Reduction in COVID-19 related resource loss and decline in prevalence of probable depression in Chinese adults:an application of the Conservation of Resource Theory

Background The levels of resource losses due to coronavirus disease 2019(COVID-19)and mental distress may change during the pandemic period.Based on the Conservation of Resource(COR)Theory,this study investigated such changes and the mediation between survey time(Round 2 versus Round 1)and depression via resource losses.Methods Two serial random population-based telephone surveys interviewed 209 and 458 Hong Kong Chinese adults in April 2020 and May 2021,respectively.Probable depression was defined as 9-item Patient Health Questionnaire(PHQ-9)score≥10.The validated Conservation of Resources Scale for COVID-19(CORS-COVID-19)scale was used to assess resource losses due to COVID-19.Multivariable logistic regression analysis,hierarchical logistic regression analysis,and structural equation modeling(SEM)was conducted to test the association,interaction,and mediation hypotheses,respectively.Results The prevalence of probable depression declined from 8.6%to 1.0%over time,together with reductions in losses of financial resource(Cohen’s d=0.88),future control(Cohen’s d=0.39),social resource(Cohen’s d=0.60),and family resource(Cohen’s d=0.36)due to COVID-19.All the overall scale/subscales of the CORS-COVID-19 were positively and associated with probable depression[adjusted odds ratio(aOR)ranged from 2.72 to 42.30].In SEM,the survey time was negatively associated with the latent variable of resource loss(β=−0.46),which in turn was positively associated with probable depression(β=0.73).In addition,the direct effect of survey time on probable depression was statistically non-significant(β=−0.08),indicating a full mediation effect of resource losses.Conclusions The lessening of the resource losses might have fully accounted for the significant decline in probable depression from Month 3 to 15 since the first COVID-19 outbreak in Hong Kong,China.The level of depression might have increased during the first phase of the pandemic,but might decline in the later phases if resources losses could be lessened.All stakeholders should hence work together to minimize individuals’COVID-19-related resource losses to prevent depression in the general population,as COVID-19 might be lasting.

A model for end-of-life vehicles management based on green manufacturing

Like any industrial product, the vehicles also do have end of their service lives and ultimately result in end-of-life vehicle (ELV) at a certain stage of the vehicle life. The concern of this paper is to make a general review on ELV management process and present a model for ELV management on the basis of green manufacturing so as to reduce the environmental impact and minimize the resources loss due to the ELVs. The various phases of vehicle life cycle starting from design, manufacturing and utilization, occurrence of ELV and then the storage, depollution, dismantling and finally the shredding and post shredder material processing phases of ELV for the resources recovery, all of these phases need to be considered to give totality to the model, which makes end-of-life vehicles management more benign for environment, and more efficient for the usage of resources.

An Air Defense Weapon Target Assignment Method Based on Multi-Objective Artificial Bee Colony Algorithm

With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.