In wealthy nations, biofuel usage has grown in importance as a means of addressing climate change concerns, ensuring energy security, and promoting agricultural development. Because they understand the potential advantages of biofuel for rural development and job creation, governments have created policies and legislation to encourage the production of biofuel. However, the province of Limpopo hasn’t fully taken advantage of the potential to use biofuel production as a vehicle for job development, despite a higher demand for the fuel. There is currently a lack of understanding of the role of biofuel in promoting local development in developing regions. For this reason, this study made use of semi-structured interviews to explore how biofuel production can be used as an instrument for Local Economic Development (LED) in the Limpopo province of South Africa. The research investigated the determinants of empowerment that could impact the commercial feasibility of biofuel production in the province. It also identified the need for human resource development to get workers ready for jobs in Limpopo’s biofuel sector. The results showed that, provided certain conditions were met, the production of biofuel in Limpopo may be a useful instrument for creating local jobs. By highlighting the potential for job creation and the importance of human resource development, this research aims to facilitate evidence-based decision-making that can harness biofuel production for sustainable rural development in the region. The value of this study lies in its contribution to the understanding of biofuel’s role in LED, offering actionable insights for policymakers and stakeholders in Limpopo. 

Desert environments face the challenge of promoting sustainable tourism while balancing economic growth with cultural and environmental preservation. In the context of rapid global tourism expansion, effective destination management becomes crucial for positive economic impact and long-term preservation. This study aims to identify key factors influencing the sustainability of tourism. It explores the interactions between local stakeholders, the supply of tourism products and services, and tourism governance. Utilizing structural equation modeling through the PLS-SEM method, data was collected from 150 stakeholders in desert environments. The findings reveal that the involvement of local tourism stakeholders and the supply of tourism products and services significantly impact sustainable tourism in the desert environment. However, we observe a lack of influence between tourism governance and sustainable desert tourism. The novelty of the study lies in the identification of promotional factors for sustainable desert tourism. The originality of this study lies in its in-depth exploration of the mechanisms for promoting sustainable tourism.

This study examined the labor regulations regarding the hours of work and rest for representative fishing countries (Norway) by the International Labor Organization (ILO) Convention C188—Work in Fishing, 2007. A dual comparative analysis with Norway is used to explore policy implications for the representation and protection of fishers’ labor standards in Korea. This study examined the possibility of synchronisation between national and international legislation on the hours of work and rest for fishers, with a particular focus on the Norwegian case. The objective is to identify policy enhancements related to the Korean Seafarers Act. This study looked in depth at the fatigue and well-being problems faced by Korean fishers working long times on various vessels. It is based on the results of a qualitative comparative study. To achieve the objectives, We proposed to ‘the name of the fishing vessel’, which are excluded from the protections afforded by the Seafarers Act and to clarify the regulations regarding the labor standards for them. This proposal will provide compensation and protection for Korean fishers’ labor rights. It aims to enhance labor conditions in line with ILO standards, harmonize national and international agreements to protect small-scale fisheries and contribute to the development of environmentally friendly propulsion technologies, such as hydrogen-fueled electric hybrids and LPG (Liquefied Petroleum Gas).

Madura Island, with more than half of its population, are women encountering socio-economic problems, which eventually create high poverty and unemployment rates. However, the Madurese are also well-known for their resiliency and entrepreneurial characteristics. The effort to solve the issues by empowering the community, women in particular, has been taken seriously primarily by entrepreneurs who were born and raised in the community. Therefore, this research aims to gain insight into the current Madurese entrepreneur’s business pattern and their social concerns in order to propose a strategy to increase productivity as an effort to empower women’s communities. The methodology is qualitative research, which collects data using semi-structured interviews with representatives of the Madurese entrepreneurs in four areas of Madura Island. Their responses are then transcripted and coded for content analysis based on the designed themes. The result shows that they recognise and practise the social entrepreneurship (SE) pattern, although they do not understand the term. Subsequently, the technological application for business operations in general is still limited to the usage of digital technology (DT) for marketing and transaction activities, which helps increase business performance or productivity. Hence, the initiation of technosociopreneurship as a strategy to further develop SE activities with the hope of increasing productivity in empowering women’s communities is proposed. Further research development is advised using quantitative methods for generalisable findings.

Based on digital technology, the digital economy has typical characteristics of high efficiency, greenness, intelligence, innovation, strong penetration and so on, which can promote the sporting goods manufacturing industry (SGMI) to realize the goal of green development. This study selects panel data from 30 provinces in China over the period of 2011 to 2022. And the green total factor productivity of the sporting goods manufacturing industry (SGTFP) is used to reflect the green development of SGMI. The level of digital economy development (DIG) and the SGTFP are measured by using the entropy method and the Super-SBM model with undesirable outputs. Based on the method of coupling coordination degree model, the coordinated development degree of DIG and SGTFP is analyzed first. Then, by making use of the fixed effect model, intermediary effect model and spatial Durbin model, the influence of DIG on the green development of SGMI and its mechanism are empirically studied. The results show that DIG, SGTFP and the degree of their coupling and coordination are generally on the rise. The benchmark regression results show that the coefficient of DIG on SGTFP is 0.213; that is, the digital economy can significantly promote the improvement of green development in SGMI. According to the analysis of the spatial Durbin model, the impact of the digital economy on SGTFP has a certain spatial spillover, that is, the development of digital economy in the region will have a certain promoting effect on the green development of SGMI in the surrounding region. The intermediary effect model analyzes the influence mechanism and finds that the digital economy mainly boosts SGTFP through green innovation technology and energy consumption structure.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.