The Republic of Moldova is a state with a small, but dynamic economy and which, with the help of competitiveness in the IT industry, is looking for a place on the economic market in the Eastern European region. The research approaches this topic from an economic, historical, but also geopolitical point of view. This analysis of economic data and figures from the last period, combined with government policies and that of the National Bank of Moldova, means that in the near future the software economic area of Moldova will become an important regional player in this part of Europe.

This study meticulously explores the crucial elements precipitating corporate failures in Taiwan during the decade from 1999 to 2009. It proposes a new methodology, combining ANOVA and tuning the parameters of the classification so that its functional form describes the data best. Our analysis reveals the ten paramount factors, including Return on Capital ROA(C) before interest and depreciation, debt ratio percentage, consistent EPS across the last four seasons, Retained Earnings to Total Assets, Working Capital to Total Assets, dependency on borrowing, ratio of Current Liability to Assets, Net Value Per Share (B), the ratio of Working Capital to Equity, and the Liability-Assets Flag. This dual approach enables a more precise identification of the most instrumental variables in leading Taiwanese firms to bankruptcy based only on financial rather than including corporate governance variable. By employing a classification methodology adept at addressing class imbalance, we substantiate the significant influence these factors had on the incidence of bankruptcy among Taiwanese companies that rely solely on financial parameters. Thus, our methodology streamlines variable selection from 95 to 10 critical factors, improving bankruptcy prediction accuracy and outperforming Liang's 2016 results.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers' capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

The health of employees is so paramount for employee productivity. While emphasis is often placed on the physical health of employees, less emphasis is placed on the psychological or mental health of the employees. Similarly, it seems as if health challenges are more occurring in manufacturing industries, but the service organizations employees are as well susceptible to mental health challenges. Understanding the predictive factors to mental health challenges therefore becomes imperative. It is on this note that the present research examines how employee mental health is predicted by work safety measures like perceived workplace safety, work overload and pay satisfaction. The workplace safety variables include perception of job, co-worker, supervisor, management, and safety programs. A cross sectional survey method was adopted, using ex-post-facto research design. Data were gathered from 258 employees, including 150 (58.1%) females and 108 (41.9%) males of a non-governmental organization. Correlation and regression analyses were used to analyze data obtained from the standardized psychological scales that were administered. The results showed that mental health correlated positively with perceived job safety, but negatively with perceived co-worker, supervisor, management, safety programs and pay satisfaction. Workplace safety variables jointly predicted mental health, accounting for 23% variance, but only perceived job safety and supervisor safety were significant. The higher employees perceived job safety, the lower their mental health challenges. Similarly, the higher they perceived supervisor safety, the lower their mental health issues. Pay satisfaction accounted for 3% variance in mental health, and the higher the pay satisfaction, the lower the level of employee mental health issues. It is implied that the human resource unit of service organizations should intermittently examine their organizations to identify and prevent possible job and supervisor safety threats. Supervisors should be trained on how to be discrete in communicating safety measures to subordinates so that it will not boomerang to hamper mental health. The human resources unit should also intermittently organize workshop, training, and employee-assisted programs for younger and lower grade employees on adaptive mechanisms for reducing mental health challenges.