This study addresses the crucial question of the macroeconomic impact of investing in railroad infrastructure in Portugal. The aim is to shed light on the immediate and long-term effects of such investments on economic output, employment, and private investment, specifically focusing on interindustry variations. We employ a Vector Autoregressive (VAR) model and utilize industry-level data to estimate elasticities and marginal products on these three economic indicators. Our findings reveal a compelling positive long-term spillover effect of these investments. Specifically, every €1 million in capital spending results in a €20.84 million increase in GDP, a €17.78 million boost in private investment, and 72 new net permanent jobs. However, these gains are not immediate, as only 14.5% of the output increase and 38.8% of the investment surge occur in the first year. In contrast, job creation is nearly instantaneous, with 93% of new jobs materializing within the first year. A short-term negative impact on the trade balance is expected as new capital goods are imported. Upon industry-level analysis, the most pronounced output increases are witnessed in the real estate, construction, and wholesale and retail trade industries. The most substantial net job creation occurs in the construction, professional services, and hospitality industries. This study enriches the empirical literature by uncovering industry-specific impacts and temporal macroeconomic effects of railroad infrastructure investments. This underscores their dual advantage in bolstering long-term economic performance and counteracting job losses during downturns, thus offering valuable public policy implications. Notably, these benefits are not evenly distributed across all industries, necessitating strategic sectoral planning and awareness of employment agencies to optimize spending programs and adapt to industry shifts.

This work evaluates the physical and physical-chemical parameters of the strawberry variety “Festival”, obtained in the soil and climate conditions of Humpata, Huila Province, Angola, following the transformation into sweet of adequate quality. The analyses made were: the mass determined on an analytical balance and the transversal and longitudinal diameters with a pachymeter. Other analyses were: total titratable acidity by volumetry, pH by potentiometry, total soluble solids by refractometry, moisture and ash by gravimetry. The study showed that the pH of the pulp was 3.41; and in the candy it was 3.31. The titratable acidity in the strawberry pulp had a value of 0.186 g/100 mL and in the jam 0.096 g/100 mL; the ascorbic acid content in the pulp was 18.60 mg∕100 g. The average soluble solids content in the pulp was 9.51 °Brix and for the jam 68.83 °Brix. These chemical characteristics of the pulp and jam provide information about their nutritional values.

On the basis of the enlightenment of international engineering education accreditation for the reform and development of higher education in China, combined with the important measures of the national “double first-class” construction, new challenges have been proposed for innovative talent cultivation among engineering majors in the context of promoting national development. These challenges also promote the reform of science-oriented courses among engineering majors. As a core mandatory course for engineering majors, biochemistry plays a crucial role in the entire educational process at universities, serving as a bridge between basic and specialized courses. To address challenges such as limited course resources, insufficient development of students’ advanced thinking and innovation skills, and overly standardized assessment methods, the bioengineering major from Guilin University of Technology restructured the biochemistry course content. A blended teaching model termed “three integrations, three stages, one sharing”, was implemented. This effort has yielded significant results, providing a research foundation for constructing an innovative talent cultivation system that is oriented toward industry needs within modern industrial colleges. It also offers valuable insights into and reference points for the cultivation of engineering talents and curriculum reform in local universities.

This quantitative survey was non-experimental and had two goals. An evaluation of predictor variables of empowerment, motivation, teamwork, interpersonal skills, and training and development in project environments was one goal to help explain the industry’s high project failure rate. Second, this research tested Bandura’s social learning theory and tested the hypothesis that empowerment and motivation boost performance. Using a survey-based questionnaire, the data was collected from 212 employees working in different IT companies in Pakistan. The results revealed that empowerment, motivation, teamwork, and training and development have a significant impact on project performance. Using the results, this study proposes theoretical implications for the researchers and managerial implications for the organizations.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.