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Found 6374 publications
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
Predatory Medical Journals in Patent Literature: A Hidden Threat
Publication Name: Advanced Pharmaceutical Bulletin
Publication Date: 2025-11-01
Volume: 15
Issue: 4
Page Range: 693-699
Description:
Purpose: The negative impact of potential predatory journals has been widely discussed, primarily within academic contexts. However, their influence beyond academia remains underexplored. This study aims to address that gap. Methods: The current editorial utilised a sample list of 8 potential predatory medical journals. We compiled a list of potential predatory medical journals using the discontinued titles list in Scopus and the current blocklists. Then their patent-to-paper citations have been examined to understand the dissemination of questionable medical publications outside of academia. Results: This indicates that potential predatory medical journals received 483,848 citations from scholarly works and 4,251 citations from patents. Conclusion: When patents cite papers from predatory journals, flawed information may propagate, or potentially leading to wrongful patent rejections and wasted resources. This serves as a warning for the patent community to take action against potential predatory journals.
Open Access: Yes
Feature-Optimized Machine Learning Approaches for Enhanced DDoS Attack Detection and Mitigation
Publication Name: Computers
Publication Date: 2025-11-01
Volume: 14
Issue: 11
Page Range: Unknown
Description:
Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation.
Open Access: Yes
AGGLOMERATION TENDENCY ANALYSIS OF STAINLESS STEEL POWDERS FOR DMLS PRODUCTION USING SEM IMAGING
Publication Name: Mm Science Journal
Publication Date: 2025-11-01
Volume: 2025-November
Issue: Unknown
Page Range: 8759-8764
Description:
Direct Metal Laser Sintering (DMLS) technology is gaining ground in the automotive industry, especially in areas where a high strength-to-weight ratio is of paramount importance, such as racing cars and limited production supercars. It has the advantage of being able to create complex, weight-reduced components. In this research, we present a scanning electron microscopy study of a powder feedstock of a given particle size. The state of the new feedstock and the effect of cyclic reuse are investigated and the changes are analysed. DMLS technology enables virtually waste-free production, as the powder can be reused after sieving, with the exception of the supports used for undercuts. The aim is to investigate whether the particles tend to agglomerate and thus form larger irregular clusters. It is well known in the literature that clusters adversely affect powder fluidity, spreadability and laser energy absorption, suggesting that the quality of the powder is a fundamental determinant of the properties of the final product. Clusters should be identified and investigated from both a scientific and a technological point of view. The investigated powder material is Oerlikon MetcoAdd 17-4PH-A, a martensitic stainless steel feedstock produced by gas atomization.
Open Access: Yes
Educating the Educators: Initial Findings from a University CPD in Hungary
Publication Name: Education Sciences
Publication Date: 2025-11-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
Improving the quality of higher education (HE) is a global priority as universities strive to equip graduates with skills necessary for today’s dynamic world. Well-trained educators are key to fostering these skills and can best develop them by adopting active learning approaches that deepen student understanding. Educator training is thus vital. In 2022 Széchenyi István University (Hungary), launched a four-year Continuing Professional Development (CPD) programme to upskill its academic staff. Given the traditional teaching culture in Hungarian HE, the CPD helps teachers adopt active learning practices to better prepare students for today’s world. This study explores the impact CPD has had on teaching practices thus far. Using a mixed-methods design, data were collected through questionnaires completed by 97 teachers (13% of staff) in 2022–2023 and follow-up group interviews with 13 teachers in 2025. Findings indicate that the CPD initiative has fostered professional growth to a certain extent, with teachers selectively experimenting with new methods, enhanced teacher motivation and increased student engagement. However thus far, systemic pedagogical change is limited, constrained by cultural and institutional barriers. The study highlights the importance of institutional support to achieve widespread pedagogical change in Hungarian higher education.
Open Access: Yes
Straw mulching optimized the root and canopy structure of soybean by reducing the topsoil temperature before blooming period
Publication Name: Field Crops Research
Publication Date: 2025-11-01
Volume: 333
Issue: Unknown
Page Range: Unknown
Description:
Context: The soybean seed yield in the Huang-Huai-Hai (HHH) region is challenged by high temperatures before blooming. Straw mulching can act to reduce topsoil temperature. However, little is known about whether changes in topsoil temperature contribute to the optimization of soybean root and canopy structure and, ultimately, yield. Objective: The aim of this study is to investigate the effects of straw mulching on soybean topsoil temperature, root growth, and canopy structure in the HHH region, China. Methods: A randomized block design was adopted (2020–2023) in the field, including three straw treatments: straw removing (SR), straw mulching (SM), and straw crushing (SC). Topsoil temperature, root morphology, leaf area index (LAI), light transmittance, canopy photosynthesis, dry matter accumulation, and seed yield of soybean under different treatments were measured. Furthermore, the test results were validated by pot experiment (LT: topsoil cooling, CT: topsoil non-cooling) in 2024. Results: Before soybean blooming, the highest topsoil temperature was 28.47℃ in SR, followed by 27.47℃ in SC and 26.95℃ in SM. Compared to SR and SC, the root length, root surface area, root volume and root dry weight of SM increased by an average of 26.04 %, 27.79 %, 29.13 % and 38.82 %, respectively. Soybean root dry matter weight was significantly positively correlated (P < 0.01) with the LAI and above-ground dry matter accumulation. Compared to SR and SC, Fv/Fm, Y(II), and ETR under SM treatment increased by 8.38 %, 7.94 %, and 7.73 %, respectively. Y(II) of the LT treatment was also significantly (P < 0.05) increased by 17.53 % compared to CT. Among the three treatments, soybean canopy photosynthetic rate and seed yield under SM treatment were, on average, significantly increased by 9.97 %, and 11.87 %, respectively. Furthermore, we identified the LAI characteristics of high-yield soybean canopy: 2.22 0.62 in the lower layer. Conclusion and implications: These findings imply that regulating topsoil temperature through straw mulching optimizes root and canopy development, improving soybean yield. This study provides insights into mitigating heat stress and enhancing sustainable soybean production in warm climates.
Open Access: Yes
