The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day⁻¹ are generated in 2016, 4.37 Tn/day⁻¹ in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m⁻³ in 2016 and 172.69 kg/m⁻³ in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

Management and efficiency have a fundamental impact on the performance of public hospitals, as well as on their philanthropic mission. Various studies have shown that the financial weaknesses of these entities affect the planning, setting of goals and objectives, monitoring, evaluation and feedback necessary to improve health systems and guarantee accessibility as an inalienable right. This study aims to analyze the management and efficiency of third-level and/or high-complexity hospitals in Colombia, through a statistical model that uses financial analysis and key performance indicators (KPIs) such as ROA, ROE and EBITDA. A non-experimental cross-sectional design is used, with an analytical-synthetic, documentary, exploratory and descriptive approach. The results show financial deficiencies in the hospitals evaluated; hence it is recommended to make adjustments in the operating cycle to increase efficiency rates. In addition, the use of the KPIs ROA and ROE under adjusted models is suggested for a more precise analysis of the financial ratios, since these adequately explain the variability of each indicator and are appropriate to evaluate hospital management and efficiency, but not in EBITDA ratio, hence the latter is not recommended to evaluate hospital efficiency reliably. This study provides relevant information for public health policy makers, hospital managers and researchers, in order to promote the efficiency and improvement of health services.

The aim of this paper is to introduce a research project dedicated to identifying gaps in green skills by using the labor market intelligence. Labor Market Intelligence (LMI). The method is primarily descriptive and conceptual, as the authors of this paper intend to develop a theoretical background and justify the planned research using Natural Language Processing (NLP) techniques. This research highlights the role of LMI as a tool for analysis of the green skills gaps and related imbalances. Due to the growing demand for eco-friendly solutions, there arises a need for the identification of green skills. As societies shift towards eco-friendly economic models, changes lead to emerging skill gaps. This study provides an alternative approach for identification of these gaps based on analysis of online job vacancies and online profiles of job seekers. These gaps are contextualized within roles that businesses find difficult to fill due to a lack of requisite green skills. The idea of skill intelligence is to blend various sources of information in order to overcome the information gap related to the identification of supply side factors, demand side factors and their interactions. The outcomes emphasize the urgency of policy interventions, especially in anticipating roles emerging from the green transition, necessitating educational reforms. As the green movement redefines the economy, proactive strategies to bridge green skill gaps are essential. This research offers a blueprint for policymakers and educators to bolster the workforce in readiness for a sustainable future. This article proposes a solution to the quantitative and qualitative mismatches in the green labor market.

The high unemployment rate among university graduates is prompting universities to enhance the business skills of their students. This research aims to holistically explain the role of university support and entrepreneurial resilience in increasing students’ business innovation capabilities. To analyze phenomena and relationships between variables, a quantitative approach using partial least square structural equation modeling (PLS-SEM) was used. This research sample involved 165 student entrepreneurs who are members of the student entrepreneur community in Indonesia. Knowledge management does not significantly impact increasing business innovation capabilities. However, perceived university support and entrepreneurial resilience have been shown to significantly impact business innovation capabilities and strengthen the influence of knowledge management activities on increasing business innovation capabilities. Universities must create policies supporting extracurricular entrepreneurship programs, focusing on building entrepreneurial resilience. This can be achieved through workshops and business incubator initiatives involving partnerships with industry and the entrepreneurial community. This research provides a new perspective in analyzing higher education entrepreneurship education through a more in-depth explanation of the extracurricular activities of the student business community to build business innovation capabilities based on knowledge, institutional, and trait theory perspectives.

This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.