This study employs logistic regression to investigate determinants influencing active living among elderly individuals, with “Active Living” (1 = Active, 0 = Inactive) as the dependent variable. Analysing data from 500 participants, findings reveal significant associations between active living and variables such as chronic conditions (OR = 0.29, p < 0.001), mental well-being (OR = 1.57, p < 0.001), social support (OR = 5.75, p < 0.001), access to parks/recreational facilities (OR = 2.59, p < 0.001), income levels (OR = 1.82, p = 0.003), cultural attitudes (OR = 2.72, p < 0.001), and self-efficacy (OR = 2.01, p < 0.001). These findings highlight the complex interplay of factors influencing active living among elderly populations. Recommendations include implementing targeted interventions to manage chronic conditions, enhance mental well-being, strengthen social networks, improve access to recreational spaces, provide economic support for fitness activities, promote positive cultural attitudes towards aging, and empower older adults through self-efficacy programs. Such interventions are crucial for promoting healthier aging and fostering sustained engagement in physical activity among older adults.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Infrastructure decision-making has traditionally been focused on the use of cost-benefit analysis (CBA) and multicriteria decision analysis (MCDA). Nevertheless, there remains no consensus in the infrastructure sector regarding a favored approach that comprehensively integrates resilience principles with those tools. This review focuses on how resilience has been evaluated in infrastructure projects. Initially, 400 papers were sourced from Web of Science and Scopus. After a preliminary review, 103 papers were selected, and ultimately, the focus was narrowed down to 56 papers. The primary aim was to uncover limitations in both CBA and MCDA, exploring various strategies for amalgamating them and enhancing their potential to foster resilience, sustainability, and other infrastructure performance aspects. Results were classified based on different rationalities: i) objectivist, ii) conformist, iii) adjustive, and iv) reflexive. The analysis revealed that while both CBA and MCDA contribute to decision-making, their perceived strengths and weaknesses differ depending on the chosen rationality. Nonetheless, embracing a broader perspective, fostering participatory methods, and potentially integrating both approaches seem to offer more promising avenues for assessing the resilience of infrastructures. The goal of this research proposal is to devise an integrated approach for evaluating the long-term sustainability and resilience of infrastructure projects and constructed assets.

The article examines the issues of application and improvement of the methodology for evaluating industrial enterprises as recipients of state support within the framework of the implementation of industrial policy. The authors considered approaches to the content of industrial policy, investigated the factors influencing its efficiency, identified aspects of its imperfections that arise when applying an incomplete list of important parameters of economic development and ambiguity in the interpretation of previously applied estimates. The article presents proposals to improve the methodology for assessing potential recipients of state support based on the development of a comprehensive indicator for assessing enterprises (recipients of support), taking into account not only the classical parameters of the economic efficiency of industrial enterprises applying for state financial assistance, but also such aspects as the development of budgetary funds, belonging to priority sectors of the economy, characteristics of sustainable development and export and innovation potential. Combining the results of a comprehensive assessment of the recipient of state support with a map of the business demography of the territory allows making a decision not only about the fact of support and its efficiency, but also to predict the assessment of the life cycle of the enterprise and its subsequent development.

This study explores the dynamic relationship between ethical human resources management (HRM) strategies, the level of commitment an employee feels towards their organization, and their job performance, paying particular attention to how employees’ perceptions of the support they receive from their organization can influence these interactions, especially during challenging times. Drawing on a sample of full-time non-executive Indonesian employees, the research employs descriptive statistics for initial data analysis, followed by structural equation modeling (SEM) to test the proposed hypotheses rigorously. The investigation reveals a positive relationship between ethical HRM and employee performance (EP) and organizational commitment (OC). Additionally, OC emerges as a pivotal mediator in the ethical HRM-EP link. Notably, employees’ organizational support perception (EOSP), often assumed to enhance positive organizational outcomes, displays a surprising negative moderating effect when combined with OC, suggesting a more intricate relationship than traditionally posited. These findings enhance our comprehension of how ethical HRM practices function in times of crisis, questioning conventional beliefs regarding the influence of organizational support. The study’s methodological approach, combining descriptive and advanced statistical analyses, provides a robust framework for understanding these complex relationships. This research holds significant implications for HRM practices, particularly in crisis response and management, indicating a need for nuanced support strategies that reflect the complexity of employee-organization dynamics.

Research has shown that understanding the fundamental of public support for carbon emission reduction policies may undermine policy formulation and implementation, yet the direction of influence and the transmission mechanism remain unclear. Using data from using data from 1482 questionnaires conducted in Hangzhou, China, this paper has examined a comprehensive model of the factors and paths influencing public support for carbon emission reduction policies, and evaluated the determinants and predictors of policy support regarding individual psychological perceptions, social-contextual perceptions, and perceptions of policy features. The results show that the variables in both the individual psychological perception and social contextual perception dimensions have no significant effect on carbon tax, however, be important constructure in carbon trading; in the policy characteristics perception dimension, both variables have a significant positive effect on both carbon tax and carbon trading, and are also the strongest predictors of policy support for carbon policies. Further evidence suggests that future policies could be more acceptable to residents by strengthening their environmental values, social norms can further arouse residents’ social responsibility to care about climate, and whether the policy is effective or fair to help residents realize the importance of the policy as well as the need for their participation and willingness to dedicate themselves to the mitigation of climate change.