Using a newly-developed data set for Portugal, we analyze the industry-level effects of infrastructure investment. Focusing on the divide between traded and non-traded industries, we find that infrastructure investments have a non-traded bias, as these shift the industry mix towards private and public services. We also find that the industries that benefit the most in relative terms are all non-traded: construction, trade, and real estate, among the private services, and education and health, among the public services. Similarly, emerging trading sectors, such as hospitality and professional services, stand to gain. The positive impacts on traded industries are too small to make a difference. These results highlight that infrastructure-based strategies are not neutral in terms of the industry mix. Moreover, with most of the benefits accruing to non-traded industries, such a development model that is heavily based on domestic demand may be unsustainable in light of Portugal’s current foreign account position. 

Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.

Firms, recognizing their Corporate Social Responsibility (CSR), are becoming catalysts for societal change by integrating Environmental, Social and Governance (ESG) criteria into their activities. The fashion industry exemplifies this effort, with an increasing number of companies embracing sustainability and ethical practices. In this context, our purpose is to provide a clear and comprehensive picture of the link between sustainability and business performance in the fashion industry. This work presents a Multivariate Regression Analysis, scrutinizing both external perspectives through stock prices and internal perspectives via profitability indices. Our aim is to discern the intricate relationship between sustainability practices and financial performance within the fashion industry, aligning ESG criteria with long-term economic success. Our regression analysis reveals a significant positive correlation between ESG scores and stock prices, indicating investor recognition of ESG performance as a crucial investment criterion. However, when focusing internally on profitability, the ESG score does not exhibit statistical significance, suggesting a yet-to-be-established connection between ESG policies and corporate profitability. This study underscores the evolving role of companies as sustainability promoters, emphasizing the crucial role of ESG performance in shaping investor perceptions. Nevertheless, it also highlights the need for further exploration into the intricate relationship between sustainable policies and corporate profitability. As businesses increasingly embrace sustainability, in fact, it could become paramount for informed decision-making and fostering ethical societal and environmental progress.

This article explores the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework in the context of integrating self-driving tractors into agricultural practices. With a focus on understanding the factors influencing the acceptance and adoption of this transformative technology, we delve into the implications for farmers, industry stakeholders, and the future of sustainable agriculture and rural tourism.

The main objective of this study was comparative advantages analysis at social price of Num-mango in the export channels. The examination of the domestic resource cost per shadow exchange rate (DRC/SER) ratio provides insights into the comparative advantage of the trading system in the Num-mango industry. A comprehensive study was conducted, with a total of 317 observations, with a specific emphasis on the significant individuals in Vinh Long, Vietnam. The comparative advantage of the Num-mango commerce system was inferred from a DRC/SER ratio below one, which may be attributed to the existence of two distinct export channels. The DRC/SER in export channel 1 exhibited values of 0.55, 0.67, and 0.53 over the three seasons. In season 1, export channel 2 had a score of 0.42, which then was 0.79 in season 2. The value of export channel 2 had a consistent upward trend during season 3, reaching its highest point of 0.3. It is recommended that regulators and governments provide export-focused incentives that prioritize the maximum comparative advantage. This study examines the concept of comparative advantage within export supply chains, specifically in relation to a diverse selection of tropical fruits and vegetables. Furthermore, it provides empirical evidence that supports the applicability and reliability of the Ricardian model.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.