The impact of the COVID-19 prevention and economic interventions on express delivery industry:Evidence from China

As a fundamental logistics service,the express delivery(ED)industry provides production and consumption activities to maintain efficient supply chain operations.It plays an important role in promoting industrial development and maintaining daily life and consumption.This study examined the effects of COVID-19 on the ED industry using ED package delivery data from 31 provinces(autonomous regions and municipalities)in China.Using a difference-in-differences method,the COVID-19 outbreak was found to have a positive effect and epidemic prevention and control(EPC)policies were found to have negative effects on the development of the ED industry.In addition to EPC policies,the Chinese government has implemented other short-term economic interventions(SEI),such as the resumption of work and production(RWP),policies to balance economic growth,and epidemic prevention.This study divided the pandemic period into three stages to analyze the different impacts of EPC and SEI policy changes on the ED industry.The RWP policies adopted during the pandemic positively impacted the volume and income of packages delivered to the ED industry.The conclusions of this study are significant for the ED industry in addressing public health emergencies and for the Chinese government to formulate EPC and SEI policies during epidemic periods.

Iatrogenic opioid dependence is endemic and legal: Genetic addiction risk score (GARS) with electrotherapy a paradigm shift in pain treatment programs

The mounting endemic of prescription iatrogenic opioid dependence in pain patients provoked this treatise about an alternative method that can be used to treat pain, improve function and reduce the risk of opioid dependence. It is well known that as well as the side effects reported for chronic opioid therapy, genetically predisposed individuals are at risk for opioid dependence. We propose the use of the Genetic Addiction Risk Score (GARS) assessment to identify patients early in treatment who should avoid narcotic pain medications. Primarily, this review will be an exploration of the mechanisms of action of an electrotherapeutic alternative to narcotic treatment that can be used to augment tissue healing and reduce the pain associated with human injuries and neuropathies. This particular electrotherapeutic device was developed at the Electronic Waveform Laboratory in Huntington Beach, California and is called the H-Wave? device. The primary effect of the H-Wave?device is stimulation (HWDS) of small diameter fibers of “red-slow-twitch” skeletal muscle. Mechanisms of action of HWDS have been investigated in both animal and human studies. They include edema reduction, induction of nitric oxide dependent augmented microcirculation and angiogenesis, small muscle contraction that eliminates transcapillary fluid shifts, reducing the painful effects of tetanizing fatigue and gradual loading of healing injured muscle tissue that helps repair and remodeling. A recent metaanalysis found a moderate-to-strong-positive effect of the HWDS in providing pain relief, reducing the requirement for pain medication, with the most robust effect being increased functionality. We are proposing that GARS can be used to identify those at risk of developing opioid dependence and that the need for opioid analgesia can be reduced by use of this electro therapeutic alternative to opioid analgesia in the treatment of pain and injuries.

A statistical learning approach for stock selection in the Chinese stock market

Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index.Decile portfolios are formed according to rankings of the forecasted future cumulative returns.The equity market’s neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to,and a low correlation with,the Shanghai Composite Index.To make our strategy more useful to practitioners,we evaluate the proposed stock selection strategy’s performance by allowing only long positions,and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market.The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index.A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.

Group Buying of Competing Retailers with Fairness Concerns

The fairness concern behavior is particularly common among members in supply chain framework.The downstream retailers often compare their profits with those of the other members in the supply chain,especially that of the supplier,and do not like the unfavorable distribution of the total profits.This work considers a supply chain consisting of a supplier and two competing retailers with fairness concerns.The supplier offers a wholesale contract with quantity discount,and the two competing retailers have the options of individual purchasing and group buying.Through a Stackelberg game model,the optimal decisions for the players are obtained for the group buying and the individual purchasing strategies.The results indicate that the supplier benefits more than the retailers from the group buying strategy.In particular,the group buying strategy gives the supplier a higher profit if the operational cost of group buying is below a threshold.Furthermore,the fairness concern behavior of the retailers does not always hurt the profit of the supplier.Specifically,the supplier will benefit from the fairness concern behavior of the retailers in individual purchasing when the retailers operates in a relatively poor environment.Finally,market competition between the two fairness concerned retailers does not always benefit the supplier.Fierce competition between the fairness concerned retailers reduces the supplier's profit if the group buying strategy is used when the retailers operates in a relatively poor environment.

Bayesian quantile semiparametric mixed-effects double regression models

Semiparametric mixed-effects double regression models have been used for analysis of longitu-dinal data in a variety of applications,as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors.However,these models are commonly estimated based on the normality assumption for the errors and the results may thus be sensitive to outliers and/or heavy-tailed data.Quantile regression is an ideal alternative to deal with these problems,as it is insensitive to heteroscedasticity and outliers and can make statistical analysis more robust.In this paper,we consider Bayesian quantile regression analysis for semiparamet-ric mixed-effects double regression models based on the asymmetric Laplace distribution for the errors.We construct a Bayesian hierarchical model and then develop an efficient Markov chain Monte Carlo sampling algorithm to generate posterior samples from the full posterior dis-tributions to conduct the posterior inference.The performance of the proposed procedure is evaluated through simulation studies and a real data application.