Search

A continuous monitoring of neurological status can help in reporting the physical and mental health of a person. This can be capitalized for building a healthcare tracking system using a wearable device and a handheld mobile device. In this paper, we have used the non-EEG physiological biosignals dataset which gives practicability among subjects for acquiring data easily from wearable device sensors linearly and comfortably rather than the way of putting the subjects in a cumbersome setup laboratory. This paper proposes a custom fully convolutional-LSTM (FCN-LSTM) network to identify the neurological status of a subject using multivariate time series physiological sensor data. The proposed architecture uses parallel stacks of the convolutional layers and LSTM cells. This combination of different network types is significant for the selected problem as the fully convolutional section of the model extracts the local spatial features in the data, while the LSTM network handles the high-level features and temporal dependencies. The proposed FCN-LSTM model yielded a high accuracy of 98.6% and a precision of 98% on the non-EEG dataset from UT-Dallas. The average accuracy of single-subject results of the dataset using the proposed model was observed to be 99.26%. The results from the proposed model are significantly improved when compared with various state-of-the-art works on this problem. These results strongly suggest that this model, when put on a wearable device, can be effectively used to detect the neurological status or stress that the subject may be going through in real time. [ABSTRACT FROM AUTHOR]

This paper presents an equilibrium-based modeling framework for emergency response (ER) workload balancing for robust operations in metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal maximum workload for each ER station such that the weighted sum of the area-wide expected response time and its variation is minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station's workload increase on the robustness of the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively balanced among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service performance especially in situations with imbalanced workloads. [ABSTRACT FROM AUTHOR]

Objectives: Understanding if police malfeasance might be "contagious" is vital to identifying efficacious paths to police reform. Accordingly, we investigate whether an officer's propensity to engage in misconduct is associated with her direct, routine interaction with colleagues who have themselves engaged in misbehavior in the past. Methods: Recognizing the importance of analyzing the actual social networks spanning a police force, we use data on collaborative responses to 1,165,136 "911" calls for service by 3475 Dallas Police Department (DPD) officers across 2013 and 2014 to construct daily networks of front-line interaction. And we relate these cooperative networks to reported and formally sanctioned misconduct on the part of the DPD officers during the same time period using repeated-events survival models. Results: Estimates indicate that the risk of a DPD officer engaging in misconduct is not associated with the disciplined misbehavior of her ad hoc, on-the-scene partners. Rather, a greater risk of misconduct is associated with past misbehavior, officer-specific proneness, the neighborhood context of patrol, and, in some cases, officer race, while departmental tenure is a mitigating factor. Conclusions: Our observational findings—based on data from one large police department in the United States—ultimately suggest that actor-based and ecological explanations of police deviance should not be summarily dismissed in favor of accounts emphasizing negative socialization, where our study design also raises the possibility that results are partly driven by unobserved trait-based variation in the situations that officers find themselves in. All in all, interventions focused on individual officers, including the termination of deviant police, may be fruitful for curtailing police misconduct—where early interventions focused on new offenders may be key to avoiding the escalation of deviance. [ABSTRACT FROM AUTHOR]

The study examines racial differences in crime victimization rates among Black, Hispanic and White individuals during the stay-at-home order in Dallas city, TX. The study is based on sample of 85,958 calls for service recorded by Dallas Police Department between February 13, 2019, and April 30, 2020, where victims have been identified and their race is known. The findings suggest that Blacks were less likely to experience unintended drop in crime rates during the lockdown and also less likely to see decline in crimes against property when compared to both White and Hispanic victims. At the same time Blacks were more likely to experience increase in crimes committed at the apartments during the lockdown with Black females experiencing higher increase than Black males. There were no differences in crime victimization for incidents occurred in public places and crimes against persons. [ABSTRACT FROM AUTHOR]

Large, low-cost entrants can have a disrupting impact on incumbents. Supercenters may influence competing supermarkets' prices, but their effects on competition between supermarkets' private labels and manufacturer brands remain poorly understood. This study examines the impact of Wal-Mart Supercenters on supermarkets' strategic use of private labels and the control they may exercise over the pricing of manufacturer brands. We use a structural model to investigate alternative pricing scenarios in which the supermarkets have varying control over manufacturer-brand retail prices. The analysis is applied to the milk market in the Dallas/Fort Worth metroplex in an early period of Supercenter growth: 1996–2001. We find that Wal-Mart induces supermarkets to price their private label more competitively and has a very small effect on the pricing of the manufacturer brand. We also identify the type of consumers Wal-Mart Supercenters can attract from supermarkets, leaving incumbents to face a different composition of consumers. Consumers that continue shopping at traditional supermarkets are found to be less price-sensitive, view incumbents as less heterogeneous, and experience the manufacturer brand as more differentiated from private labels. [ABSTRACT FROM AUTHOR]

Overall trends of academic staffing in the US have indicated declines in tenure, increased use of contingent faculty, and stratification of teaching and research efforts. However, little is known about academic staffing patterns in professional programs, and little research has been done using program-level data. The purposes of this study were to identify faculty staffing patterns, determine if patterns change over time, and identify predictors of staffing patterns in US-based physical therapy programs. Yearly program-level accreditation data from 2008 to 2017 were analyzed. A finite mixture model analysis identified staffing patterns. Logistic regression analyses were run to predict category membership and observe change over time. Two academic staffing categories emerged: (1) Broad staffing patterns (90.4% of programs) composed of more core faculty on tenure track and who held academic degrees and (2) Flexible programs (9.7% of observations) with higher numbers of core faculty, fewer faculty on tenure track, and fewer faculty with an academic doctoral degree. There was little change over time from one category to another. Programs were more likely to adopt a Flexible pattern when budget and faculty teaching time rose. Membership to one of two staffing models appears to be predicted by institutional reliance on professional programs for revenues. Either programs lower expenses by having tenured faculty balance teaching and research, or they rely on non-tenured faculty positions and higher enrollments. [ABSTRACT FROM AUTHOR]