Search Results (917)

Background and Objectives: Metacognition-related processes (e.g., confidence calibration, self-evaluation and the use of feedback) have been linked to cognitive insight, self-evaluation, and daily functioning in psychosis. However, clinic-based assessments only provide limited information. Digital methods may capture state-like variations and contextual factors, but it is unclear to what extent they operationalise core metacognitive monitoring constructs versus adjacent self-evaluative/insight-related constructs. We mapped digital approaches used to assess metacognition-related constructs across the psychosis spectrum, summarising the associated feasibility and validity. Materials and Methods: We conducted a scoping review (PRISMA-ScR) of psychosis-spectrum studies that used digital tools to assess metacognition-related targets. These included ecological momentary assessment/experience sampling (EMA/ESM), task-based paradigms with confidence ratings, and hybrid approaches. Searches covered MEDLINE (via PubMed), Scopus, and IEEE Xplore, with the final search run on 15 December 2025. We charted constructs, operationalisations, feasibility/engagement indices and reported links with clinical or functional measures. Results: The empirical evidence map comprised 13 studies directly assessing metacognition-related constructs; eight additional implementation/methodological sources were synthesised separately to contextualise feasibility, reporting, ethics, and governance. EMA studies more often assessed adjacent self-evaluative constructs, including context-linked self-appraisal bias, conviction, and self-report–context mismatch in daily life, whereas task-based studies more directly assessed confidence–accuracy calibration and feedback updating. Across EMA studies, greater momentary symptom severity and more restricted contexts were often associated with inflated self-evaluations and divergence from observer-rated functioning. Task-based studies indicated that confidence calibration and feedback utilisation may diverge from objective performance; in performance-controlled paradigms, some studies reported comparable metacognitive sensitivity/efficiency, but the overall evidence remains uncertain. Passive sensing was common in psychosis research but was rarely explicitly tied to metacognitive constructs. Conclusions: Current digital work spans both core metacognitive monitoring constructs and adjacent self-evaluative/insight-related constructs, rather than a single unitary construct. Clinical translation remains hypothesis-generating: interpretability may be improved by combining clinical anchors, low-burden EMA, and optional contextual streams, but thresholds, workflows, and signal-action rules require prospective validation. Full article

Since 2000, the flipped classroom model has been widely adopted in higher education within the context of digital transformation; however, a comprehensive historical synthesis of review evidence remains limited. This study addresses this gap by conducting a review of reviews to clarify developmental trends, theoretical foundations, instructional designs, research methods, outcome variables, reported effects and implementation challenges, with the aim of informing sustainable education practices. Following the PRISMA framework, we systematically searched Web of Science Core Collection, Scopus, and Google Scholar for studies published between 2000 and 2024. Predefined inclusion and exclusion criteria were applied, and 25 systematic reviews met the eligibility requirements. Risk of bias and reporting quality were assessed descriptively at the review level. The results indicate generally positive perceptions among students and teachers, particularly regarding learning performance, collaboration and motivation, with the strongest evidence observed at the teaching and learning levels rather than at pedagogical or institutional levels. Substantial variation in flipped classroom designs and inconsistent reporting limited cross-study effect size synthesis. Further analysis reveals a fragmented theoretical basis and uneven attention to post-class learning processes. In response, two integrative frameworks—the Instructional Design Analysis Model for Flipped Classrooms (IDAMFC) and the Transformative Activation Theory for Flipped Classrooms (TAT-FC) are proposed to align pre-, in-, and post-class phases with learning strategies, cognitive engagement, and assessment in digitally supported environments. This study highlights the need for longitudinal designs and more comprehensive outcome measures to support sustainable educational development. Full article

Background/Objectives: Preterm and critically ill neonates in neonatal intensive care units (NICUs) require multiple medical devices, including incubators, radiant warmers, phototherapy systems, and patient monitors. The coexistence of standalone devices without interoperability increases cognitive and operational burdens for healthcare providers and leads to spatial inefficiency. This study aimed to develop and evaluate a multi-functional neonatal incubator integrating these core functions into a single platform, using user-centered design (UCD) and usability engineering principles. Methods: By synthesizing and analyzing international standards (ISO 13485, IEC 62366-1, IEC 62366-2, and ISO 9241-210), a four-phase design process was established. Following the development of the monitoring system, the design was iteratively refined and validated through repeated formative usability evaluations. A summative usability evaluation was then conducted with 20 NICU clinicians in a simulated NICU environment, using 13 scenarios comprising 39 tasks. Outcome measures included task success rate, the After-Scenario Questionnaire (ASQ), the NASA Task Load Index (NASA-TLX), and the System Usability Scale (SUS). Results: The overall task success rate was 95.64%. When analyzed by function, success rates were 94.63% for incubator-related tasks, 98.33% for patient monitoring, 96.67% for radiant warmer tasks, and 98.33% for phototherapy tasks. The mean SUS score was 78.63, exceeding the benchmark score of 68 that indicates good usability. In addition, no statistically significant differences were observed in workload (NASA-TLX) or usability (SUS) scores according to clinical role or length of clinical experience. Conclusions: The multi-functional neonatal incubator developed in this study demonstrated high usability despite the integration of multiple medical device functions. The findings suggest that this integrated system has the potential to enhance clinical workflow efficiency, optimize spatial utilization, and improve patient safety in NICU settings. Full article

Against the dual backdrop of the global drive toward carbon peaking and carbon neutrality, a core pillar of the United Nations Sustainable Development Goals (SDGs), and the accelerated integration of new-generation digital technologies into sustainable production practices, this study employs a micro-level perspective to systematically explore how AI innovation optimizes organizational, production, and investment structures to enable corporate low-carbon development. The study sample comprises 21,428 firm-year observations from Chinese A-share listed manufacturing companies over the period of 2010–2022. The results show that AI innovation can significantly reduce corporate carbon emission intensity, specifically achieving corporate low-carbon development through three paths: optimizing low-carbon organizational governance, upgrading emission-reducing production processes, and reorienting investment toward green assets. Further analysis shows that executives’ green cognition and government environmental attention together constitute dual internal and external driving forces for corporate carbon emission reduction. Heterogeneity analysis reveals that the emission reduction effect of AI innovation is more significant for enterprises with a low supply chain concentration, those in high-environmental-sensitivity industries, and those located in regions with underdeveloped factor markets. From the micro-perspective of corporate sustainable low-carbon development, this study offers further theoretical support and empirical evidence for regulators aiming to optimize AI innovation incentives, improve sustainable environmental governance, and advance global sustainable industrial development. Full article

Traditional cognitive diagnosis models typically rely on expert-annotated Q-matrices to define the relationship between exercises and knowledge concepts. This process is not only highly subjective and costly, but also prone to introducing noise and bias, which directly affects diagnostic accuracy. Meanwhile, most existing deep learning-based methods overlook the rich semantic information contained in concept descriptions, making it difficult to deeply model the intrinsic relationships among knowledge points, resulting in limited interpretability of the models. To address these issues, this paper proposes a cognitive diagnosis model that incorporates key textual information from concept descriptions to refine the Q-matrix (KECQCD). The core innovation of the model lies in leveraging the pre-trained language model RoBERTa to encode concept texts, fusing semantic features with identifier embeddings through a gating mechanism to construct semantically-enhanced concept representations. It designs a concept-exercise heterogeneous information network and employs a graph attention mechanism to adaptively aggregate node features, explicitly modeling high-order knowledge dependencies. Furthermore, a multi-task joint learning framework is established to predict student performance while dynamically correcting association errors in the initial Q-matrix. Experimental results on the public Junyi dataset show that the KECQCD model significantly outperforms mainstream baseline models across multiple metrics, including accuracy (ACC), area under the curve (AUC), and root mean square error (RMSE). Ablation studies confirm the effectiveness of each core module, and diagnostic consistency (DOA) evaluation further demonstrates the enhanced interpretability of the model’s outcomes. This research offers a new solution for building accurate, reliable, and interpretable cognitive diagnosis systems, contributing positively to the advancement of personalized intelligent education. Full article

Artificial intelligence (AI) can help students accelerate assignment completion, but it may also foster cognitive outsourcing and learning detached from authentic contexts. This paper presents E3-HOT, a conceptual framework that leverages embodied intelligence to sustain learners’ cognitive agency and higher-order thinking for sustainable learning, aligned with SDG 4 (Sustainable Development Goal 4) and its emphasis on inclusive and equitable quality education and lifelong learning. Using an iterative conceptual synthesis, we distill three embodied pathways—situational embedding, embodied participation, and cognitive creation—and translate them into a practical system design with a three-module E3 core. It includes a virtual–real integrated learning environment for rich scenarios, embodied interaction for action and sensing, and an intelligent core that provides bounded and teacher-controlled support. To facilitate equitable adoption across resource-diverse settings, we specify multi-fidelity enactment options and an auditable set of evidence artifacts for subsequent expert review and future validation studies. We further provide an illustrative university human–AI design project that outlines a week-by-week workflow and corresponding evidence plan, presented as a worked example rather than a report of an implemented study. E3-HOT offers a traceable design-and-evidence blueprint without claiming measured learning gains. Full article

Global urbanization has long been hampered by the “metrocentric priority” paradigm, with small towns—core hubs for urban–rural integration—severely undervalued in practical value. Amid China’s transition to high-quality urban–rural integration, unbalanced small town development has become a critical bottleneck for county-level factor flows, demanding systematic research to unlock their strategic value and resolve urban–rural dual predicaments. Existing studies suffer from scientific gaps including unidirectional linear cognition, insufficient complex system thinking, and weak interpretation of regional heterogeneity, remaining at the stage of static correlation description and failing to reveal the two-way reciprocal feedback logic between small towns and urban–rural integration. Meanwhile, the application of complex system theory in urban–rural research is still confined to theoretical narratives, which hinders the advancement of research from descriptive analysis to mechanism interpretation. Taking Henan Province (a typical agricultural and populous province reflecting China’s urban–rural development) as a case, this study builds a “local emergence–global synergy” framework based on complex system theory, establishes a dual indicator system for small towns’ multidimensional performance and county-level urban–rural integration, and integrates spatial statistical analysis, bidirectional regression and coupling coordination models to explore their cross-scale spatiotemporal evolution and reciprocal feedback during 2019–2023. Findings show the following: (1) The multidimensional performance of small towns presents a pattern characterized by polarized expansion of high-value regions and overall improvement of low-value regions, while county-level urban–rural integration evolves into a polycentric structure featured by central dominance and southern growth. (2) There is a significant two-way asymmetric relationship between small towns’ multidimensional performance and county-level urban–rural integration: the positive effect is significantly stronger than the reverse effect, and both direct impacts are significantly weakened after introducing economic variables, indicating that economic development serves as a key transmission channel. (3) The coupling mechanism presents three evolutionary paths with pronounced core–periphery spatial heterogeneity. Grounded in complex system theory, this study constructs a systemic analytical framework of “local emergence of small-town subsystems and global synergy of county-level systems”, verifies the core proposition of two-way interactions between subsystems and the overall system in the urban–rural complex giant system, and enriches the localized application of complex system theory and the urban–rural continuum theory in traditional agricultural regions of China. This study provides a foundational empirical paradigm for the in-depth exploration of nonlinear characteristics and threshold effects in future research. It offers theoretical support for policy formulation of county-level urban–rural integration in traditional agricultural regions of China, and it provides Chinese experiences for the Global South with similar contexts to explore inclusive urbanization pathways, promoting cross-cultural dialogue and practical transformation of urban–rural integration theory. Full article

Green production technologies are pivotal for achieving agricultural ecological sustainability; however, farmers’ adoption intention remains sluggish under current policy frameworks. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to build a policy regulation–cognitive transformation–intention analytical framework. Based on 498 survey responses collected from June to October 2024 in Guizhou Province, Structural Equation Modeling (SEM) and the DEMATEL method were employed to quantify influence paths and causal attributes. (1) The results reveal that policy regulation, perceived usefulness, perceived ease of use, behavioral attitude, subjective norm, and perceived behavioral control all have notable direct positive impacts on farmers’ intention to adopt eco-friendly agricultural technologies. (2) Perceived usefulness plays a pivotal role in the direct impact path, while perceived ease of use exerts the strongest indirect influence, driving farmers’ ultimate adoption intentions by being transformed into perceived usefulness and positive attitudes. (3) Based on the causal network analysis, policy regulation is identified as the core source factor with the highest centrality, and it provides foundational support by driving key mediating factors such as behavioral attitudes, Subjective Norms, and perceived behavioral control. Consequently, this study proposes policy recommendations, such as optimizing policy formulation, enhancing the pragmatic perception of technological usefulness, dismantling behavioral and cognitive barriers, and eliminating resource bottlenecks, to provide decision-making references for the green transformation of agriculture. Full article

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

Background/Objectives: Observational learning allows people to acquire new skills by observing the actions of others embedded in their social environment. From childhood, observational learning is a central process in human cognitive development, playing a crucial role in the acquisition of complex skills. Children and adults with autism spectrum disorder (ASD) often exhibit deficits in what are considered prerequisites for observational learning to occur (i.e., attending, imitation, delayed imitation, consequence discrimination). Considering this, the present review examined the literature on the complex and timely question of whether individuals with ASD can learn by observation, while accounting for the social versus non-social nature/content of the tasks. Methods: This work was a narrative review aimed at providing an overview of published studies in which observational learning was analyzed in individuals with ASD. Twenty-two studies met the inclusion criteria and were eligible for this review. Results: The core findings indicate that individuals with ASD may be able to learn by observing others, especially when taught the prerequisites for observational learning. Furthermore, the findings indicate that observation may be an effective way to expand the typically restricted and circumscribed interests of children with ASD and to increase emotion recognition skills. Conclusions: Overall, these findings have significant educational, clinical, social, and economic implications, supporting the use of observational learning strategies for both social and non-social skills to reduce reliance on expensive one-on-one teaching and to address some of the core deficits of ASD. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article

As pioneers in the reform of higher education in China, China’s new-type universities, often referred to as the fourth generation of universities, play a crucial role in driving the iteration of educational concepts and innovation in planning and design through their campus construction. As an emerging campus type, existing research largely focuses on planning and design schemes and the static form of campus space, lacking a systematic exploration of its historical dynamic evolution and core influencing factors. This study uses Southern University of Science and Technology (SUSTech), which is a typical example of this new type of university, as a case study to analyze its spatial evolution characteristics, core driving factors, and spatial shaping mechanisms, considering the interactions among multiple stakeholders from the perspective of dynamic campus spatial development. It comprehensively utilizes literature and archive analysis, drawing and image comparison, and field research to systematically trace the entire lifecycle of SUSTech’s campus planning and construction. By combining cognitive maps and questionnaire surveys, it can explore the spatial imagery characteristics of the completed campus, analyze the key influencing factors of its spatial evolution, and propose critical thinking on related issues. It finds that SUSTech’s campus spatial form gradually took shape through a game of radical and eclectic ideas, exhibiting a dual characteristic of innovative pursuit and practical adaptation in terms of site attitude, innovative educational concepts, and planning and design concepts. Spatial evolution is the result of the combined effects of the demands of multiple stakeholders, changes in educational concepts, and the urban development context. This also reflects problems such as an imperfect consultation mechanism, inconsistent planning concepts, and insufficient functional adaptability of architectural images, which hinder the effective implementation of strategies for optimizing campus spaces in the context of China’s higher education transformation. This study reveals the inherent laws governing the dynamic evolution of new university campus spaces during the historical stage of China’s higher education transformation, providing theoretical and practical support for the planning, construction, and operational optimization of similar campuses. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article