Search Results (1,082)

Background: While the biomechanical, physiological, and nutritional determinants of table tennis (TT) performance have already been summarized, a dedicated synthesis of players’ anthropometric and motor characteristics is still lacking. This review aimed to synthesize current evidence across different ages, genders, and competitive levels. Methods: BASE, PubMed, ScienceDirect, Scopus, Semantic Scholar, and Web of Science databases were searched for study selection. Cross-sectional studies investigating the anthropometric and motor characteristics of healthy TT players aged 7–40 years were considered eligible. Results: Thirty-one studies were included. Eighteen studies focused on motor characteristics, seven on anthropometric traits, and six examined both. Conclusions: Adult male TT players predominantly exhibit an endomorph–ectomorph profile with greater lean mass, whereas females show an endomorph–ectomorph somatotype. Moreover, higher-ranked athletes generally display greater lean mass. Compared to specific strength-based sports athletes, TT players tend to have lower limb circumferences and lower lean and muscle mass, while showing higher body fat than specific speed- and agility-based sports. Higher-level players tend to demonstrate faster reaction and movement times, as well as superior change-of-direction abilities. Compared to other racket sports, they tend to exhibit superior coincidence-anticipation timing under high stimulus velocities and distinctive motor coordination. In terms of strength, TT players show proficiency in horizontal jumps and dynamic back strength, despite lower vertical explosive power. The identified characteristics provide a descriptive profile of table tennis players and may inform future longitudinal research investigating their potential relevance for talent identification and development. Full article

Policies designed to enhance agricultural resilience may produce unintended trade-offs among ecological, industrial, and social subsystems due to resource competition, structural constraints, and cross-subsystem feedback. This study conceptualizes these unintended effects as a “resilience compensation trap.” Taking Nenjiang City, Heilongjiang Province, China, as a case study, we integrate social–ecological systems theory with system dynamics modeling to examine the evolution of agricultural resilience from 2015 to 2035 under five scenarios: baseline development, ecological priority, industrial upgrading, talent revitalization, and comprehensive optimization. The results show that overall agricultural resilience increases slowly under the baseline scenario. Industrial upgrading raises overall resilience to 16.43 in 2035, representing an increase of 60.92% relative to the baseline, but reduces ecological resilience by 26.94%, thereby producing a clear cross-subsystem compensation effect. By contrast, the comprehensive optimization scenario increases overall resilience to 18.07, 76.98% above the baseline, while reducing conflicts among the three subsystems. These results indicate that single-objective interventions may activate negative feedback that offset their intended benefits. By operationalizing absorptive, adaptive, and transformative capacities as interacting variables and feedback loops, this study provides a process-based explanation of agricultural resilience trade-offs. The findings further highlight the importance of coordinated ecological, industrial, and social policies. Full article

Conventional international rankings assess urban efficiency via context-blind metrics, a design that the literature suggests may structurally disadvantage Mediterranean centres and obscure the sustainable policy pathways mandated by the Sustainable Development Goals, most notably the governance of urban transitions under SDG 11. This paper proposes the Context-Weighted Liveability Index (CWLI), which introduces context-sensitive weights into the aggregation of standard smart city KPIs, bridging the global comparability of IMD-style indices with the Mediterranean-specific assessment logic of the ASCIMER framework. Weights derive from five geographic coefficients—climate, culture, economy, historical density, and demography—through a transparent weighted additive formulation with an explicit sensitivity matrix, whose robustness is verified through Monte Carlo uncertainty analysis over 5000 perturbed configurations spanning parameters, coefficients, measurements and benchmarks. Coefficients and KPIs are computed from open spatial data through a replicable GIS protocol (QGIS; OpenStreetMap, Copernicus land cover and land surface temperature, and ISTAT/ELSTAT census data at sub-municipal scale). Applied comparatively to Bologna and Athens, the framework shows that contextual weighting concentrates over 60% of the total weight on climate-sensitive indicators and yields, through the decomposition of contributions, a policy diagnosis that differs from the one suggested by reading an overall smart city rank in isolation: Athens’ largest contribution is digital and its liveability deficit territorial—a profile with direct consequences for sustainable urban transition and talent attraction. Implications for SDG 11 monitoring, equitable access to urban green space, and digital twin integration are discussed. Full article

This paper uses panel data from 30 Chinese provinces (municipalities and autonomous regions) for the period 2007–2024. It examines the impact of the agglomeration of high-end talent on regional innovation efficiency and its underlying mechanisms, based on the theory of external economies of talent agglomeration. Additionally, it analyzes regional heterogeneity and heterogeneity across innovation actors. The study finds that the agglomeration of high-end talent exerts a significant positive effect on regional innovation efficiency, exhibiting a nonlinear inverted U-shaped relationship. These findings hold even after addressing endogeneity issues and conducting various robustness tests. Heterogeneity analysis indicates that the agglomeration of high-end talent has a more pronounced positive effect on regional innovation efficiency, particularly in the eastern and western regions and in universities and research institutions; however, the optimal agglomeration level in the western region is lower than that in the eastern region. The agglomeration of high-end talent in central regions and in enterprises above a certain scale fails to significantly enhance regional innovation efficiency. The agglomeration of high-end talent positively affects regional innovation efficiency in highly marketized regions, but such an effect is not observed in regions with low marketization. The results of mediation analysis suggest that high-end talent agglomeration fosters regional innovation efficiency by facilitating knowledge spillovers and collaborative industry–university–research activities. The above research not only confirms the positive impact of the agglomeration of high-end talent on regional innovation efficiency but also provides policy recommendations for local governments regarding talent development and regional mobility. It holds practical significance for achieving major strategic goals such as building a science and technology powerhouse, a talent powerhouse, and sustainable development. Full article

Background: Sensorimotor (mu) rhythms reflect the functional state of sensorimotor cortical networks and are of increasing interest for understanding typical and atypical neurodevelopment. However, the developmental trajectories of mu rhythm modulation in preschool and early school-age children remain poorly characterized. Objectives: We studied age-related changes in alpha (8–13 Hz) and beta (13–30 Hz) sensorimotor rhythms in children aged 3 to 10 years using a mixed longitudinal design and investigated their relationship with language development. Methods: EEG was recorded twice (interval ~1 year) in 44 typically developing children during three conditions: passive hand movement (PHM), video hand movement observation (VHM), and a control condition (video fractal movement, VFM). Language was assessed with the Preschool Language Scales—Fifth Edition (PLS-5) in a subset of 32 of the 44 participants at the first time point. Modulation indices (log10(experimental/control)) were computed, and repeated-measures ANOVAs and Spearman correlations were performed. Results: PHM elicited desynchronization in both alpha and beta bands, while VHM induced alpha synchronization only. Alpha desynchronization showed contralateral lateralization during right- and left-hand movements, without age-related changes. Beta desynchronization showed no lateralization. Beta desynchronization during PHM correlated negatively with Auditory Comprehension and Total Language scores (ρ up to −0.62, FDR-corrected p < 0.05), indicating that more pronounced desynchronization of sensorimotor rhythms relates to better language abilities. Conclusions: These findings support the involvement of sensorimotor networks in auditory language comprehension and suggest that beta mu rhythm may serve as a sensitive marker of individual differences in language development, though replication in larger samples is warranted. Full article

Social media is a vital platform for policy communication, yet research rarely integrates content and structural features to evaluate communication effects multidimensionally. This study proposes a convergent framework combining text mining and social network analysis to assess policy communication effects and underlying mechanisms. Using China’s upgraded Mass Entrepreneurship and Innovation policy on Sina Weibo as a case, we analyze communication breadth, depth, audience sentiment, thematic focus, network topology, key nodes, and community characteristics. The results reveal that (1) communication breadth is dominated by official communicators, while audiences drive interactive depth, reflecting a “centralized broadcasting, decentralized engagement” model; (2) influential users express more positive attitudes than ordinary audiences; (3) discussions diversify from core innovation themes to micro-level concerns like regional development and talent policies; (4) the network shows loose global structure but strong local clustering, with bridging nodes posting less polarized, broader content. Theoretically, this study offers behavioral-level observations that align with key corollaries of the Spiral of Silence Theory—the tendency for individuals with deviating views to shift toward lower-visibility participation. These pattern-level findings offer a complementary empirical perspective on opinion expression in digital policy contexts. Practically, the findings offer preliminary insights that may inform adaptive, decentralized strategies for enhancing policy diffusion in similar social media contexts. Full article

The Artificial Intelligence Pilot Zone Policy is an important strategic initiative for building artificial intelligence (AI) innovation hubs. It provides new opportunities to enhance manufacturing firm resilience and promote the sustainable development of the manufacturing sector. The creation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones (AI Pilot Zones) is viewed in this study as a quasi-natural experiment. Using data from Chinese A-share-listed manufacturing firms from 2015 to 2023, we employ a staggered DID model to evaluate the impact of the policy on manufacturing firm resilience. We find that the AI Pilot Zone policy increases manufacturing firm resilience by an average of 0.0282 units. The analysis of potential mechanisms shows that the policy significantly promotes digital talent agglomeration, stimulates urban innovation vitality, and improves firm-level supply chain efficiency. These findings are consistent with the theoretical expectations and provide supportive evidence that these factors may constitute potential mechanisms associated with the policy’s effect on manufacturing firm resilience. The heterogeneity analysis reveals a pronounced “weakness-compensating” effect. At the regional level, the resilience-enhancing effect is stronger for manufacturing firms located in areas with relatively weak digital infrastructure. At the industry level, the effect is more pronounced among firms in low-technology manufacturing industries. At the firm level, the effect is stronger for firms with lower levels of human capital, weaker innovation capacity, and lagging digital transformation. Overall, this study provides micro-level evidence on the resilience effects of the AI Pilot Zone policy and offers policy implications for integrating AI more effectively with the real economy. Full article

Sprint performance improves throughout adolescence as a result of both structural and neuromuscular development. In particular, increases in body height, lower limb length, and muscle volume have been consistently linked to improvements in sprint performance. However, allometric scaling of force, speed, and power remain lower in adolescents compared to adults even after structural differences are accounted for, implicating neural factors as independent contributors to performance development. The purpose of this study was to investigate differences in neuromuscular activation patterns of the lower limb muscles during maximal sprinting between youth male athletes across two age groups (U19: 17–19 years; U16: 13–16 years). Eighteen athletes performed a 50 m maximal sprint. Spatiotemporal variables (running speed, step frequency, step length) were measured over 30–50 m using a high-speed camera (240 Hz) and timing gates. Electromyographic (EMG) signals were recorded simultaneously from ten lower limb muscles using wireless EMG sensors (2000 Hz): rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gluteus maximus (Gmax), gluteus medius (Gmed), vastus lateralis (VL), vastus medialis (VM), tibialis anterior (TA), gastrocnemius (GAS), and soleus (SOL). Root mean square (RMS) amplitude was calculated across four gait phases (contact, early-swing, mid-swing, late-swing) and normalised to maximal voluntary Isometric contraction (%MVIC). The U19 group demonstrated significantly greater running speed (U19: 9.49 ± 0.39 vs. U16: 8.67 ± 0.25 m·s⁻¹, p < 0.001), step frequency (U19: 4.49 ± 0.12 vs. U16: 4.35 ± 0.16 Hz, p = 0.004), and step length (U19: 2.12 ± 0.12 vs. U16: 1.99 ± 0.06 m, p = 0.010) than U16. The overall pattern of lower limb muscle activation across the gait cycle was broadly similar between groups; however, a significant group × phase interaction was observed for RF (p = 0.003, F = 5.257, η² = 0.247), with post hoc analysis revealing greater RF activation during early swing in U19 (p = 0.033). These findings may indicate that sprint-specific training in youth athletes is associated with not only structural but also neuromuscular differences, specifically reflecting enhanced RF recruitment during the phase-critical moment of early swing—a window in which high-threshold motor unit activation is most mechanically decisive. EMG-based assessment of hip flexor activation during maximal sprinting may provide a complementary tool, pending further validation, for talent identification and training prescription in youth track and field. Full article

The integration of Artificial Intelligence (AI) into human resources management is driving a profound transformation in the evolution of management, and even more so in the management of human talent, which is the primary resource of any organization. This research provides an in-depth analysis of the impact of AI on core human resource management processes, covering the automation of operations that enables the exploration of dimensions such as talent acquisition, training, potential development, mental well-being, strategic workforce planning, job design, diversity, compensation, equity and inclusion, change management, culture and sustainability. The purpose of this study is to systematically synthesize the existing evidence on the impact of artificial intelligence on human management processes, identifying the scientific consensus, emerging contradictions, research gaps, and implications for sustainable organizational development. A systematic review was conducted of various sources published between 2020 and 2025 from databases such as ScienceDirect and Scopus, among others, using predefined Boolean search strategies, explicit inclusion and exclusion criteria and a structured thematic synthesis narrowing down the main studies based on search criteria. It was determined how algorithms are changing the employer-employee relationship within organizations. The findings indicate that the effectiveness of AI depends on the development of a hybrid intelligence that preserves the human factor consideration. It is concluded that AI enables the optimization of cultural change management, analytical precision, and ethical oversight—which are irreplaceable and critical human competencies in today’s digital age. This review contributes to the literature by providing a comprehensive synthesis of recent evidence, identifying unresolved research gaps, and proposing a future research agenda that will lead to the development of sustainable, responsible, and people-centered AI in human resource management. Full article

AI is currently shaping how people are being entrepreneurial by allowing the establishment of AI-native ventures. The creation of these new types of businesses builds off an infrastructure of data, computing power, and research that typically accompany advanced economies. In contrast, most developing economies are still experiencing institutional and structural barriers that inhibit the formation of new ventures and their subsequent growth. Despite the existence of research that explores some of the ways in which successful ecosystems from developed economies could be applied to developing ecosystems, there is little guidance on how to systematically adapt these successful practices in resource-constrained environments. This research uses a comparative, document-based study design to assess how to benchmark and configure AI-native entrepreneurship ecosystems across heterogeneous institutional environments. Using Switzerland and Jordan as two contrasting analytical cases, we define twelve dimensions of an ecosystem and then create comparative ecosystem profiles using a standardized coding and scoring framework. We combine dimension-level data on talent development, applied research, infrastructure, financing, governance and market access with baseline socio-economic indicators. Our results demonstrate a high level of structural asymmetry between the two ecosystems. Switzerland has a balanced and highly coordinated configuration, whereas there is a strong university anchor and demand for talent in Jordan, but there are also significant weaknesses in terms of infrastructure, financing, and industry linkages. Building from these results, we present the parameter re-weighting and the context-sensitive design model to encourage the emergence of AI-native entrepreneurship in Jordan through coordinated architecture, collaborative experimentation resources and internationalization at an early stage. This article advances both the fields of entrepreneurial ecosystems and digital entrepreneurship by framing AI-native entrepreneurship as a new form of knowledge-intensive venture creation and providing a context-sensitive approach for adapting entrepreneurial ecosystems. The results provide a document-informed basis for policymakers, academic institutions and other ecosystem actors seeking to develop AI-based innovation in resource-constrained economies. Full article

As a new engine of economic development, the digital economy is profoundly reshaping the governance structure and production models of modern agriculture and has increasingly become a significant force driving the high-quality development of agricultural export trade. Based on provincial panel data from 30 Chinese provinces spanning from 2014 to 2023, this paper employs a two-way fixed effects model to systematically examine the statistical association between the digital economy and agricultural export competitiveness. Furthermore, a mediation effects model is adopted to explore the underlying transmission pathways, and a threshold effects model is applied to reveal the nonlinear characteristics of this association, thereby providing a multidimensional analysis of the underlying mechanisms. The empirical results indicate that: (1) there is a significant positive statistical association between the digital economy and agricultural export competitiveness, and this finding remains robust across a series of robustness checks; (2) upgrading of agricultural industrial structure, agricultural technological innovation, and agricultural labor transfer serve as three important transmission pathways through which the digital economy is associated with enhanced export competitiveness; (3) this positive association exhibits regional heterogeneity, with stronger effects observed in central regions and major grain-producing areas; and (4) there is a nonlinear relationship between the digital economy and agricultural export competitiveness. Finally, based on these empirical findings, several policy implications are proposed, including strengthening digital infrastructure construction, enhancing trade openness, cultivating agricultural digital talents, optimizing factor allocation structures, and improving the digital economy governance system, so as to fully unlock the potential space of the positive association between the digital economy and agricultural export competitiveness. Full article

Against the backdrop of intensifying climate change and stricter carbon-neutrality targets, improving logistics green innovation performance (GIP) is essential for low-carbon transformation. However, existing studies largely emphasize contemporaneous linear effects and insufficiently address nonlinear interactions, lagged responses, and regional heterogeneity. This study develops a DLIA framework that integrates driver screening, lag-response diagnosis, integrated learning validation, and SHAP-based attribution. Using panel data from 30 Chinese provinces over 2011–2024, logistics GIP is measured with the Super-SBM model, while complementary correlation diagnostics identify significant drivers and their optimal lag structures. Four ensemble-learning algorithms are then compared, with XGBoost selected for explainable attribution analysis. Results show that national logistics GIP increased from 0.405 to 0.467, although pronounced spatial disparities persist and high-performance provinces remain concentrated in eastern China. Twenty-seven variables jointly influence GIP through nonlinear relationships. Highly qualified talent produces the fastest response, with an average lag of 0.60 years, whereas capital stock requires a longer accumulation period of 5.29 years. SHAP results identify economic concentration as the largest contributor (14.0%), followed by highly qualified talent (9.6%) and capital stock (8.4%). These drivers also display threshold effects and substantial provincial heterogeneity. The findings extend innovation-ecosystem research by demonstrating that logistics green innovation depends on nonlinear interactions, differentiated temporal transmission, and regional absorptive capacity. Green logistics policies should therefore shift from uniform linear interventions toward time-sensitive and place-based strategies aligned with local factor endowments and knowledge capacities. Full article

The creation of innovative learning environments in courses to provide sustained talent support has long remained a central research concern for high-quality social development. Neglected foundational skills in professional course clusters are often a hidden barrier to higher education for sustainable development (HESD). To close five persistent foundational skill gaps across improper citation (J1), ineffective figure use (J2), poor analysis (J3), irresponsible AI use (J4), and comprehensive application (J5) within the hydraulic engineering course cluster, a four-stage deliberate practice module (5Di-40Pr-5Tr-3Cm) has been embedded into a two-week hydraulic model experiment course, and its learning outcomes are systematically evaluated. A systematic analysis of its achievement levels across neglected foundational skill indicators of J1~J5 at each stage was conducted, stratified by the overall cohort and subgroups (P: objective demand, T: behavior type, G: optimization methods). The key findings include: ① deliberate practice demonstrates better teaching outcomes than lecture-based instruction, which can be evidenced in 2026, when J5’s achievement levels at the 3Cm stage yielded a moderate effect size relative to the 2025 lecture-based condition (d = 0.42); compared with the 2024 no-intervention baseline, the cumulative effect is a obvious increasing trend (d = 1.43); ② In far-transfer subgroup diagnosis, P2 (medium objective demand) shows a rank-order reversal, low at 40Pr but higher at 3Cm, and is identified as the “partial understanding” group and providing a diagnostic anchor for tiered intervention; ③ In near-transfer pathway diagnosis, J5’s low performance in 5Tr (65.35%, below overall mean of 83.09%; CV = 7%) stems from two distinct pathways: a “knowledge-deficit pathway” (max-decay subgroups) and a “processing-load pathway” (subgroups where J1, J2 do not exhibit max decay). In addition, stage-specific thresholds (40Pr: 90%, range 60~99%; 5Tr and 3Cm: 83% ± 3%, range of for 40Pr, mean = 90%, recommended range = 60~99%; for 5Tr, mean = 83% ± 3%, range = 65~96% and 75~90%) provide quantitative benchmarks for targeted intervention. These cumulative findings are intended to advance the evaluation paradigm of engineering practice courses from “total score attainment” toward “structural diagnosis” and align with the competency-oriented philosophy of higher education for sustainable development (HESD). Full article

Background: Cultivating medical students’ innovative behavior is crucial for talent development in medical education. However, the factors associated with medical students’ innovative behavior are still unclear. The aim was to examine factors associated with self-reported online innovative behavior of medical students. Methods: A cross-sectional electronic survey was conducted at a public medical university in China. A total of 371 medical undergraduates participated in the study. Data were collected with questionnaires on students’ abilities scales and analyzed with SPSS 26 and AMOS 26. The associations among constructs were analyzed through structural equation modeling. Results: Results showed that both digital resilience and technological self-efficacy were positively associated with medical students’ self-reported online innovative behavior. Statistically significant indirect associations were found between digital resilience and self-reported online innovative behavior through online learning engagement and self-directed learning behaviors. Statistically significant indirect associations were also found between technological self-efficacy and self-reported online innovative behavior through online learning engagement and self-directed learning behaviors. Conclusions: Digital resilience and technological self-efficacy were directly and indirectly associated with medical students’ self-reported online innovative behavior in online learning environments. The results provide useful empirical insights for understanding students’ self-reported online innovative behavior in online learning environments. Full article

Green smart city development increasingly depends on cooperation between public authorities and enterprises, yet existing research has not adequately explained how government-side enabling conditions and enterprise-side capabilities are structurally connected. This study identifies the key factors affecting green smart city development from the perspective of government–enterprise cooperation and examines their causal relationships, hierarchical structure, driving power, and dependence. A systematic literature review and Delphi consultation with ten experts from government agencies, enterprises, and universities in China were used to establish a framework of ten factors. The enterprise factors include technologies, innovation, responsibilities, management, and talents, while the government factors include databases, platforms, infrastructure, funds, and policy system. An integrated DEMATEL-ISM-MICMAC approach was then applied to analyze the relationships among these factors. The results show that policy system and funds are the fundamental driving factors, infrastructure and talents serve as key transmission factors, and technologies and innovation are highly connected but mainly dependent on upstream conditions. Responsibilities show relatively weak structural connectivity and become more influential when embedded in formal institutional and managerial mechanisms. This study develops a unified analytical framework that links government enabling conditions with enterprise implementation capabilities, clarifies the internal structure of green smart city development, and provides practical guidance for improving policy coordination, financial support, digital foundations, and enterprise capacity in China and other comparable contexts. Full article