Predicting Counterproductive Work Behavior with Explicit and Implicit Measures of Conscientiousness, Agreeableness, and Emotional Stability

The current study leveraged the stressor-emotion model of CWB, the reflective-impulsive model of behavior, and theories of explicit and implicit personality to investigate the roles explicit and implicit aspects of personality, and work stressors have in influencing CWB. The stressor-emotion and reflective-impulsive models suggest that in addition to reflective (i.e., explicit) processes, impulsive (i.e., implicit) processes may also influence CWB because the act can be motivated by negative emotions induced by frustrating working conditions. Theories of personality and motivation suggest that conscientiousness, agreeableness, and emotional stability predict CWB because these traits motivate people to pursue goals that reduce or increase acts of CWB. Explicit and implicit theories of personality suggest that explicit aspects of personality should predict CWB driven by explicit processes, whereas implicit aspects of personality should predict CWB driven by implicit processes. These ideas were tested by examining explicit and implicit conscientiousness, agreeableness, and emotional stability as predictors of CWB, by examining implicit personality\u27s incremental prediction of CWB over explicit personality, and by examining the interactions between implicit personality and work stressors as predictors of CWB. A series of hierarchical regression analyses were conducted using online survey data from 194 participants. The results of this study suggest that CWBs can be influenced by both explicit and implicit aspects of personality; however, in contrast to explicit personality, implicit personality is most likely to influence CWB when individuals experience a high level of work stressors

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop (HITL) evaluations of air traffic management concepts is described. The objective is to replace the difficult manual process with the automated process for creating an initial (seed) scenario that serves as a starting point for manual adjustments for creating the Human-in-the-Loop scenario. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario derived from it to meet the experiment objectives are discussed. Results of comparison of input Human-in-the-Loop-scenario with the Multi-Aircraft Control System output are also presented. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop-scenario, (2) landing rate profile of the traffic generated by the Multi-Aircraft Control System (MACS) using the input scenario compares reasonably well with that intended in the input scenario, and (3) many of the desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation

Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations

Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to repair and adapt. There is an emerging interest in programmatic imitation learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and repair. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose PLUNDER, a novel PIL algorithm that integrates a probabilistic program synthesizer in an iterative Expectation-Maximization (EM) framework to address these shortcomings. Unlike existing PIL approaches, PLUNDER synthesizes probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark PLUNDER against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. PLUNDER policies achieve 95% accuracy in matching the given demonstrations, outperforming the next best baseline by 19%. Additionally, policies generated by PLUNDER successfully complete the tasks 17% more frequently than the nearest baseline

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop evaluations of air traffic management concepts is described. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario designed to meet the experiment objectives are discussed. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop Scenario, (2) landing characteristics of the traffic generated by the Multi-Aircraft Control System using the input scenario compare reasonably well with that intended in the input scenario, and (3) many of desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation

Acceptability and feasibility of a computer-based application to help Aboriginal and Torres Strait Islander Australians describe their alcohol consumption

We examined acceptability and feasibility of a tablet application (“App”) to record self-reported alcohol consumption among Aboriginal and Torres Strait Islander Australians. Four communities (1 urban; 3 regional/remote) tested the App, with 246 adult participants (132 males, 114 females). The App collected (a) completion time; (b) participant feedback; (c) staff observations. Three research assistants were interviewed. Only six (1.4%) participants reported that the App was “hard” to use. Participants appeared to be engaged and to require minimal assistance; nearly half verbally reflected on their drinking or drinking of others. The App has potential for surveys, screening, or health promotion

Optimizing Fresh Agricultural Product Distribution Paths Under Demand Uncertainty

Consumers' demand for fresh agricultural products (FAPs) and their quality requirements are increasing in the current agricultural-product consumption market. FAPs' unique perishability and short shelf-life features mean a high level of delivery efficiency is required to ensure their freshness and quality. However, consumers' demand for FAPs is contingent and geographically dispersed. Therefore, the conflicting relationship between the costs associated with the logistics distribution and the level of delivery quality is important to consider. In this paper, the authors consider a fresh agricultural-product distribution path planning problem with time windows (FAPDPPPTW). To address the FAPDPPPTW under demand uncertainty, a mixed-integer linear programming model based on robust optimization is proposed. Moreover, a particle swarm optimization algorithm combined with a variable neighborhood search is designed to solve the proposed mathematical model. The numerical experiment results show the robustness and fast convergence of the algorithm.</p

Common susceptibility variants are shared between schizophrenia and psoriasis in the Han Chinese population

Previous studies have shown that individuals with schizophrenia have a greater risk for psoriasis than a typical person. This suggests that there might be a shared genetic etiology between the 2 conditions. We aimed to characterize the potential shared genetic susceptibility between schizophrenia and psoriasis using genome-wide marker genotype data