The aim of this research is to determine the incidence of socioeconomic variables in migration flows from the main countries of origin that form part of the international South-North migration corridor, such as Mexico, China, India, and the Philippines, during the 1990–2022 period. The independent variables considered are GDP per capita, unemployment, poverty, higher education, and public health, while the dependent variable is migration flows. An econometric panel data model is implemented. The tests conducted indicate that all variables have an integration order of I (1) and exhibit long-term equilibrium. The econometric models used, Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS), reveal that unemployment and poverty had the strongest influence on migration flows. In both models, within this international migration corridor, GDP per capita, higher education, and health follow in order of importance.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

The significant climate change the planet has faced in recent decades has prompted global leaders, policymakers, business leaders, environmentalists, academics, and scientists from around the world to unite their efforts since 1987 around sustainable development. This development not only promotes economic sustainability but also environmental, social, and corporate sustainability, where clean production, responsible consumption, and sustainable infrastructures prevail. In this context, the present article aims to propose a development framework for sustainability in food sector SMEs, which includes Life Cycle Assessment (LCA) and the integration of Environmental, Social, and Governance (ESG) strategies as key elements to reduce CO₂ emissions and improve operational efficiency. The methodology includes a comparative analysis of strategies implemented between 2019 and 2023, supported by quantitative data showing a 20% reduction in operating costs, a 10% increase in market share, and a 25% increase in productivity for companies that adopted clean technologies. This study offers a significant contribution to the field of corporate sustainability, providing a model that is adaptable and applicable across different regions, enhancing innovation and business resilience in a global context that requires collective efforts to achieve the sustainable development goals.

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

The present study, developed under a quantitative approach, explanatory scope and causal correlational design, aims to determine the influence of invisible learning on the research competence of high school students in two private schools in the city of Lima, Peru, whose educational models seek to develop autonomous learning and research through discovery learning and experimentation. Two questionnaires were applied to 120 students of the VII cycle of basic education, one to measure the perception regarding invisible learning with 20 items and the other to measure investigative competencies with 21 items; both instruments underwent the corresponding validity and reliability tests before their application. Among the main findings, descriptive results were obtained at a medium level for both variables, the correlations found were significant and moderate, and as for influence, the coefficient of determination R² yielded a value of 0.13, suggesting that 13% of investigative competence is predicted by invisible learning. These results show that autonomy, the use of digital technologies, metacognition and other aspects that are part of invisible learning prepare students to solve problems of varying complexity, allowing them to face the challenges of contemporary knowledge in an innovative and effective manner.