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Whether substance abuse treatment centers affect neighborhood crime is hotly debated. Empirical evidence on this issue is lacking because of the difficulty of distinguishing the crime effect of treatment centers in high-crime areas, the inability to make before-and-after comparisons for clinics founded before computerized crime data, and the need for appropriate control sites. The authors present an innovative method (without an actual data analysis) to overcome these challenges. Clinic addresses and crime data are geocoded by street address. Crimes are counted within concentric-circular, 25-meter "buffers" around the clinics. Regression analyses are used to calculate the "crime slope" (β) among the buffers. A negative β indicates more crimes closer to the site. A similar process is used to evaluate crimes around control sites: convenience stores, hospitals, and residential points. This innovative technique provides valid empirical evidence on crime around substance abuse treatment centers.

We investigated the mediating effect of social support in the relationship between the resilience and anxiety of new recruits to the Chinese military during COVID-19. We conducted a routine psychological assessment of 665 new recruits and the results indicated that resilience had a
negative impact on anxiety symptoms and that social support mediated this association. Our findings suggest that improving the resilience of new recruits and strengthening their social support systems during the COVID-19 pandemic could effectively reduce their anxiety and improve their psychological
health during the military adaptation period.

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy.

In this paper, we examine the link between regional financial development and foreign direct investment using a large micro-level dataset of Chinese manufacturing enterprises. We first investigate the effect of regional financial development on foreign direct investment location choices, and find such development to significantly promote foreign direct investment. We then show that regional financial development also plays an important role in foreign direct investment productivity spillovers. Domestic firms located in financially developed regions gain positive knowledge spillovers from foreign direct investment, but the competitive pressure from multinationals exerts a negative influence on the productivity of domestic firms.