Hui Zhang

Person Re-identification (person Re-ID) plays an important role in dynamic evacuation path planning and person tracking in emergency scenarios. However, its robustness is severely challenged in such conditions, where persons’ appearances may change rapidly due to stress responses or environmental interventions. Meanwhile, privacy regulations and data access constraints limit the availability of long-term surveillance data, hindering the generalization capability of re-identification models. Virtual try-on technologies offer a promising means of enriching appearance diversity under limited data conditions. In this study, a virtual try-on–based data augmentation method for person Re-ID is developed. To address inaccurate clothing mask extraction caused by low image resolution, occlusions, and complex backgrounds, the original mask generation module used in existing virtual try-on pipelines is replaced with a composite framework integrating Grounding DINO and the Segment Anything Model (SAM). The proposed framework enables precise extraction of clothing regions using text-based prompts, through which appearance-diverse person images are generated. Extensive comparative experiments and multi-level analyses demonstrate that the generated images exhibit high visual realism, preserve identity-related information, and do not introduce systematic distribution shifts. Controlled experiments on a ResNet-50–based benchmark further confirm that the proposed data augmentation strategy consistently improves re-identification performance.

This article presents an online full real-time dual nonuniform rational B-spline interpolator. Under the constraints of the kinematic parameters of the drive axis, the nonlinear effects of the five-axis CNC are solved on the basis of dual NURBS curve trajectory. Furthermore, smooth interpolation data are generated to realize machining efficiency, balance of quality and machining accuracy. After introducing the idea of ​​interval division, this research divides the tool path into instruction speed, transition speed, and constant speed intervals. In order to prevent the increase of the computational burden of the numerical control system due to the too large sampling data points, the two-step scanning method is adopted to realize the precise expression of the parameter position of the starting point in each interval, and the calculation of the interval speed is studied. Further, in order to meet the real-time requirements of industrial systems and avoid inputting all trajectory segments into the system for calculation, this article proposes a prospective velocity bidirectional scanning planning method based on interval division, which realizes the segmented planning of the full trajectory. The planning result adopts the S-curve acceleration and deceleration control commonly used in the industry to realize the interpolation output. The proposed system interpolation method belongs to the online real-time processing and can be applied to the actual numerical control system. The proposed interpolator is verified by simulation and experimental tests for the cutter shaft to participate in the oblique flow impeller.

This study aimed to analyze the impact of hosting large events on the spread of pandemics, taking Tokyo Olympics 2020 as a case study. A risk assessment method for the whole organization process was established, which could be used to evaluate the effectiveness of various risk mitigation measures. Different scenarios for Games participants and Japanese residents during the Tokyo Olympics were designed based on the infection control protocols proposed by the Olympic Committee and local governments. A modified Wells–Riley model considering the influence of social distance, masking and vaccination, and an SIQRV model that introduced the effect of quarantine and vaccination strategies on the pandemic spread were developed in this study. Based on the two models, our predicted results of daily confirmed cases and cumulative cases were obtained and compared with reported data, where good agreement was achieved. The results show that the two core infection control strategies of the bubble scheme and frequent testing scheme curbed the spread of the COVID-19 pandemic during the Tokyo Olympics. Among Games participants, Japanese local staff accounted for more than 60% of the total in positive cases due to their large population and most relaxed travel restrictions. The surge in positive cases was mainly attributed to the high transmission rate of the Delta variant and the low level of immunization in Japan. Based on our simulation results, the risk management flaws for the Tokyo Olympics were identified and improvement measures were investigated. Moreover, a further analysis was carried out on the impact of different preventive measures with respect to minimizing the transmission of new variants with higher transmissibility. Overall, the findings in this study can help policymakers to design scientifically based and practical countermeasures to cope with pandemics during the hosting of large events.

The MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series acquired during nearly two decades (2000 to 2020) covering the area burned by the Camp Fire (California) in 2018 is investigated in this study by using the multifractal detrended fluctuation analysis in relation to the recovery process of vegetation after fire. In 2008, the same area was partially burned by two wildfires, the BTU Lightning Complex Fire and the Humboldt Fire. Our results indicate that all vegetation index time series are featured by six- and twelve-month modulating periodicities, with a larger spectral content at longer periods for two-fire-affected sites. Furthermore, two fires cause an increase of the persistence of the NDVI and EVI time series and an increase of the complexity, suggesting that the recovery process of vegetation dynamics of fire-affected sites is characterized by positive feedback mechanisms, driving the growth-generating phenomena, which become even more effective in those sites affected by two fires.

The ongoing novel coronavirus (COVID‐19) epidemic has evolved into a full range of challenges that the world is facing. Health and economic threats caused governments to take preventive measures against the spread of the disease. This study aims to provide a correlation analysis of the response measures adopted by countries and epidemic trends since the COVID‐19 outbreak. This analysis picks 13 countries for quantitative assessment. We select a trusted model to fit the epidemic trend curves in segments and catch the characteristics based on which we explore the key factors of COVID‐19 spread. This review generates a score table of government response measures according to the Likert scale. We use the Delphi method to obtain expert judgments about the government response in the Likert scale. Furthermore, we find a significant negative correlation between the epidemic trend characteristics and the government response measure scores given by experts through correlation analysis. More stringent government response measures correlate with fewer infections and fewer waves in the infection curves. Stringent government response measures curb the spread of COVID‐19, limit the number of total infectious cases, and reduce the time to peak of total cases. The clusters of the results categorize the countries into two specific groups. This study will improve our understanding of the prevention of COVID‐19 spread and government response.