Resilience as a predictor for why some marital relationships flourished and others struggled during the initial months of COVID-19

Using the theory of resilience and relational load, this study examined how married individuals' baseline communal orientation (CO) and relational load (RL) at the beginning of the pandemic predicted their stress, conflict, mental health, and flourishing during quarantine. Using a Qualtrics Panel, married individuals (N = 3,601) completed four online surveys from April to June 2020. Results revealed the initial levels of CO brought to quarantine predicted less stress and conflict, and better mental health and flourishing at baseline, and these outcomes remained relatively stable across the next 3 months. RL at baseline did the exact opposite for these outcomes, making coping more difficult. We also hypothesized CO and RL moderate the impact of stress (T1) on mental health 3 months later by reducing conflict. Rather than serving as buffers, CO and RL at baseline directly affected conflict (T2/T3) and mental health (T4) throughout quarantine.

Mathematical search technique for detecting moving novel coronavirus disease (COVID-19) based on minimizing the weight function.

The study of search plans has found considerable interest between searchers due to its interesting applications in our real life like searching for located and moving targets. This paper develops a method for detecting moving targets. We propose a novel strategy based on weight function W ( Z ) , W ( Z ) = λ H ( Z ) + ( 1 - λ ) L ( Z ) , where H ( Z ) , L ( Z ) are the total probabilities of un-detecting, and total effort respectively, is searching for moving novel coronavirus disease (COVID-19) cells among finite set of different states. The total search effort will be presented in a more flexible way, so it will be presented as a random variable with a given distribution. The objective is searching for COVID-19 which hidden in one of n cells in each fixed number of time intervals m and the detection functions are supposed to be known to the searcher or robot. We look in depth for the optimal distribution of the total effort which minimizes the probability of undetected the target over the set of possible different states. The effectiveness of this model is illustrated by presenting a numerical example.

Optimal discrete search for a randomly moving COVID19

In this paper, we subedit a search for a randomly moving Coronavirus (COVID-19) among a finite set of different states. We use a monitoring system to search for COVID-19 which is hidden in one of the n cells of the respiratory system in the human body in each fixed number of time intervals m. The expected rescue time of the patient and detecting COVID-19 has been obtained. Also, we extend the results and obtain the total optimal expected search time of COVID-19. The optimal search strategy is derived suing a dynamic programming algorithm. An illustrative real life example introduced to clear the applicability of this model. © 2020 Natural Sciences Publishing. All rights reserved.