Climate change has adverse effects on ecosystems and several socio-economic sectors including health. Indeed, infrastructure, continuity of medical services, and the hospital environment are all directly affected by the effects of climate-related risks. This study aims to describe the observations of the effects of climate change risks on health systems in the Greater Lomé health region of Togo. We used an interview guide and a questionnaire to collect information. The observations allowed us to assess the effects caused by climate risks. According to the results, 84.62% of respondents attest that health centers experience flooding during rainy periods and damage caused by strong winds is noticeable among 76.92% of respondents. More than 25.40% and 61.86% respectively of respondents mention that droughts and floods have effects on health systems. The results of this study will allow health system managers to become aware of how to plan useful actions to facilitate the management of climate-related risks in health facilities in the Greater Lomé health region. In view of all these results, it is necessary that measures be taken to strengthen the resilience of health systems through awareness campaigns and training of actors throughout the health pyramid.

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

This study intends to explore the idea of a vocational village strategy to foster sustainable rural development. Vocational villages, offering targeted skills training and economic opportunities, present a compelling soft approach to rural development, addressing the need for sustainable livelihoods and community empowerment. Drawing upon the collaborative governance (the penta-helix model); underpinning the social capital perspective; and highlighting the economic, institutional, cultural, environmental, technological, and institutional dimensions of sustainable development, a vocational village strategy is expected to level up village capacities and facilitate modernization. The research was narratively developed through a qualitative methodology using primary and secondary data sources. Primary empirical data was employed to analyze vocational village practices in Panggungharjo Village, Yogyakarta, Indonesia as a representative example. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) framework provided secondary data to present comparative literature on vocational village development. The findings determined a four-staged vocational village model includes initiation, training, business development, and independence. The success of this model is contingent upon political, bureaucratic, and sociocultural factors (social capital), as well as the effective collaboration of government, academia, industry, and community (penta-helix). This research contributes to the urgency of vocational village practices and models as a viable strategy for achieving equitable and sustainable rural development.

Managing business development related to the innovation of intelligent supply chains is an important task for many companies in the modern world. The study of management mechanisms, their content and interrelations of elements contributes to the optimization of business processes and improvement of efficiency. This article examines the experience of China in the context of the implementation of intelligent supply chains. The study uses the methods of thematic search and systematic literature review. The purpose of the article is to analyze current views on intelligent supply chain management and identify effective business management practices in this area. The analysis included publications devoted to various aspects of supply chain management, innovation, and the implementation of digital technologies. The main findings of the article are as follows: Firstly, the key elements of intelligent supply chain management mechanisms are identified, secondly, successful experiences are summarized and the main challenges that companies face in their implementation are identified. In addition, the article focuses on the gaps in research related to the analysis of successful experiences and the reasons for achieving them.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.