Search Results (854)

Entrepreneurship is widely recognized as a critical engine of economic growth. This is especially true in rural areas, where resources, policy support, and talent pools are often constrained. Stimulating entrepreneurial vitality in these regions has thus become an urgent policy and research priority. This study adopts an inclusive growth perspective, selecting six key elements—economic level, industrial structure, financial development, educational condition, medical condition, and social security—to construct a theoretical model exploring the configuration pathways that drive rural entrepreneurial activity. Using fuzzy set qualitative comparative analysis (fsQCA), the study examines 982 rural regions in China and draws the following conclusions: (1) None of the six key elements is a necessary condition for rural entrepreneurial activity. (2) The “finance and healthcare-driven” type (C1), “industrial and educational balance” type (C2), “financial and educational synergy” type (C3), and “industrial and healthcare support” type (C4) are the configuration paths to achieve high rural entrepreneurial activity. The findings provide both theoretical and practical insights for stimulating entrepreneurship in rural China. Specifically, they highlight how different developmental configurations can activate local entrepreneurial ecosystems, expand employment and entrepreneurship opportunities for vulnerable groups, and contribute to sustainable poverty alleviation. Full article

Amid rapid technological change and industrial transformation, digital consumption (DC) has become both a driver of domestic demand and a potential catalyst for corporate innovation. Yet, systematic evidence on how DC shapes innovation behavior remains limited. This study investigates the causal effect of DC on corporate innovation activity (CIA) by exploiting China’s Information Consumption Pilot Policy (ICPP) as a quasi-natural experiment with firm-level panel data from 2008 to 2022. The results show that DC significantly enhances CIA through three mechanisms: strengthening government attention to science and technology talent, advancing the circulation and utilization of regional data, and promoting corporate data assetization. Moreover, the effect is stronger in eastern regions, in areas with greater governmental digital engagement, and among firms with stronger managerial incentives. Further analysis indicates that DC not only increases overall innovation activity but also disproportionately fosters substantive innovation, as reflected in invention patents. These findings provide new empirical evidence on the differentiated role of DC in shaping both the quantity and quality of corporate innovation, offering insights for the design of digital economy policies in developing countries. Full article

Addressing Artificial Intelligence (AI) faculty deficiencies in higher education, this paper develops the AI+ Dual-qualified Teacher Capability Ecology Model (AI-DTCEM) based on Capability Ecology Theory. The model is developed after a thorough analysis of the current state of new engineering talent cultivation in universities and the innovative practical abilities required in the AI+ environment. This paper proposes an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Additionally, it uses the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a backdrop to introduce a deep collaborative education model, showcasing the theoretical and practical achievements of this project. Using NetLogo as the simulation platform, this paper designs a 96-month system dynamics experiment to compare and analyze the outcomes of four scenarios: the baseline experiment, the AI-enhanced experiment, the policy-driven experiment, and the comprehensive optimization experiment. This study reveals the following findings: (1) Policy-driven initiatives are crucial for the successful construction of dual-qualified teacher teams, with the policy-driven scenario achieving the highest overall skill level (9.332). (2) The application of AI technology significantly enhances teacher skill development, resulting in AI skill improvements ranging from 116.6% to 163.4%. (3) The comprehensive optimization scenario (utilizing a collaborative mechanism) achieves systemic advantages, realizing a 100% dual-qualified teacher ratio. However, this comes with diminishing marginal returns on investment. This research provides a theoretical foundation, quantitative analysis, and practical pathways for developing dual-qualified teacher teams in the AI+ era. Full article

Teacher innovation is critical for fostering student creativity, enhancing school effectiveness, and advancing national talent strategies. Grounded in the broaden-and-build theory of positive emotions and social information processing theory, this study develops a moderated mediation model to explore the motivational mechanisms underlying teachers’ innovative work behavior. Using survey data from 508 teachers in mainland China, the analysis reveals that teacher well-being positively influences innovative work behavior, and this relationship is mediated by perceived insider status. Furthermore, principal authentic leadership enhances the impact of perceived insider status on innovation and strengthens the indirect effect of well-being through this mediator. These findings underscore the importance of both emotional pathways and contextual signals in shaping teacher innovation, offering theoretical contributions to education leadership and teacher work behavior research while providing practical implications for creating supportive and innovation-conducive school environments. Full article

Background: Football performance depends on the integration of physical, technical, and cognitive abilities under constantly changing conditions. In this context, dual-task training combining physical and cognitive demands has emerged as a promising approach to enhance decision-making and game intelligence in youth football players. Objective: The aim of this study was to determine the effects of an eight-week dual-task training programme on physical (speed, strength, and agility), cognitive (working memory, planning, processing speed, and response time), technical (dribbling and short passing), and dual-task performance in U16 football players. Methods: Thirty-two players (age: 14.88 ± 0.65 years; BMI: 20.98 ± 1.79 kg/m²) were randomly assigned to a control group (n = 14) and an experimental group (n = 18). The experimental group completed a dual cognitive–motor training (CMT) programme consisting of 24 sessions (3 sessions/week, 10–15 min each), integrated into regular football practice. Pre-intervention and post-intervention assessments included football skills (dribbling and passing tests), cognitive tests (Wom-Rest and Vismem-Plan), physical tests (countermovement jump, 20 m sprint, and 505 change-of-direction), and a dual-task test (soccer skills and cognitive aptitude test). Results: The experimental group showed significant improvements in all assessed variables, while the control group exhibited no changes or declines in performance. The most notable effects were observed in SoSCAT with visual interference, dual-task cost, and 505 change-of-direction. Conclusions: The findings suggest that integrating brief dual CMT programmes into regular football practice can simultaneously enhance physical, technical, and cognitive performance in youth players. This evidence supports the implementation of dual CMT as an effective and time-efficient tool in talent development programmes. Full article

As youth workers increasingly offer support and guidance within digital environments, the question arises as to what impact this support has on the (online) lives of young people. This paper explores the contribution of youth work practice in the online lifeworld on young peoples’ development, building on previous studies concerning youth work outcomes and the developmental needs of young people. A qualitative research design was employed, including digital diaries of youth workers and semi-structured interviews with both young people (N = 37) and youth workers (N = 25). The findings highlight the role of youth work in helping young people navigate social media; develop new skills, talents, and social connections; and increase awareness of online risks. Youth workers also support young people in coping with negative online experiences, including loneliness and mental health challenges. The contribution of online youth work is less visible in certain aspects of developmental needs, namely online safety and privacy, self-image, and assessing online information. This paper concludes by emphasising the need for further research into the long-term impact of youth work in the online lifeworld, particularly in light of rapid technological developments, the growing influence of artificial intelligence, and the increasing involvement of youth in digital forms of crime. The findings described in this study can form a base for future research to better understand the impact of these emerging issues on youth development and youth work practice, as well as to develop appropriate interventions. Full article

Food legumes play a pivotal role in China’s food security, nutritional health, and green development strategies due to their unique advantages. This paper presents an empirical study on the economic evaluation of scientific research on pest and disease control for food legumes. Using panel data from 31 Chinese provinces from 2008 to 2023, we employ a Double Machine Learning (DML) approach to identify the impact of investment in plant protection research on food legume outputs. The results indicate a steady increase in China’s investment in this field, with an average annual growth rate of 5.19% from 2008 to 2023, and the total investment in 2023 was 2.14 times that of 2008. Investment in plant protection research effectively mitigates output losses and leads to significant production increases. Specifically, a 1% increase in research investment corresponds to a 0.2% increase in food legume output. This effect remains robust across various algorithms, time windows, and control variable settings. Based on these findings, we recommend: (1) increasing financial support and talent acquisition for research on food legume pests and diseases to enhance the stability and sustainability of research investment; (2) strengthening cooperation mechanisms between research institutions and enterprises to leverage their respective strengths and promote the commercialization of research outcomes and regional variety extension; (3) establishing a diversified research investment system that explores a co-construction model guided by the government, involving enterprises, and utilizing public–private partnerships to reconcile the conflict between long research cycles and market demands; (4) fostering a dual-track linkage between regional technological innovation and enterprise product commercialization to improve the efficiency of technology transfer and application; and (5) strengthening R&D in cutting-edge fields like Artificial Intelligence to improve the efficiency and precision of pest and disease control. Full article

Amid intensifying geopolitical tensions and global uncertainties, regional economies face mounting pressures that threaten both stability and sustainability. Against this backdrop, building resilient regional systems has become a central issue in sustainable urban development. As a key driver of resilience, innovation has been central to China’s development agenda. Continuous and large-scale R&D investment has redirected focus from input expansion to efficiency improvement, positioning R&D efficiency at the heart of resilience-building. Under external shocks and uncertainty, can improvements in R&D efficiency enhance regional economic resilience? If so, which additional factors embedded in sustainable urban development planning can further amplify this effect? To address these questions, this study employs provincial panel data from 2000 to 2021 and integrates the SBM-DEA approach with an entropy-weighted resilience index for regression analysis. The results indicate that (1) R&D efficiency exerts a positive but limited impact on resilience, with an average increase of only 0.188 units, indicating that efficiency alone cannot generate resilient economies without institutional coordination; (2) human capital agglomeration and financial density strengthen this relationship, highlighting the need to integrate talent and financial strategies; (3) the positive effect is observed in eastern provinces but remains insignificant in central and western regions, revealing pronounced structural disparities that risk widening the resilience gap across regions rather than fostering balanced development; and (4) targeted government intervention effectively converts innovation efficiency into resilience gains, fostering coordinated and sustainable development. This study empirically demonstrates that improving R&D efficiency significantly enhances regional resilience in China and based on this evidence introduces the ICT Synergy Framework as a novel analytical lens for understanding how innovation, capital, and talent jointly drive resilience and sustainable development. The findings further suggest that targeted government intervention in R&D resource allocation can reinforce resilience, offering broader lessons for other developing economies. By integrating innovation outcomes with spatial and institutional planning, the study provides actionable insights for advancing sustainable urban development and coordinated regional growth. Full article

Climate change is a critical constraint on the development of new-quality productive forces (NQPFs), making it essential to clarify its relationship with urban development strategies to enhance productivity. Using panel data from 284 Chinese cities during 2010–2022, this study leverages the climate-resilient city pilot policy as a quasi-natural experiment and applies a double machine learning approach to estimate both the causal impact and underlying mechanisms of this policy on NQPFs. We further examine heterogeneous effects across geographic regions and city types. Our findings show that first, climate-resilient urban development significantly boosts NQPFs, with results remaining robust across multiple sensitivity tests. Second, this effect operates through three key channels—talent agglomeration, data flow enhancement, and infrastructure-related industrial upgrading. Third, the policy’s impact is stronger in western and coastal cities; resource-based cities and non-environmentally protected cities exhibit greater responsiveness, amplifying the positive outcomes. This study provides systematic empirical evidence on the nexus between climate resilience and high-quality development, offering actionable insights for designing localized strategies to advance climate-resilient urbanization and foster high-quality productive forces. Full article

This study proposes a novel curriculum framework for Smart Breeding 4.0 to address the interdisciplinary talent gap in sustainable agriculture. Responding to the limitations of traditional agricultural education, the curriculum was developed through an analysis of emerging technological trends and industry needs. It is structured around four integrated modules: (1) Foundational Theory, tracing the evolution to data-driven breeding; (2) Technology Integration, combining AI and blockchain for precision breeding; (3) Practical Innovation, using real-world platforms for simulation projects; (4) Ethics and Policy, cultivating responsibility through case studies. Teaching emphasizes project-based learning with open-source tools, while assessment combines exams, data analysis, and innovation proposals. Explicitly aligned with key UN Sustainable Development Goals (SDGs), this conceptual framework provides a foundational model for agricultural universities worldwide. The primary contribution of this paper lies in its systematic design; future research will focus on empirical validation through pilot implementation. Full article

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system. Full article

Marine soft soils are characterized by high compressibility, low strength, and low permeability, which often result in excessive settlement and stability problems. Drainage consolidation methods are widely regarded as effective solutions for improving such soils. This review summarizes recent progress from four perspectives: optimization of traditional techniques, combined applications of multiple methods, development of emerging innovative approaches, and advances in drainage element materials and structures. Traditional methods such as surcharge and vacuum preloading have been refined through innovations in loading schemes, drainage improvements, and design approaches, while hybrid combinations with electroosmosis, thermal treatment, and dynamic loading have further enhanced their efficiency and applicability. In parallel, novel techniques such as siphon drainage, aerosol-assisted consolidation, and osmosis-based drainage show promise for sustainable applications. Furthermore, biodegradable and multifunctional drainage elements provide new directions for environmentally friendly and efficient soft soil improvement. Looking ahead, drainage consolidation technology is expected to move toward greener, low-carbon, and intelligent solutions. This review offers a comprehensive reference for engineering practice and a useful basis for guiding future research in marine soft soil improvement. Full article

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article

With the rapid development of cybersecurity technologies, cybersecurity testing has played an increasingly critical role in scientific research, new technology validation, system performance evaluation, and talent development. A central challenge in this domain lies in efficiently and rapidly constructing testing environments while ensuring the reliability and reproducibility of test results. To address this issue, this paper proposes an integrated evaluation method specifically designed for cybersecurity testing, combining anomaly detection and predictive analysis techniques. The method first employs an autoencoder (AE) to perform dimensionality reduction on the raw data collected from a testbed environment, followed by anomaly detection using the Isolation Forest (IF) algorithm. To assess the impact of cyberattacks on the stability of the testbed system, the steady-state data of the environment was treated as the ideal reference. The degree of disruption was then quantified by calculating the Euclidean distance between the dimensionally reduced experimental data and the reference state. Finally, a specific case study was conducted to validate the feasibility and effectiveness of the proposed method, and a percentage-based scoring mechanism was introduced to quantitatively evaluate the security level of the system. Full article

Educational reforms have increasingly shifted from teacher and content-centric approaches to prioritising the students’ needs, sometimes even extending this beyond academic success. Academic success is easily measured through standardised assessments; however, the ‘life skills’ that students require for more general success—such as self-regulation, goal-setting, and distraction control—are harder to quantify and therefore harder to implement and measure. Reflecting these challenges, this paper critiques some of the more popular frameworks which schools adopt in their aim to nurture successful students for the future. Drawing comparisons with high-performance domains such as sports, where psychological characteristics and potential are actively considered and nurtured in talent development, we highlight the limitations of current offerings in education. Through discussion, we advocate for a more research-grounded approach to learning, equipping schools with both systems and strategies for their students’ long-term life-related success. Full article