Search Results (2,014)

Large language models (LLMs) are increasingly used to predict human behavior from plain-text descriptions of experimental tasks that range from judging disease severity to consequential medical decisions. While these methods promise quick insights without complex psychological theories, we reveal a critical flaw: they often latch onto accidental patterns in the data that seem predictive but collapse when faced with novel experimental conditions. Testing across multiple behavioral studies, we show these models can generate wildly inaccurate predictions, sometimes even reversing true relationships, when applied beyond their training context. Standard validation techniques miss this flaw, creating false confidence in their reliability. We introduce a simple diagnostic tool to spot these failures and urge researchers to prioritize theoretical grounding over statistical convenience. Without this, LLM-driven behavioral predictions risk being scientifically meaningless, despite impressive initial results. Full article

Regional Integrated Energy Systems (RIESs) are pivotal in the low-carbon transition of energy-intensive hospital campuses. However, traditional multi-objective optimization for RIES planning often suffers from the subjective selection of weights, leading to configurations that lack robustness against varying decision-maker preferences. To address this, this paper proposes a robust optimization methodology integrating shadow cost theory and multi-scenario weight scanning. A high-fidelity dynamic simulation model of a hospital in Beijing was constructed using TRNSYS. By monetizing environmental externalities into shadow costs, a comprehensive objective function, including annual cost savings rate, primary energy savings rate, and environmental shadow cost savings rate, was established, and the Hooke–Jeeves algorithm was employed to scan ten distinct weight scenarios, ranging from profit-driven to eco-centric preferences. The results reveal that solar collectors lack economic competitiveness under current boundary conditions due to cooling–heating coupling constraints. Instead, a configuration featuring a large-capacity gas turbine (2790 kW) coupled with a moderate GSHP was identified as the optimal solution in over 80% of the scenarios, demonstrating high preference robustness. Crucially, this configuration achieves net-negative emissions by maximizing clean power exports to displace carbon-intensive grid electricity. Compared to the reference system, the optimized RIES reduces primary energy consumption by 82.7% and achieves substantial environmental benefits, subject to grid emission factors. These findings confirm that prioritizing clean power export is a resilient pathway for hospitals to balance economic feasibility with environmental goals under current policy frameworks, providing scientific guidance for policymakers and engineers. Full article

For too long, schools and other organizations have relied on what have come to be known as technical, rational, or functional approaches to leadership, stemming in the global north, from scientific management in the early 20th century. Schools have been hierarchically organized in the belief that only the “managers” were smart enough to know what is needed, with others (including teachers and students) needing to obey and comply with established regulations, curricular standards, and assessment tools. If we want more equitable, inclusive, and just outcomes, it is time for school leaders to reject the dominant scientific leadership paradigm and to employ a new paradigm, with new axiology, ontology, epistemology, and methodology focused on values, community, and cultures. Transformative leadership theory is described as an exercise of power and authority that begins with questions of justice and democracy. It critiques inequitable practices and offers the promise not only of greater individual achievement but of a better life lived in common with others. This article reviews some of the origins of transformative leadership theory, identifies some of the most common approaches and principles, and argues that by fundamentally reorienting leadership towards justice, democracy, and equity, transformative leadership theory provides the essential ethical and practical framework for education in a fractured world. Full article

Addressing the scientific problem that the profile modification design of tapered roller bearings primarily focuses on contact stress and fatigue life while neglecting its impact on wear evolution, this paper, based on Hertzian contact theory and the Archard wear theory, and considering centrifugal force, gyroscopic effect, and the complex contact state between rollers and raceways, constructed a comprehensive analysis framework integrating a quasi-static model for profiled rollers and a wear depth calculation model. This framework is novel in that it systematically couples roller profile modification parameters with raceway wear evolution under both pure axial and combined radial–axial loads. The validity and effectiveness of the proposed model were verified by comparing the results of the quasi-static model with load distribution data from existing literature and through measurements conducted on a specially designed bearing wear test platform. The main findings are as follows: (1) When the logarithmic modification parameter f₁ increases from 0.7 μm to 3.6 μm, the maximum wear depth of the inner raceway increases by 133% under pure axial load and 144% under combined load, while that of the outer raceway increases by 142% under pure axial load and expands from 0.1–0.2 μm to 0.23–0.52 μm under combined load. (2) Combined load induces significant asymmetric wear on the outer raceway, and the difference between the two wear peaks increases from 0.13 μm to 0.35 μm as f₁ rises from 0.7 μm to 3.6 μm. (3) The wear peak shifts toward the midpoint of the roller generatrix with increasing modification amount. These results provide important guidance for the wear-oriented optimization design of tapered roller bearings. Full article

Fish occupy pivotal trophic positions in aquatic ecosystems, mediating energy transfer and shaping community structure through their feeding interactions. Unraveling these dietary relationships is therefore fundamental for understanding ecosystem functioning and supporting sustainable fisheries management. Traditional morphological analyses, while informative, often fall short in resolving fine-scale prey diversity and trophic linkages. In contrast, DNA metabarcoding has revolutionized dietary studies by enabling comprehensive, high-resolution, and non-invasive characterization of prey assemblages. This review synthesizes recent progress in applying DNA metabarcoding to fish trophic ecology, emphasizing technical innovations, methodological standardization, and ecological insights. We discuss how DNA metabarcoding has advanced the understanding of food web complexity, species interactions, and ecological responses to environmental change. However, challenges persist in quantification accuracy, reference database completeness, and cross-source contamination. Future research integrating multi-marker approaches, standardized workflows, and multi-method integration holds promise for transforming DNA metabarcoding into a powerful, reliable and mechanistic tool for trophic ecology. Collectively, these developments will bridge molecular data with ecological theory, strengthening the scientific foundation for ecosystem-based fisheries management. Full article

In the present qualitative study, we first synthesize research to clarify the concept of error in science as developed by epistemologists, philosophers, and historians. We also examine the concept of error in educational science, drawing from studies on science learning and teaching. To do this, we analyzed references found through a systematic review of books and journals. We also selected published articles on the history of physics and chemistry and consulted documents authored by scientists in English or in official translations. We guided our selection by choosing sources relevant to conceptualizing error in scientific and educational contexts. Our key findings show two categories of scientific error: those that have contributed to scientific progress and those that have hindered it. Some renowned scientists, such as Aristotle and Buridan, put forward theories of force and movement that were later shown to be false. However, these errors did not always impede scientific advancement. This research highlights how scientific errors have shaped the evolution of science and reveals insights into the scientific process and the resilience of the scientific community. In science education, researchers use various terms such as “student naïve reasoning,” “students’ alternative conceptions,” “students’ alternative theory,” and “misconceptions.” Students’ errors, like scientific errors, can be classified into two categories. The first type involves errors from distractions, misunderstandings, or unintentional mistakes. The second type results from students’ interactions with many natural and man-made phenomena, the common language used in society (which differs from scientific language), and errors passed down by teachers or found in textbooks. Finally, we note that identifying errors among scientists and students supports the development of strategy-based teaching for meaningful science learning. From this perspective, students will be pleased to know that some of their conceptions of force and motion are “similar” to those developed by Aristotle and Buridan, even if these conceptions are false relative to those developed by Galileo and Newton. Recognizing both scientists’ and students’ errors is essential for creating teaching strategies that promote deeper science learning. Full article

Magnetorheological elastomers (MREs) have attracted considerable attention in high-precision sensing and intelligent control due to their responsive sensitivity. The magnetostrictive properties of MREs excited by magneto-mechanical coupling at the mesoscopic scale show broad application potential but have not yet been fully elucidated. In this study, the magnetostrictive properties were investigated at the mesoscopic scale through theoretical modeling, numerical simulation and experimental research. A correction factor was introduced to address the limitations of conventional magnetic dipole theory under near-field conditions, thereby providing a rational theoretical explanation of magnetostrictive behavior. Visualization analysis was performed using the finite element method (FEM). Subsequently, MREs were prepared under various solidified magnetic fields, and their performance was validated through scanning electron microscopy (SEM) and a laser displacement sensor. The results demonstrated that magnetostriction is determined by the relative angle between the particle chain and the magnetic field direction. The linearity of the particle chain was found to be positively correlated with magnetostriction. The maximum theoretical and experimental magnetostrictive elongations reached 0.9% and 0.565%, respectively, while the maximum theoretical and experimental magnetostrictive compression reached 2.77% and 1.81%, respectively. This work provides significant scientific insights into the magneto-mechanical energy conversion mechanism and contributes to the development of magnetostrictive instruments. Full article

This article presents a systematic, comparative, and reproducible mapping of the scientific literature examining how the term glocalization is mobilized in contemporary research. We analyze a corpus of 2200 articles (1990–September 2025) from Web of Science and Scopus and report in line with PRISMA 2020 and PRISMA-ScR (scoping). Methodologically, we combine PRISMA-ScR-aligned screening with the bibliometric and lexicometry mapping of metadata, complemented by the qualitative interpretation of a purposive subset of key texts to contextualize the mapped trajectories. Following normalization, we apply co-occurrence and correspondence analyses to identify lexical proximities and cross-disciplinary translation zones. We ask how glocalization is conceptualized, the contexts in which it is deployed, and whether classic theoretical frameworks retain their explanatory power. Findings show a gradual shift from a mainly conceptual to more operational register. Regional differences are marked yet organized around a core of governance, multilevel coordination, and collective practice. The disciplinary landscape shows continuity between techno-applied and reflexive approaches, supported by bridging disciplines (sociology, communication, education, health, sport) that translate a general grammar into research and intervention tools. We offer a cumulative methodological framework for tracing the scientific trajectory of glocalization. As a bridge concept between theory and action, glocalization provides an explanatory lens on transformations and resistances in early-twenty-first-century globalized society. We therefore position this study as a PRISMA-traceable corpus construction combined with bibliometric and lexicometry science mapping. Full article

Grounded in knowledge recombination theory and innovation tension theory, this study develops a novel measurement framework for scientific and technological innovation (STI) risks that captures the dynamic and systemic equilibrium between novelty and adaptation. We first analyze the endogenous mechanisms through which STI risks emerge from knowledge recombination processes, and then propose a classification framework for knowledge recombination, along with quantifiable metrics for novelty and adaptation. Next, we introduce a risk classification system for STI and corresponding quantitative evaluation metrics, facilitating dynamic monitoring of innovation risk states. Finally, we validate the framework through an empirical case study in natural language processing (NLP). Our results reveal a persistent innovation tension within the STI system between novelty and adaptation. Emerging phrases and reinforced phrases demonstrate distinct risk profiles and distribution patterns, corresponding to differentiated structural and evolutionary regimes. These differences stem from their distinct mechanisms in the novelty–adaptation interaction within a complex innovation system. Specifically, in emerging phrases, novelty shows a stable positive linear correlation with Z-score, while adaptation exhibits a significant negative linear correlation with Z-score. In reinforced phrases, novelty displays a significant bimodal association with Z-score, and adaptation demonstrates a robust inverted U-shaped relationship with Z-score. Emerging knowledge combinations show significantly higher risk scores than reinforced combinations, with high-novelty–low-adaptation combinations consistently in the highest risk quantile across stages. Moreover, the risk threshold for emerging phrases increases monotonically across developmental phases. Thus, our framework advances innovation risk assessment from static categorization to dynamic, system-level evaluation, enabling tiered risk management and optimized resource allocation for high-potential innovation pathways. Full article

To improve the fire safety performance of fire protection renovation projects for existing public buildings, this paper systematically sorts out and analyzes relevant research studies, accident reports, and fire protection renovation codes and guidelines. It constructs a fire performance evaluation system for such projects, including 4 first-level indicators—”Building Characteristics”, “Building Fire Protection and Rescue”, “Fire Facilities and Equipment”, and “Heating, Ventilation, Air Conditioning (HVAC) and Electrical Systems”—and 19 second-level indicators such as “Building Usage Function”. The subjective–objective combined weighting method of Analytic Hierarchy Process (AHP)-CRITIC is adopted to determine the weights of indicators at all levels. Four high-weight second-level indicators are selected as core remediation objects: average fire load density, floor layout, automatic fire alarm and linkage control system, and electrical systems. Meanwhile, the evaluation system is converted into a Bayesian Network model, with an empirical verification analysis carried out on a shopping mall in Chaoyang District, Beijing, as a case study. Results show that the approach of combining partial codes with the rectification of high-weight indicators can reduce the fire occurrence probability of the mall from 78%, before renovation, to 24%. Therefore, the constructed evaluation system and Bayesian Network model can realize the accurate quantification of fire risks, provide scientific and feasible technical schemes for the fire protection renovation of existing public buildings, and lay a foundation for enriching and improving fire protection assessment theories. Full article

This study tackles the critical challenges of stress evolution and pillar optimization in underground potash mining, with a focus on the 351-stope of Kaiyuan Mining in Laos. Integrating theoretical calculations, large-scale 3D numerical modeling, and an AHP-Fuzzy comprehensive evaluation, we systematically analyze the complex mechanical behaviors of the mining environment. Applying key stratum theory, we reveal the unique mechanism by which overlying hard rock bends without fracturing in carnallite layers under room-and-pillar conditions. Comparative numerical simulations of four pillar-width schemes—involving 8 m rooms with 10 m, 8 m, 6 m, and 4 m pillars—show that reducing pillar width markedly increases vertical stress, triggers exponential roof subsidence, and expands pillar failure zones. Using an AHP-Fuzzy method that incorporates safety, technical, and economic factors, the Simultaneous Backfilling with 8 m Mining and 6 m Pillar Retention is identified as the optimal scheme. This configuration demonstrates superior stability, exhibiting an average pillar stress of 9.3 MPa and only limited plastic failure zones at pillar ends. These findings offer a robust scientific and technical foundation for enhancing the safety, efficiency, and sustainability of underground potash mining operations. Full article

Community public sport facilities are core carriers of the national fitness public service system, with their supply–demand alignment directly linked to megacity governance efficiency and residents’ well-being. To address structural issues, such as “human–land imbalance” in facility layout, this study uses the 2010–2024 panel data from Shanghai’s 16 districts, applies supply–demand equilibrium theory, and integrates quantitative methods to analyze spatio-temporal supply–demand coupling and identify key influencing factors. The study yields four key findings: (1) The spatial distribution of facilities and population demonstrates a differentiated evolutionary trajectory marked by “central dispersion and suburban stability”. (2) Supply–demand alignment has continuously improved, as evidenced by the increase in coordinated administrative districts from six to thirteen. Nonetheless, the distance between sports facilities and population centers widened, suggesting that spatial adaptation remains incomplete. (3) Urban population growth exerts a significant positive impact on facility supply. Elasticity coefficients are generally high in suburban areas, while negative elasticity is detected in some central urban areas due to population outflow. (4) Facility construction intensity and residential activity intensity are core driving factors, with economic conditions, transportation infrastructure, and housing prices acting as key supporting factors. This study overcomes traditional aggregate-quantity research limitations, reveals megacity facility supply–demand “spatial mismatch” dynamics, and provides a scientific basis for targeted public sports facility layout and refined governance. Full article

Teacher education represents a global strategic priority for improving educational systems and fostering inclusive, high-quality processes. Recent studies highlight the need for systematic and replicable education models capable of addressing the challenges of contemporary complexity and bridging the gap between theory and practice. Teaching professionalism is increasingly recognized as a key driver of change, requiring a balance of pedagogical, relational, and technological competences, along with strong reflective capacity. Within this framework, practicum programs play a crucial role for the development of professional identity and authentic teaching skills. Methods: This contribution adopts a theoretical–argumentative approach grounded in a critical analysis of the international scientific literature on teacher education, with specific focus on the role of practicums. The aim is to present the model implemented by Pegaso University in the context of practicum activities within initial teacher education programs to outline an interpretative framework and provide pedagogical reflections in light of the results arising from critical reflection and systematic monitoring (not covered in this specific contribution) of the effectiveness of the model implemented in the first two training cycles (academic years 23–24 and 24–25), with the involvement of 5 regions and a total of 2834 teachers in the first cycle and 10 regions and a total of 5551 teachers in the second cycle. Convenience sampling based on a non-probabilistic method was adopted, using the entire sample of teachers admitted to the training program who met the requirements of Article 7 of the Decree of the President of the Council of Ministers (DPCM). Results: This paper outlines the theoretical and methodological trajectories of the model, offering interpretative frameworks and pedagogical reflections in light of the outcomes achieved during the initial implementation phase. Conclusions: In accordance with recent national and European regulatory frameworks, the Pegaso teaching model is presented as an example of good practice for initial teacher education. It aims to foster a reflective, situated, and responsible teaching professionalism, moving beyond traditional approaches toward a continuous and transformative learning process. Full article

Laboratory mice are the most widely used model organisms in biomedical and behavioral research, yet growing concerns regarding reproducibility and translational validity have highlighted the substantial influence of housing and husbandry conditions on experimental outcomes. Although domestication is often assumed to have rendered laboratory mice fully adapted to artificial environments, evidence from ethology indicates that many core behavioral and physiological needs remain conserved. As a result, standard laboratory housing may generate chronic stress, alter behavior, and introduce systematic bias into experimental data. This narrative review critically examines how ethological constraints persisting after domestication interact with key environmental factors, social housing, environmental enrichment, ambient temperature, and lighting regimes to shape welfare and experimental validity in laboratory mice. Rather than providing an exhaustive overview of mouse behavior, the review adopts a problem-oriented and solution-focused approach, highlighting specific welfare-related mechanisms that can distort behavioral and physiological readouts. Particular attention is given to social isolation and aggression in male mice, the role of nesting material in mitigating thermal stress, and the effects of circadian disruption under standard and reversed light–dark cycles. By integrating ethological theory with laboratory animal welfare research, this review argues that housing conditions should be regarded as integral components of experimental design rather than secondary technical variables. Addressing welfare-related bias through evidence-based refinement strategies is essential for improving reproducibility, enhancing data interpretability, and strengthening the scientific validity of mouse-based research. Full article

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise in scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today’s AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: these countries account for a large share of global GDP and energy use and are therefore expected to be most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth. Full article