Media exposure,threat processing,and mitigation behaviors in Gulf Coast residents facing the co-occurring threats of COVID-19 and hurricanes

The 2020 hurricane season threatened millions of Americans concurrently grappling with COVID-19. Processes guiding individual-level mitigation for these conceptually distinct threats, one novel and chronic (COVID-19), the other familiar and episodic (hurricanes), are unknown. Theories of health protective behaviors suggest that inputs from external stimuli (e.g., traditional and social media) lead to threat processing, including perceived efficacy (self- and response) and perceived threat (susceptibility and severity), guiding mitigation behavior. We surveyed a representative sample of Florida and Texas residents (N = 1846) between April 14, 2020 and April 27, 2020; many had previous hurricane exposure; all were previously assessed between September 8, 2017 and September 11, 2017. Using preregistered analyses, two generalized structural equation models tested direct and indirect effects of media exposure (traditional media, social media) on self-reported (1) COVID-19 mitigation (handwashing, mask-wearing, social distancing) and (2) hurricane mitigation (preparation behaviors), as mediated through perceived efficacy (self- and response) and perceived threat (susceptibility and severity). Self-efficacy and response efficacy were associated with social distancing (p = .002), handwashing, mask-wearing, and hurricane preparation (ps < 0.001). Perceived susceptibility was positively associated with social distancing (p = 0.017) and hurricane preparation (p < 0.001). Perceived severity was positively associated with social distancing (p < 0.001). Traditional media exhibited indirect effects on COVID-19 mitigation through increased response efficacy (ps < 0.05), and to a lesser extent self-efficacy (p < 0.05), and on hurricane preparation through increased self-efficacy and response efficacy and perceived susceptibility (ps < 0.05). Social media did not exhibit indirect effects on COVID-19 or hurricane mitigation. Communications targeting efficacy and susceptibility may encourage mitigation behavior; research should explore how social media campaigns can more effectively target threat processing, guiding protective actions.     

On the selection of predictors by using greedy algorithms and information theoretic criteria

We discuss the use of the following greedy algorithms in the prediction of multivariate time series: Matching Pursuit Algorithm (MPA), Orthogonal Matching Pursuit (OMP), Relaxed Matching Pursuit (RMP), Frank–Wolfe Algorithm (FWA) and Constrained Matching Pursuit (CMP). The last two are known to be solvers for the lasso problem. Some of the algorithms are well-known (e.g. OMP), while others are less popular (e.g. RMP). We provide a unified presentation of all the algorithms, and evaluate their computational complexity for the high-dimensional case and for the big data case. We show how 12 information theoretic (IT) criteria can be used jointly with the greedy algorithms. As part of this effort, we derive new theoretical results that allow modification of the IT criteria such that to be compatible with RMP. The prediction capabilities are tested in experiments with two data sets. The first one involves air pollution data measured in Auckland (New Zealand) and the second one concerns the House Price Index in England (the United Kingdom).     

Single machine scheduling with non-availability interval and optional job rejection

This paper studies the single machine scheduling problem with availability constraints and optional job rejection. We consider the non-resumable and resumable variants, and show that the problems remain ordinary NP-hard, even with the rejection possibility extension, by presenting pseudo-polynomial dynamic-programming (DP) solutions. We also present an enhanced running time implementation of the algorithm of Kellerer and Strusevich (Algorithmica 57(4):769–795, 2010) for the resumable scenario without job rejection. This solution is adapted to efficiently solve the machine non-availability problem with a floating interval and the problem of two competing agents on a single machine, with and without optional job rejection. Numerical experiments support the efficiency of our DP implementation.