Understanding foraging flexibility in urban vervet monkeys,Chlorocebus pygerythrus,for the benefit of human-wildlife coexistence

Urban Ecosystems - Generalist wildlife species often thrive in urban environments because of increased anthropogenic resources. However, human-wildlife interactions, especially if negative, raise...     

Capturing temporal dynamics of fear behaviors on a moment‐to‐moment basis

Identifying patterns of fearful behaviors early and accurately is essential to identify children who may be at increased risk for psychopathology. Previous work focused on the total amount of fear by using composites across time. However, considering the temporal dynamics of fear expression might offer novel insights into the identification of children at risk. One hundred and twenty‐five toddlers participated in high‐ and low‐fear tasks. Data were modeled using a novel two‐step approach. First, a hidden Markov model estimated latent fear states and transitions across states over time. Results revealed children's behavior was best represented by six behavioral states. Next, these states were analyzed using sequence clustering to identify groups of children with similar dynamic trajectories through the states. A four‐cluster solution found groups of children varied in fear response and regulation process: “external regulators” (using the caregiver as a regulation tool), “low reactive” (low reaction to stimulus), “fearful explorers” (managing their own internal state with minimal assistance from the caregiver), and “high fear” (fearful/at‐caregiver state regardless of task). The combination of analytic tools enabled fine‐grained examination of the processes of fearful temperament. These insights may help prevention programs target behaviors that perpetuate anxious behavior in the moment.     

A Generalization of Wendell's Tolerance Approach to Sensitivity Analysis in Linear Programming

A criticism of linear programming has been that the data which are available in practice are too inexact and unreliable for linear programming to properly work. Managers are therefore concerned with how much actual values may differ from the estimates that were used in the model before the results become irrelevant. Sensitivity analysis emerged to help deal with the uncertainties inherent in the linear programming model. However, the ranges calculated are generally valid only when a single coefficient is varied. An extension of sensitivity analysis, the 100 Percent Rule, allows the simultaneous variation of more than one element in a vector, but does not permit the independent variation of the elements. A tolerance approach to sensitivity analysis enables the consideration of simultaneous and independent change of more than one coefficient. However, the ranges developed are unnecessarily restricted and may be reduced in width to zero when primal or dual degeneracy exists. This paper presents an extension of the tolerance approach which reduces the limitations of both the traditional and tolerance approaches to sensitivity analysis.     

Faculty Performance Evaluation: Modeling to Improve Personnel Decisions*

Based on likely future changes in faculty evaluation practices, this paper examines how institutions of higher education might operationalize performance evaluation as related to research, teaching, and service. A model is developed that allows coupling performance evaluation and relevant market considerations with merit pay, tenure, and promotion decisions. The approach is specifically applicable to tenure-track faculty members in colleges and universities.     

Multi-Item Grouping Algorithm Yielding Near-Optimal Logistics Cost

This article describes an algorithm used to formulate an inbound consolidation strategy when multi-items are replenished in groups and when total logistics cost is to be minimized. The importance of this algorithm is threefold (1) no optimal procedure exists for grouping multi-items when minimizing total logistics cost, (2) a complete enumeration of all possible groups (from which the optimal grouping set can be identified) is impractical due to the combinatorial nature of the grouping problem, and (3) no other heuristics have been developed that adequately reflect shipping cost in the analysis like this one does. We report the experience of using this algorithm to group and reorder 75 selected ensembles containing a total of 517 inventory items of a retail merchandising firm.     

Solution Procedures for the Lot-Streaming Problem

Lot streaming is the process of splitting a job into sublots so its operations can be overlapped and its progress accelerated. We present a computationally efficient procedure for solving the m-machine, two-sublot problem, and we discuss the bottleneck insights that emerge from the analysis. We also examine heuristic approaches for more than two sublots and discuss computational results for these procedures.     

Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation

Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our aim was to develop a method to estimate time-dependent survival probabilities and represent them graphically. Cox models with time-dependent indicators to represent state changes were fitted, and survival probabilities were plotted using pre-specified times of state changes. Time-varying hazard ratios for the state change were also explored. The method was applied to data from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). Survival curves showing a ‘split’ at a pre-specified time t allow for the qualitative comparison of survival probabilities between patients with similar baseline covariates who do and do not experience a state change at time t. Time since state change interactions can be visually represented to reflect changing hazard ratios over time. A2ALL study results showed differences in survival probabilities among those who did not receive a transplant, received a living donor transplant, and received a deceased donor transplant. These graphical representations of survival curves with time-dependent indicators improve upon previous methods and allow for clinically meaningful interpretation.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

“Shifting the Power”: Youth Activists’ Narrative Reframing of the Immigrant Rights Movement

This article explores collective efforts by undocumented youth activists to use storytelling to reframe the debates around immigration reform and discursively position themselves as the rightful leaders of a movement that had been dominated by adult citizen‐advocates. Drawing on 19 months of fieldwork, 37 in‐depth interviews, and hundreds of pages of movement documents, I show how youth activists in the United States worked together to develop stories that: (1) drew into question the legitimacy of adult citizen‐advocates to speak on issues of immigration and (2) cast undocumented immigrant youth as the proper authorities on these matters. I argue that through collective storytelling and character work, the activists were able to subvert adult citizen authority and construct themselves as powerful, new collective actors in the contemporary immigrant rights movement. I conclude by discussing some of the practical implications and limitations of using narrative reframing strategies to advance the social change agendas of marginalized movement factions.