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Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best-worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts' opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.

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Tianyu Tan,1 Chao Zhou,1 Rongrong Lu,1 Cheng Chen,2 Chongyang Bai,1 Long Li,1 Guohui Wu1 1Chongqing Center for Disease Control and Prevention, Chongqing, 400042, People’s Republic of China; 2Chongqing Jiulongpo District Center for Disease Control and Prevention, Chongqing, 400039, People’s Republic of ChinaCorrespondence: Guohui Wu, Chongqing Center for Disease Control and Prevention, No. 8, Changjiang 2nd Road, Chongqing, 400042, People’s Republic of China, Tel +8602368801491, Email wgh68803652@163.comPurpose: This study aimed to assess the proportion of depression and its associated factors among men living with HIV/AIDS and aged 50 and over in Chongqing, China.Methods: This cross-sectional study included 774 men diagnosed as HIV/AIDS and aged 50 and over in four regions. Data were collected through face-to-face interviews with a structured questionnaire. Multivariate logistic regression analysis was performed to identify factors associated with depression. A two-tailed P-value less than 0.05 was considered as statistical significance.Results: A total of 293 participants (37.9%) reported depressive symptoms. Results from multivariate logistic regression suggested that individuals aged ≥ 70 (AOR = 1.99, 95% CI: 1.28– 3.08) and those living in rural areas (AOR = 2.79, 95% CI: 1.96– 3.97) were associated with higher odds of depression, while those being employed (AOR = 0.50, 95% CI: 0.32– 0.80) and with monthly income > 3000 CNY (AOR = 0.47, 95% CI: 0.30– 3.74) had lower odds of depression.Conclusion: Our findings indicate a high prevalence of depression among older men living with HIV/AIDS in Chongqing, China. Age, residence, employment, and income have been identified as potential risk factors of depressive symptoms. Mental-health intervention initiatives should be tailored to target adults with a higher risk of depression.Keywords: depression, China, older male, HIV/AIDS, influencing factors

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As COVID-19 develops, dynamic changes occur in the patient’s immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.

Inclusive finance is a financial system that can effectively and comprehensively provide services to all social classes and groups. Design an inclusive financial economic impact system based on feature extraction and data fusion. Based on the perspective of factor market, product market and financial market, the system structure is designed based on the level of information fusion, and the system is divided into data layer, feature layer and decision layer. Collect different information from the sample data of 31 provinces across the country and perform information fusion to establish a regression model to maximize the information retention rate. Explore the influence of the three market development degrees and the development level of inclusive finance on the high-quality development of the regional economy, and improve the robustness of information feature fusion. The results show that the development level of inclusive finance and the development degree of the three markets have a significant role in promoting the high-quality development of the regional economy. Inclusive finance can promote the high-quality development of the regional economy by optimizing the development of the "three markets". &nbsp

High-speed dry grinding has the characteristics of high processing efficiency and clean environment. The high-speed dry grinding method meets the requirements of the green and efficient development of the national manufacturing industry. However, inappropriate cutting parameters seriously affect the surface quality of the workpiece and cause the workpiece to be scrapped. Therefore, this paper proposed an optimization method based on the combination of the improved extreme learning machine neural network (ELM) for high-speed dry milling surface roughness prediction model and genetic algorithm (GA). The Taguchi orthogonal experiment results show that the surface roughness of high-speed dry milling can be accurately predicted by the improved ELM and thereafter the optimal cutting parameter combination can be determined by GA.

Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users' increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3% to 28.6%, and rebuffering time is limited within 0.8s on average, which is reduced by 53.5% to 92.3%.

Traveling waves in a reaction-diffusion system are essential for long-range communication in living organisms and inspire biomimetic materials of similar capabilities. One recent example is the traveling motion waves among photochemically oscillating, silver (Ag)-containing colloids. Being able to manipulate these colloidal waves holds the key for potential applications. Here, we have discovered that these motion waves can be confined by light patterns and that the chemical clocks of silver particles are moved forward by reducing local light intensity. Using these discoveries as design principles, we have applied structured light technology for the precise and programmable control of colloidal motion waves, including their origins, propagation directions, paths, shapes, annihilation, frequency, and speeds. We have also used the controlled propagation of colloidal waves to guide chemical messages along a predefined path to activate a population of micromotors located far from the signal. Our demonstrated capabilities in manipulating colloidal waves in space and time offer physical insights on their operation and expand their usefulness in the fundamental study of reaction-diffusion processes. Moreover, our findings inspire biomimetic strategies for the directional transport of mass, energy, and information at micro- or even nanoscales.

Arsenic, a widely existing environmental contaminant, is recognized to be toxic to multiple organs. Exposure to arsenic results in liver damage via excessive production of reactive oxidative species (ROS). PIN1 regulates the levels of ROS. N-acetyl-L-cysteine (NAC) is an ROS scavenger that protects the hepatic functions. Whether PIN1 plays a regulatory role in NAC-mediated antagonism against arsenic hepatotoxicity remains largely unknown. In our study, the protective effects of NAC against arsenic (NaAsO2)-induced hepatotoxicity were evaluated in vitro and in vivo. Arsenic exposure induced cytotoxicity by increasing the intracellular ROS production, impairing mitochondrial function and inducing apoptosis in L02 hepatocytes. Overexpression of PIN1 markedly protected against arsenic cytotoxicity, decreased ROS levels, and mitigated mitochondrial dysfunction and apoptosis in L02 cells. However, loss of PIN1 further aggravated arsenic-induced cytotoxicity and abolished the protective effects of NAC in L02 cells. An in vivo study showed that pretreatment with NAC rescued arsenic-induced liver injury by restoring liver function and suppressing hepatic oxidative stress. Overexpression of PIN1 in mice transfected with AAV-Pin1 relieved arsenic-induced liver dysfunction and hepatic oxidative stress. Taken together, our study identified PIN1 as a novel intervention target for antagonizing arsenic-induced hepatotoxicity, highlighting a new pharmacological mechanism of NAC targeting PIN1 in antagonism against arsenic toxicity.