromen-​3-​one was found to be the most cytotoxic against DAL cell line and 6-​Isopropyl-​3-​(2-​methyl-​pyridin-​3-​yloxymethyl)​-​chromen-​2-​one was found to be the most cytotoxic against EAC cell line

Simultaneous equation penalized likelihood estimation of vehicle accident injury severity

A bivariate system of equations is developed to model ordinal polychotomous dependent variables within a simultaneous additive regression framework. The functional form of the covariate effects is assumed fairly flexible with appropriate smoothers used to account for non‐linearities and spatial variability in the data. Non‐Gaussian error dependence structures are dealt with by means of copulas whose association parameter is also specified in terms of a generic additive predictor. The framework is employed to study the effects of several risk factors on the levels of injury sustained by individuals in two‐vehicle accidents in France. The use of the methodology proposed is motivated by the presence of common unobservables that may affect the interrelationships between the parties involved in the same crash and by the possible heterogeneity in individuals’ characteristics and accident dynamics. Better calibrated estimates are obtained and misspecification reduced via an enhanced model specification

Biclustering models for two-mode ordinal data

The work in this paper introduces finite mixture models that can be used to simul- taneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets

On Jointly Analyzing the Physical Activity Participation Levels of Individuals in a Family Unit Using a Multivariate Copula Framework

At the time of publication Ipek N. Sener was at the Texas Transportation Institute, Naveen Eluru was at McGill University, and Chandra R. Bhat was at the University of Texas at Austin.The current paper focuses on analyzing and modeling the physical activity participation levels (in terms of the number of daily “bouts” or “episodes” of physical activity during a weekend day) of all members of a family jointly. Essentially, we consider a family as a “cluster” of individuals whose physical activity propensities may be affected by common household attributes (such as household income and household structure) as well as unobserved family-related factors (such as family life-style and health consciousness, and residential location-related factors). The proposed copula-based clustered ordered-response model structure allows the testing of various dependency forms among the physical activity propensities of individuals of the same household (generated due to the unobserved family-related factors), including non-linear and asymmetric dependency forms. The proposed model system is applied to study physical activity participations of individuals, using data drawn from the 2000 San Francisco Bay Area Household Travel Survey (BATS). A number of individual factors, physical environment factors, and social environment factors are considered in the empirical analysis. The results indicate that reduced vehicle ownership and increased bicycle ownership are important positive determinants ofweekend physical activity participation levels, though these results should be tempered by the possibility that individuals who are predisposed to physical activity may choose to own fewer motorized vehicles and more bicycles in the first place. Our results also suggest that policy interventions aimed at increasing children‟s physical activity levels could potentially benefit from targeting entire family units rather than targeting only children. Finally, the results indicate strong and asymmetric dependence among the unobserved physical activity determinants of family members. In particular, the results show that unobserved factors (such as residence location-related constraints and family lifestyle preferences) result in individuals in a family having uniformly low physical activity, but there is less clustering of this kind at the high end of the physical activity propensity spectrum.Civil, Architectural, and Environmental Engineerin

On Jointly Analyzing the Physical Activity Participation Levels of Individuals in a Family Unit Using a Multivariate Copula Framework

At the time of publication Ipek N. Sener was at the Texas Transportation Institute, Naveen Eluru was at McGill University, and Chandra R. Bhat was at the University of Texas at Austin.The current paper focuses on analyzing and modeling the physical activity participation levels (in terms of the number of daily “bouts” or “episodes” of physical activity during a weekend day) of all members of a family jointly. Essentially, we consider a family as a “cluster” of individuals whose physical activity propensities may be affected by common household attributes (such as household income and household structure) as well as unobserved family-related factors (such as family life-style and health consciousness, and residential location-related factors). The proposed copula-based clustered ordered-response model structure allows the testing of various dependency forms among the physical activity propensities of individuals of the same household (generated due to the unobserved family-related factors), including non-linear and asymmetric dependency forms. The proposed model system is applied to study physical activity participations of individuals, using data drawn from the 2000 San Francisco Bay Area Household Travel Survey (BATS). A number of individual factors, physical environment factors, and social environment factors are considered in the empirical analysis. The results indicate that reduced vehicle ownership and increased bicycle ownership are important positive determinants ofweekend physical activity participation levels, though these results should be tempered by the possibility that individuals who are predisposed to physical activity may choose to own fewer motorized vehicles and more bicycles in the first place. Our results also suggest that policy interventions aimed at increasing children‟s physical activity levels could potentially benefit from targeting entire family units rather than targeting only children. Finally, the results indicate strong and asymmetric dependence among the unobserved physical activity determinants of family members. In particular, the results show that unobserved factors (such as residence location-related constraints and family lifestyle preferences) result in individuals in a family having uniformly low physical activity, but there is less clustering of this kind at the high end of the physical activity propensity spectrum.Civil, Architectural, and Environmental Engineerin

A Multivariate Ordered Response Model System for Adults’ Weekday Activity Episode Generation by Activity Purpose and Social Context

This paper proposes a multivariate ordered-response system framework to model the interactions in non-work activity episode decisions across household and non-household members at the level of activity generation. Such interactions in activity decisions across household and non-household members are important to consider for accurate activity-travel pattern modeling and policy evaluation. The econometric challenge in estimating a multivariate ordered-response system with a large number of categories is that traditional classical and Bayesian simulation techniques become saddled with convergence problems and imprecision in estimates, and they are also extremely cumbersome if not impractical to implement. We address this estimation problem by resorting to the technique of composite marginal likelihood (CML), an emerging inference approach in the statistics field that is based on the classical frequentist approach, is very simple to estimate, is easy to implement regardless of the number of count outcomes to be modeled jointly, and requires no simulation machinery whatsoever. The empirical analysis in the paper uses data drawn from the 2007 American Time Use Survey (ATUS) and provides important insights into the determinants of adults’ weekday activity episode generation behavior. The results underscore the substantial linkages in the activity episode generation of adults based on activity purpose and accompaniment type. The extent of this linkage varies by individual demographics, household demographics, day of the week, and season of the year. The results also highlight the flexibility of the CML approach to specify and estimate behaviorally rich structures to analyze inter-individual interactions in activity episode generation

A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity

This paper formulates and estimates an econometric model, referred to as the latent segmentation based generalized ordered logit (LSGOL) model, for examining driver injury severity. The proposed model probabilistically allocates drivers (involved in a crash) into different injury severity segments based on crash characteristics to recognize that the impacts of exogenous variables on driver injury severity level can vary across drivers based on both observed and unobserved crash characteristics. The proposed model is estimated using Victorian Crash Database from Australia for the years 2006 through 2010. The model estimation incorporates the influence of a comprehensive set of exogenous variables grouped into six broad categories: crash characteristics, driver characteristics, vehicle characteristics, roadway design attributes, environmental factors and situational factors. The results clearly highlight the need for segmentation based on crash characteristics. The crash characteristics that affect the allocation of drivers into segments include: collision object, trajectory of vehicle's motion and manner of collision. Further, the key factors resulting in severe driver injury severity are driver age 65 and above, driver ejection, not wearing seat belts and collision in a high speed zone. The factors reducing driver injury severity include the presence of pedestrian control, presence of roundabout, driving a panel van, unpaved road condition and the presence of passengers

Examining driver injury severity in two vehicle crashes - a copula based approach

A most commonly identified exogenous factor that significantly affects traffic crash injury severity sustained is the collision type variable. Most studies consider collision type only as an explanatory variable in modeling injury. However, it is possible that each collision type has a fundamentally distinct effect on injury severity sustained in the crash. In this paper, we examine the hypothesis that collision type fundamentally alters the injury severity pattern under consideration. Toward this end, we propose a joint modeling framework to study collision type and injury severity sustained as two dimensions of the severity process. We employ a copula based joint framework that ties the collision type (represented as a multinomial logit model) and injury severity (represented as an ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across drivers) in the dependency structure. Further, the study incorporates collision type as a vehicle-level, as opposed to a crash-level variable as hitherto assumed in earlier research, while also examining the impact of a comprehensive set of exogenous factors on driver injury severity. The proposed modeling system is estimated using collision data from the province of Victoria, Australia for the years 2006 through 2010

An Analysis of Bicycle Route Choice Preferences in Texas, U.S

In the U.S., the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling). The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing bicyclists' route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists' characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists' demographics in bicycle route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs, red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle facility on the route.Civil, Architectural, and Environmental Engineerin

A Flexible Spatially Dependent Discrete Choice Model: Formulation and Application to Teenagers' Weekday Recreational Activity Participation

This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate ordered-response discrete choice models with flexible copula-based spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides standard error estimates for all parameters, and does not require any simulation machinery. The combined copula-CML approach proposed here should be appealing for general multivariate modeling contexts because it is simple and flexible, and is easy to implement The ability of the CML approach to recover the parameters of a spatially ordered process is evaluated using a simulation study, which clearly points to the effectiveness of the approach. In addition, the combined copula-CML approach is applied to study the daily episode frequency of teenagers physically active and physically inactive recreational activity participation, a subject of considerable interest in the transportation, sociology, and adolescence development fields. The data for the analysis are drawn from the 2000 San Francisco Bay Area Survey. The results highlight the value of the copula approach that separates the univariate marginal distribution form from the multivariate dependence structure, as well as underscore the need to consider spatial effects in recreational activity participation. The variable effects indicate that parents physical activity participation constitutes the most important factor influencing teenagers physical activity participation levels. Thus, an effective way to increase active recreation among teenagers may be to direct physical activity benefit-related information and education campaigns toward parents, perhaps at special physical education sessions at the schools of teenagers.Civil, Architectural, and Environmental Engineerin