This study presents the results of fracture toughness tests conducted on specimens obtained by additive manufacturing techniques, specifically using low-force stereolithography. The samples were manufactured from a transparent 3D printing material for biocompatible applications, the so-called BioMed Durable Resin, which is a Formlabs-patented polymer material that simulates the strength and rigidity of polyethylene. The selected toughness tests in this context were performed following the ASTM D5045-99 guidelines. All tests were conducted under controlled laboratory conditions at 23 °C and 50% relative humidity, ensuring adherence to the standard and the replicability of the experimental results. To investigate the influence of printing plane orientation, specimens were produced at three printing orientation angles (0, 45, and 90 degrees). These angles were selected to provide a comprehensive evaluation of the anisotropy effects in the material. They cover both extreme orientations (0° and 90°) and include an intermediate value (45°), allowing us to assess variations in mechanical behavior across a representative range of printing orientations, consistent with prior research in the field. The experimental tests yielded data on the crack resistance and energy release rate for each angle of orientation. There are various implications of the findings, beyond materials engineering, for applications in biomedicine. Indeed, this same approach opens the door to new research methods for manufacturing certified biocompatible materials from such durable resins. Finally, complementary issues such as related medical applications have been slightly addressed for future work, since biomedicine innovation clusters can contribute to accelerating growth in this crucial field for productive sector activity and the local business environment.

The promotion of efficient and sustainable means of transport has been a key issue of public debate across the European Union (EU). Particularly, transport needs in peripheral and remote areas have a great importance for EU transport policies in order to ensure the mobility of goods and persons in these regions, and thus to achieve greater territorial cohesion in the member states concerned. Despite the remarkable efforts to improve the competitiveness of aviation in the EU single market, certain regional routes have traditionally suffered a lack of scheduled air routes. The internal market, once liberalized, has provided an instrument in order to ensure adequate transportation for the carriage of goods and persons in special cases. When this occurs, according to Regulation (CE) no. 1008/2008, national civil aviation authorities may impose a public service obligation (PSO) on specific air routes. In addition, this form of public intervention can lead to restriction on market access to one carrier, and even to the payment of compensation to the awarded airline through the launching of a tendering procedure for the operation of the required air service. In the case of Spain, this type of administrative concession has been shifting towards e-procurement processes by providing transparent electronic portals. This research aims, precisely, at encouraging a better understanding of how information technology may have systemic benefits to public bodies aiming to efficiently conduct a digital procurement ecosystem. To do this, the authors have analyzed the 23 PSO routes imposed so far, and the corresponding administrative procedures. The findings suggest that e-procurement has led to greater public transparency.

In economic literature, the relationship between entrepreneurship and economic growth has been widely discussed for some time now. In addition to the different theoretical approaches, a considerable amount of empirical works in recent decades have sought to verify the direct link between both variables by analyzing datasets from several distinct geographic areas. On one hand, it highlights the absence of a common indicator to measure entrepreneurship in practice relating to a country’s economic growth; on the other hand, it shows a great diversity of factors determining them. With the aim of providing new empirical evidence in the field of European entrepreneurship, this paper has analyzed data relating to 31 European countries over the last decade by introducing self-employment as an empirical proxy of entrepreneurship. In particular, this study contrasts the positive effect of public expenditure, investment, human capital, and entrepreneurship on economic growth for a wide range of countries and examines the impact of some economic and educational variables on self-employment, such as unemployment, taxes, education, and early school leaving. The estimation method used in this research had to consider the Ordinary Least Squares through a multiple regression model of constant coefficients based on annual Eurostat statistics for the period of 2010 to 2019. The results obtained verify the positive effect of public expenditure, investment, human capital, and entrepreneurship on economic growth. Moreover, the analysis of other factors affecting entrepreneurship, segregated by gender, shows how unemployment and the level of education have a positive impact on self-employment, while significant increases in the tax rate on capital and early school leaving harmed such variable. No significant differences were found between males and females.

PURPOSE: Accurately forecasting real estate prices presents a significant challenge due to the complex interplay of economic, social, and spatial variables. Artificial Intelligence (AI) offers a promising avenue to enhance predictive accuracy by integrating advanced analytical techniques. This study examines the role of AI in real estate pricing by identifying prevailing research trends and assessing its practical applications in cost reduction, process automation, and decision-making. METHODOLOGY: A two-pronged approach was employed, combining bibliometric analysis with insights from expert interviews. The bibliometric study mapped the evolution of AI-related research in real estate, highlighting key themes and methodological trends. The case study analysis provided complementary insights into how AI is applied in industry practice, particularly in streamlining construction processes, automating asset monitoring, and enhancing marketing strategies. FINDINGS: The study identifies a growing academic interest in AI-driven real estate research, particularly since 2018, with an increasing focus on machine learning, deep learning, and geospatial analysis. While scholarly research aligns with market needs in price forecasting and decision support, gaps remain in topics like operational efficiency and automation. Empirical evidence suggests that AI applications extend beyond price estimation, influencing profitability through process acceleration and cost optimization. However, international collaboration in the field remains low, limiting the scalability of AI-driven pricing models across different market contexts. IMPLICATIONS: The findings underscore AI’s transformative impact on real estate by bridging research and industry applications. Theoretically, the study highlights the shift from management-oriented frameworks toward data-driven and algorithmic approaches. AI enhances price estimation by integrating diverse data sources and improving risk assessment. However, challenges persist, including data accessibility, algorithm interpretability, and the demand for specialized AI expertise. Addressing these issues could unlock further advancements in predictive modeling and real estate market efficiency. ORIGINALITY AND VALUE: This research provides a comprehensive perspective on AI’s role in real estate pricing by integrating bibliometric analysis with case study insights. It extends existing knowledge by identifying key research gaps, emphasizing the need for interdisciplinary collaboration, and demonstrating AI’s potential beyond price prediction to broader market dynamics and operational efficiencies.

Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure and transparent transactions. Using its decentralized structure, transparency, and even pseudonymity, blockchain is increasingly adopted worldwide for large-scale energy trading, peer-to-peer exchanges, project financing, supply chain management, and asset tracking. The research comprehensively analyzes blockchain applications across multiple fields related to energy, bibliographically evaluating their transformative potential. In addition, the study explores the architecture of various blockchain systems, assesses critical security and privacy challenges, and discusses how blockchain can enhance operational efficiency, transparency, and reliability in the energy sector. The paper’s findings provide a roadmap for future developments and the strategic adoption of blockchain technologies in the evolving energy landscape for an effective energy transition.

This research aims to assess the efficient use of any public support in ensuring adequate regular passenger transport services serving the city of Melilla, Spain, with mainland Europe. Considering ...