Search in Publications
Found 6374 publications
Empowering circular economy transformation through immersive digital open innovation
Publication Name: Journal of Innovation and Knowledge
Publication Date: 2025-11-01
Volume: 10
Issue: 6
Page Range: Unknown
Description:
Despite broad support for sustainability, the transition from linear to circular economic models remains slow and fragmented across industries. Digital technologies such as the metaverse present new opportunities for enabling circular economy (CE) practices, yet their strategic integration within organizational systems is not fully understood. To address this gap, this study proposes and empirically validates a model that explains how metaverse adoption influences CE implementation through the mediating role of Immersive Knowledge Co-Creation (IKCC), and the moderating effect of Open Eco-Innovation Capability (OEIC). Grounded in the knowledge-based view (KBV), interactive learning theory, and dynamic capabilities theory, the research develops a process-driven and capability-oriented framework to explore the mechanisms and boundary conditions for digital-enabled circular transformation. Empirical data were collected from 220 respondents in Germany's advanced manufacturing sector and analyzed using partial least squares structural equation modeling. The results confirm that IKCC significantly mediates the relationship between metaverse adoption and CE implementation, and that this mediated relationship is stronger in firms with high OEIC. These findings contribute to the growing body of literature on digital innovation in the CE by highlighting the critical role of immersive collaboration and organizational readiness in driving circular processes.
Open Access: Yes
Analysis of Success Factors in Construction Projects under Consideration of Sustainability: A Literature and Legal Approach
Publication Name: Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
Publication Date: 2025-11-01
Volume: 17
Issue: 4
Page Range: Unknown
Description:
Success of construction projects will be affected by changing law and policy. In construction, where much money is at stake, risks must be calculated very carefully to finish the construction project with success. Nowadays, social impact of buildings, engineering ethics, and, in particular, environmental sustainability become increasingly important topics in construction, driven by current law and policy, especially in the European Union. The aim of the recently started research project is to find out if and how risks resulting from current changes in law and policy, with focus on sustainability, will be considered in construction project management. In the first step, the authors reviewed legal regulations as well as political considerations, and analyzed relevant research literature. The results are summarized in this paper. They can be a basis for a deeper risk analysis and the elaboration of solutions for how to deal with these challenges in construction contract design and project management. During their research, the authors studied relevant current European and German law and policy as well as scientific articles from the databases Web of Sciences and Scopus. The conclusion from the detailed literature analysis is that there is a need for more and deeper research to identify risks for sustainable construction projects resulting from law and policy and for developing strategies to manage them.
Open Access: Yes
Airline performance assessment using an improved neutral cross-efficiency method: principal component analysis through Q-methodology
Publication Name: Transportation Research Interdisciplinary Perspectives
Publication Date: 2025-11-01
Volume: 34
Issue: Unknown
Page Range: Unknown
Description:
Assessing the performance of airlines is vital in the aviation industry, as it affects multiple stakeholders, including airlines, travelers, regulatory authorities, and investors. It is known as a key driver of growth and sustainability in the aviation sector. Hence, the main aim of the current study is to utilize the Principal Component Analysis (PCA) through Q-methodology within the Neutral Cross Efficiency Method (referred to as QNCEM) as an innovative technique to provide an assessment framework for airlines. QNCEM offers policymakers numerous advantages as it permits the elimination of irrelevant perspectives during the assessment process, enables the determination of each Decision-Maker’s (DM) contribution, and plays a crucial role in achieving consensus by leveraging factor analysis to extract perspectives that are representative of the group’s opinions. In this research, the efficiency of 17 Iranian airlines is assessed using QNCEM, considering both desirable and undesirable outputs, such as flight delays, demonstrating its practicality and effectiveness. The selection of a loading factor of 0.626 allowed QNCEM to encompass a comprehensive range of viewpoints from 17 DMs. This deliberate choice ensures the inclusion of a diverse set of perspectives, maximizing the richness of the analysis and explaining a cumulative variance of more than 96%.
Open Access: Yes
Growth Performance, Carcass and Meat Quality Traits of Three Rabbit Lines Under Heat Stress Conditions
Publication Name: Animals
Publication Date: 2025-11-01
Volume: 15
Issue: 21
Page Range: Unknown
Description:
Given the yearly challenging environmental scenario with more and more frequent and intense heat waves, the livestock sector has to find affordable and sustainable solutions to face the expected increase in meat demand by 2050. Among livestock species, rabbits are particularly sensitive to heat stress (HS) but, paradoxically, the scientific background on the response of different genetics to environmental stressors like HS is rather scarce. This is a significant gap, especially considering that most of the demographic growth, and meat demand, is expected in developing countries where rabbits play a key role in subsistence farming. Therefore, this research investigated the effects of environmental temperature (Control—20 °C; High—28 °C) on growth performance, slaughter traits and meat quality of three Hungarian rabbit genotypes (Pannon Large—PL; Pannon White—PW; Pannon Ka—PK). Animals (n = 360) were housed in wire-mesh cages (3 animals/cage) in two separate controlled-temperature rooms (60 rabbits/genotype/room), from 5 to 11 weeks of age, during which they received ad libitum feed and water. Even if the three genotypes were exposed to the same environmental challenge, they exhibited different responses. The PL line showed superior performance, with the highest carcass weight and yield (p < 0.001), and the greatest water-holding capacity (p < 0.01) in the loin muscle. The PW rabbits showed the largest reduction in overall weight gain (−24.7%; p < 0.001) and the lowest decrease in feed conversion ratio (−3.20%; p < 0.001). PK rabbits experienced the greatest reduction in total dissectible fat (−34.6%; p < 0.001) and hind leg lipid content (−20.3%; p < 0.01), with the highest proportion of polyunsaturated fatty acids (p < 0.01), which fostered meat lipid oxidation (p < 0.05). As expected, these differences in performance and meat quality traits reflected the distinct selection criteria and genetic background of these genotypes: the PL is a paternal line, the PK is a maternal line, and the PW is a productive line. Regarding the temperature effect, PK and PW genotypes were the most impacted by chronic HS: PW rabbits suffered the largest performance depression, while PK rabbits showed the worst carcass and meat quality traits. Instead, PL rabbits demonstrated the best outcomes under chronic HS, showing the greatest productive efficiency and satisfactory meat quality traits.
Open Access: Yes
DOI: 10.3390/ani15213200
Development of hybrid optimization approach combined with AI-based techniques for prediction of electrical fields in overhead transmission lines
Publication Name: Journal of Supercomputing
Publication Date: 2025-11-01
Volume: 81
Issue: 16
Page Range: Unknown
Description:
Getting a precise estimate of electric fields around extra-high-voltage (EHV) transmission lines is essential for keeping the public safe, ensuring environmental compliance, and planning infrastructure effectively. Unfortunately, traditional numerical methods often struggle with accuracy and can be slow to converge, which makes them less suitable for large-scale projects. This study introduces a hybrid computational framework that combines the Charge Simulation Method (CSM) with the Firefly Algorithm (FA). This combination helps optimize the number, position, and strength of simulation charges, leading to better modeling accuracy and efficiency. Additionally, we have trained three artificial intelligence (AI) models: Multilayer Perceptron Neural Network (MLPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM) on real-world field data to reliably predict electric field values. Notably, LS-SVM is being used in this context for the first time and has shown to outperform the other models in accuracy, generalization, and speed. We evaluated the proposed CSM-FA hybrid model alongside AI predictions using metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2), revealing significant improvements over traditional methods. Given the heavy computational demands of the optimization and learning phases, we utilized high-performance computing (HPC) resources for implementation. This work not only advances algorithmic innovation and AI-assisted simulation but also enhances HPC applications, providing a scalable and precise solution for real-time field monitoring and regulatory assessments. The methodology aligns well with the scientific goals of The Journal of Supercomputing and fosters advanced research in intelligent power system modeling.
Open Access: Yes
Quality at the Core: A Multifaceted Analysis of Higher Education’s Impact on the Knowledge Economy
Publication Name: Journal of the Knowledge Economy
Publication Date: 2025-11-01
Volume: 16
Issue: 5
Page Range: 16637-16669
Description:
In a globalized, knowledge-driven economy, the quality of higher education is a pivotal contributor to socio-economic advancement, yet its assessment remains complex due to its inherent subjectivity and multifaceted nature. This study presents an innovative methodological approach for evaluating the quality of higher education within the knowledge economy framework, utilizing the context-input-process-output (CIPO) model, exploratory factor analysis, and stochastic frontier analysis. The input indicators include financial resources (government spending per student, direct public funding for a student, share of capital/current expenditures, compensation to the teaching/nonteaching staff), human resources (student–teacher ratio, share of enrollment in higher education, number of teachers), and expected duration of higher education. The output indicators include the general level of graduation from first-degree programs and level of education, at least completed short-cycle higher education. Indicators of economic (GDP per capita) and social (employment rate and Gini index) development of the country were chosen as context parameters. Conducting a comparative analysis across 36 European countries from 2001 to 2017 available data, the authors identified integrated factors for input and output parameters, as well as context parameters characterizing the quality of higher education. Then we categorize national higher education systems into five distinct quality levels: very low, low, satisfactory, high, and very high. This classification enables us to dissect and understand the challenges faced by countries at the lower end of the quality spectrum and propose strategic solutions informed by the best practices of the leading nations. Our findings offer critical insights into optimizing higher education quality to enhance competitive advantages for educational institutions, improve employment prospects and living standards for students, secure a more qualified workforce for employers, and spur economic growth and productivity at the national level. This comprehensive assessment underscores the role of quality education as a cornerstone of the knowledge economy, driving innovation, economic development, and societal progress.
Open Access: Yes
Exploring the collaboration networks between highly cited researchers in highly cited papers
Publication Name: Scientometrics
Publication Date: 2025-11-01
Volume: 130
Issue: 11
Page Range: 6513-6540
Description:
Collaboration between researchers has been shown to influence their productivity and scientific impact. Although these ties have been widely discussed in the literature, the nature of the co-authorship networks between the most successful scholars remains a question. To provide an answer, this study conducts a cross-case analysis of the collaboration networks between Highly Cited Researchers, focusing on the research output and co-authorship patterns in Highly Cited Papers across three award categories: Clinical Medicine, Materials Science, and Social Sciences. Our findings indicate that there are category-specific differences in publication output and the intensity of collaboration between Highly Cited Researchers. Notably, Highly Cited Researchers in the Social Sciences demonstrate a less collaborative approach to research than those in Clinical Medicine and Materials Science. While Highly Cited Researchers in all three categories featured interconnected collaboration networks among themselves, those in Clinical Medicine and Materials Science exhibited a more collaborative environment, while those in Social Sciences showed a tendency towards independent research efforts. The case of Social Sciences is further evidenced by higher fragmentation within the collaboration network of Social Sciences, indicating a less cohesive collaborative framework. The analysis of the Giant Component—the largest cohesive subset of the network—revealed that it is less representative of the overall network structure in the Social Sciences than in Clinical Medicine and Materials Science. Finally, the centrality measures indicated that Highly Cited Researchers with high betweenness and closeness centrality act as crucial bridges within each network, significantly shaping the structural cohesion and collaborative dynamics of their respective fields.
Open Access: Yes
Investigation of the cascade utilization of LNG cold energy using total site heat integration method
Publication Name: Thermal Science and Engineering Progress
Publication Date: 2025-11-01
Volume: 67
Issue: Unknown
Page Range: Unknown
Description:
Liquefied natural gas (LNG) undergoes regasification before delivery to end users, releasing a large amount of cold energy that is significant for efficient utilization. Therefore, based on the principle of “temperature counterpart, cascade utilization”, this study integrates Pinch Analysis with Total Site Heat Integration (TSHI) to propose two new types of integrated systems for LNG cold energy cascade utilization. The first system, designed for rich-gas LNG, comprises light hydrocarbon separation, cryogenic comminution of rubber, electricity generation by organic rankine cycle, and heat management of data center by direct cooling (LHS-CCR-ORC-DC). The second system, designed for lean gas LNG, replaces the LHS unit with an air separation process (ASP) while retaining CCR, ORC, and DC. Through the synergistic optimization of the Grand Composite Curve (GCC) and total site composite curve (TSCC), the proposed system realizes the cascade and efficient utilization of the LNG cold energy in the whole temperature range (−160 °C to 10 °C). Thermodynamic analysis shows that the energy utilization efficiency of the LHS-CCR-ORC-DC and ASP-CCR-ORC-DC systems is improved by 50.06 % and 40.93 %, respectively, compared with the single cold energy utilization mode. Economic evaluation indicates net present values of 1.81 × 108 $ and 2.32 × 108 $ for the two systems, with levelized costs of energy of 0.062 $/kWh and 0.055 $/kWh, respectively. By replacing fossil‐fuel power generation and compression‐based refrigeration, the integrated systems achieve annual CO2 reductions of 261.84 kt and 238.20 kt, respectively. This study provides theoretical basis and technical support for the efficient utilization of LNG cold energy and for the synergistic optimization of its cascade utilization in industrial parks.
Open Access: Yes
Effects of Exercise Addiction and the COL1A1 Gene rs1800012 Polymorphism on Injury Susceptibility in Elite Female Volleyball Players
Publication Name: Genes
Publication Date: 2025-11-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
Objectives: The objective of this study was to separately examine the effects of exercise addiction and the Collagen Type I Alpha 1 Chain (COL1A1) gene rs1800012 G/T polymorphism on injury susceptibility in elite female volleyball players, and to test the hypothesis that the T allele, previously identified as a risk allele, is underrepresented in volleyball players compared to the general population. Methods: The study included 50 professional Turkish female volleyball players with documented injury data, along with 557 Turkish controls, 53 professional Russian volleyball players, and 810 Russian controls. The Turkish participants were enrolled in a case–control study, an injury study, and an exercise addiction study, whereas the Russian participants were enrolled solely in a case–control study. Results: Injured players had significantly higher scores in the Delay of Individual Social Needs and Conflict subscale of the Exercise Addiction Scale compared to their uninjured counterparts (p = 0.036). The random-effects meta-analysis revealed a significantly lower frequency of the COL1A1 T allele in volleyball players compared to controls (pooled OR = 0.63, 95% CI: 0.41–0.96, p = 0.031). Athletes who had not undergone surgery had a significantly higher frequency of the G allele compared to controls (89.2% vs. 78.7%, p = 0.037; OR = 2.23, 95% CI: 1.1–4.7). Among injured athletes, those carrying the GT genotype were significantly more likely to experience prolonged recovery (≥3 months) (57.1%) compared to those with the GG genotype (28.0%, p = 0.017). Conclusions: Exercise addiction and the COL1A1 rs1800012 T allele were associated with a higher incidence of injury in female volleyball players. The T allele was also associated with a longer recovery time following injury.
Open Access: Yes
