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.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Africa has an extensive and varied cultural history that includes works of art, music, literature, customs, and historical locations. These cultural resources are essential for creating identities, promoting social cohesiveness, and advancing economic development. However, for these institutions to have the greatest impact on the world and contribute to sustainable development, they must be managed and engaged effectively. Exploring the management of cultural institutions in Africa and their potential for global impact and sustainable development is the goal of this research study. The study relies on the extensive review of available literature, case studies, and in-depth interviews with key informants, and data obtained, subjected to content and thematic analyses. It aims to uncover flexible management techniques that can improve the global reach and sustainable development of African cultural institutions by examining successful models and cutting-edge approaches. The results of this study will help those responsible for administering Africa’s cultural institutions to formulate practical guidelines and policy recommendations. Africa can further establish its cultural identity, advance cultural diplomacy, and utilize its cultural capital to propel social and economic advancement by utilizing the potential of these institutions for global impact and sustainable development.

No less than 60% of timber production in Peru’s natural forests is the result of informal or illegal extractive activities that, by definition, are not sustainable. This article aims to demonstrate that even legitimate timber, such as timber harvested in more than 6 million hectares of forest concessions, does not meet the basic requirements of sustainable forest management. Forestry legislation itself, which does not emphasize forest management, institutional weaknesses and the socioeconomic environment are the main causes. In addition, the cutting cycles and the authorized minimum diameters, among other practices, do not allow the renewal of the resource and increase its degradation.

In the current competitive global marketplace, innovation is key for high-tech firms to thrive. Open innovation offers a promising approach, but its effectiveness remains unclear. Therefore, this research explored the connection between open innovation, knowledge management capability, and innovation performance within high-tech firms. We used a mediation approach to highlight the central role of knowledge management capability in the relationship between open innovation and innovation performance. We used a survey questionnaire approach to collect data from the 462 employees of high-tech firms on open innovation, knowledge management capability, and innovation performance using a convenient sampling technique. We used partial least square structural equations modeling through PLS-SEM statistics. Results indicated that open innovation has a direct, positive and significant connection with innovation performance. Similarly, the current research serves as a pioneering exploration into mediation analysis, highlighting the mediating role of knowledge management capability that influences the relationship between open innovation and innovation performance. Empirical studies offer valuable insights for leaders of high-tech firms, guiding them to identify effective knowledge management practices and determine the ideal extent of open innovation to boost innovation performance. The current study reveals novel insights into the benefits of knowledge management capability in enhancing open innovation efforts within firms. This research provides valuable implications and future research directions.