Sustainable innovation is crucial for addressing social and environmental challenges and is a key driver of enterprise competitiveness and economic growth. This study examines how board heterogeneity influences sustainable innovation in enterprises, particularly within the context of China’s Science and Technology innovation board. Findings reveal that diverse boards enhance sustainable innovation and impact M&A activities, which in turn mediate the relationship between board diversity and corporate sustainability. The research aims to understand the optimal board composition for scientific and creative enterprises, analyze the mechanisms behind board heterogeneity’s effect on innovation, and assess M&A’s role in this process. The study’s outcomes underscore the importance of board diversity for fostering sustainable innovation and suggest that M&A can be a critical pathway to enhancing corporate sustainability.

This article explores the development and legislative process of concession agreements within the framework of Public-Private Partnerships (PPPs) in the EU, tracing their origins to the United Kingdom in the early 1990s. Driven by national policies, the Ministry of Finance in China has promoted PPPs in infrastructure and public services. This study focuses on the basic principles, legal nature, and general rules of EU concession agreements, aiming to provide legal strategies for Chinese franchising agreement legislation by drawing on the EU’s legislative experiences.

The use of porous media to simplify the thermohydraulic of a nuclear reactor is the topic of recent research. As a case study, the rector of 200 kW installed at Missouri University of Science and Technology is modeled in this paper. To help this objective, a fundamental CFD examination was completed to supplement the neutronics investigation on the present reactor. Characteristics of thermal energy removal from a typical research reactor are modeled by numerical thermal transport in porous media. The neutron flux is modeled by the nodal expansion method. For the thermo-hydraulic part, a three-dimensional governing equation is solved by an iterative method to find the steady-state solution of fluid flow and temperature in loss of coolant condition where the heat produced in the reactor core is removed by free convection. The profiles of heat flux for various power levels are benchmarked. Pressure, temperature, and velocity contours in the power passage were assessed at 300 kW and 500 kW power levels. To reduce the computational cost, a porous media approach for the whole geometry was utilized. The numerical results agree with the experimental results. The developed model can be used for safety and reliability analysis for various loss of coolant accidents.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

This research article explores the relationship between psychological well-being and satisfaction with life among young, athletically talented students educated through individualised programs. The primary objective is to assess whether a safe educational environment, emphasising psychological safety and individual support, positively impacts the general satisfaction and academic performance of these students. Using Ryff and Keyes’ Psychological Well-Being Scale and Diener’s Satisfaction with Life Scale, data were collected from 188 participants—Secondary and university students engaged in rigorous athletic training while completing their studies in the Czech Republic. Key findings reveal a strong correlation between self-acceptance, autonomy, coping with the environment, and enhanced satisfaction with life, indicating that well-being in young athletes is significantly influenced by psychological resilience, emotional support, and control over one’s educational journey. Research highlights that individually tailored learning environments, which provide flexibility for training and access to mental health support, contribute to a balanced development between academic and athletic goals. Additionally, the results suggest that a positive correlation within the educational environment, both with peers and instructors, further strengthens the satisfaction with life and reduces the risk of burnout. Implications underscore the need for educational institutions to adopt holistic approaches that support psychological well-being and accommodate the unique needs of athletically talented students. Recommendations include structured mentorship, flexibility in academic scheduling, and access to professional counselling. Future research should investigate the long-term impacts of such environments on academic and athletic success, considering factors such as social inclusion and the effects of digital education.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.