Search Results (1,845)

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration. Full article

The rapid expansion of digital infrastructure is creating a growing need to reconcile computing demand with urban environmental goals. Yet whether greener computing facilities can contribute to the joint advancement of digitalization and green development remains insufficiently understood. This study evaluates China’s National Green Data Center (NGDC) pilot and its implications for the collaborative development of urban digitalization and greenization (CDG). The empirical analysis draws on 284 prefecture-level cities over 2011–2023 and leverages the phased introduction of NGDCs within a multi-period DID design. The estimates indicate that NGDC implementation is associated with an approximately 5.86% increase in urban CDG relative to the sample mean, with improvements observed in both the digital and green subsystems. The mechanism results are consistent with technological innovation, computing-industry agglomeration, and urban energy efficiency as potential transmission channels. The effect is stronger in telecommunications- and Internet-sector pilots, in cities covered by national urban agglomeration policies, and in cities with greater public environmental attention. Policy-interaction analyses further identify positive interaction effects between NGDC and digital-oriented policies, including Broadband China and artificial intelligence pilot zones, whereas no statistically identifiable additional interaction effect is found for green-oriented policies, including green finance and zero-waste city pilots. These findings demonstrate that greening computing infrastructure can translate infrastructure-level efficiency improvements into broader urban digital–green transformation, while highlighting functional complementarity as the key condition for effective policy coordination. Full article

Artificial Intelligence (AI) is increasingly evaluated as both a functional technology and a socially responsive interaction partner. This study examines how academic staff form trust in educational AI and how that trust contributes to perceived value. Its central contribution is to position perceived anthropomorphism as a mediating mechanism through which perceived animacy and perceived intelligence are translated into perceived empathy. A cross-sectional survey was completed by 236 academic staff members, and the model was tested using partial least squares structural equation modeling. The measurement model demonstrated satisfactory reliability and validity. A competing-path analysis provided a more differentiated account of the proposed mediation mechanism. In the final competing-path model, Perceived Usefulness remained the strongest predictor of Perceived Value (β = 0.586), while Perceived Intelligence strongly predicted Perceived Usefulness (β = 0.570) and also had a substantial direct effect on Perceived Empathy (β = 0.465). Perceived Anthropomorphism remained positively associated with Perceived Empathy (β = 0.238). The direct effect of Perceived Animacy on Perceived Empathy was not significant (β = 0.104, p = 0.154), whereas its indirect effect through Perceived Anthropomorphism remained significant (β = 0.095, p = 0.006), indicating indirect-only mediation. For Perceived Intelligence, both the indirect effect through Perceived Anthropomorphism (β = 0.057, p = 0.020) and the direct effect on Perceived Empathy were significant, indicating complementary partial mediation. Perceived Usefulness and Trust in AI jointly explained 57.3% of the variance in Perceived Value (R² = 0.573). The findings reveal two complementary routes to valuing educational AI: a functional–cognitive route centered on intelligence and usefulness, and a social–emotional route through which anthropomorphism converts AI features into perceived empathy. Full article

Robotic gait technologies can deliver intensive, repetitive, task-oriented stepping, but multimodal platforms require careful integration before clinical testing. This concept-level technical proof-of-concept describes Li-Walk^®, an investigational fixed robotic gait-training platform combining a treadmill-synchronized lower-limb exoskeleton, dynamic body-weight support, phase-synchronized infrared-A (IR-A) thermal biofeedback, directional acoustic feedback, a semi-immersive display, camera-derived body representation, and artificial intelligence (AI)-supported therapist guidance. The architecture is defined through subsystem interfaces, implementation status, operational states, explicit risk controls, and a staged validation roadmap. No human participants, patient-level data, bench datasets, or statistical analyses were involved; subsystem factory checks did not characterize integrated performance. The installed exoskeleton has four actuated sagittal axes at the hips and knees, with in-series load cells supplying interaction-force inputs for contingent ipsilateral IR-A cueing under reduced guidance. The integrated AI module combines rule constraints with a case-based design, but has no trained statistical model or clinical cohort database; the therapist retains parameter-setting authority. Live video and avatar modes are implemented, whereas independent thermal monitoring and structured recommendation audit trails remain planned. Trigger thresholds, sampling rates, end-to-end latency, exposure limits, and fault responses remain undocumented or unverified. The contribution is a conceptual integration framework that distinguishes implemented functions, declared specifications, and unverified requirements. Independent mechanical, thermal, software, and human-factors validation, followed by appropriately authorized human studies, is required to establish safety, usability, technical reproducibility, and any incremental rehabilitation benefit. The optional thermal branch requires particular safeguards for impaired thermal sensation and cannot be assumed to add clinical value beyond robotic gait training alone. Full article

With the booming of cross-border live streaming e-commerce, this study examined the influence mechanism of cross-border live streaming interactions on consumers’ impulsive purchase intention based on the timulus–organism–response (SOR) model. Two independent psychological organism mediators are proposed, immersive experience and foreign cultural identity, and AI (artificial intelligence) enablement acts as a cross-layer moderating variable. Three core dimensions of cross-border live streaming interaction (consumer–product, consumer–consumer, consumer–anchor) are refined, and empirical tests are conducted with 620 valid questionnaires via SPSS 26.0, AMOS 28.0 and PROCESS. Results showed that consumer–product and consumer–anchor interactions exerted significant positive direct effects on impulsive purchase intention (consumer–consumer interaction’s direct effect was insignificant); both flow experience dimensions exert partial mediating effects in the paths linking consumer–product/consumer–anchor interaction to impulsive purchase intention and full mediating effects for the consumer–consumer interaction path. AI enablement positively moderates the effect of live streaming interactions on flow experience and the effect of immersive experience on impulsive purchase intention, but its moderating effect on the foreign cultural identity–impulsive purchase intention link was insignificant. This study advanced the research on cross-border live streaming consumer behavior and offered targeted practical suggestions for platform optimization with AI technology while enriching the SOR model’s application in cross-cultural consumption scenarios. Full article

Physical artificial intelligence (PAI) has emerged as a transformative paradigm that integrates AI into physical entities, enabling direct interactions with real-world environments. However, despite rapid expansion across diverse domains, PAI research has remained highly fragmented and failed to provide a comprehensive understanding of its overarching knowledge structure. To address this gap, this study conducted a data-driven systematic review of 317 publications indexed in the Web of Science between September 2020 and October 2025. For the analysis of annual publication volume, the growth trend was assessed using complete calendar-year observations from 2021 to 2024, while the 2025 publication count was reported separately as a partial-year observation through October. Combining bibliometric network analysis with latent Dirichlet allocation topic modeling, we identified seven latent research topics. We integrated these fragmented topics into a unified, three-layered hierarchical architecture encompassing (1) physical interaction and infrastructure, (2) policy learning and control, and (3) cognitive integration and multimodal reasoning. The temporal analysis revealed a distinct evolutionary trajectory, indicating a structural shift from simulation-based, navigation-centric studies toward greater cognitive and multimodal integration and the practical implementation of embodied physical systems. This study provides a quantitative and structural mapping of PAI, offering a foundational framework to inform future interdisciplinary research and technological convergence. Full article

This systematic mapping review examines how assistive technology and universal design for learning (UDL) address barriers to participation in inclusive kindergarten-to-grade-12 (K-12) science and science, technology, engineering, and mathematics (STEM) education. Crossref, OpenAlex, and ERIC were searched in November 2024 and a second search wave was conducted on 9 June 2026. Two researchers screened records and extracted data using predefined criteria, resolving disagreements through discussion and, when needed, third-researcher adjudication. Fifty-eight records published between 2010 and 2026 were synthesized by accessibility domain, support function, and qualitative theme. The map included direct K-12 science/STEM evidence alongside explicitly classified adjacent and transferable evidence relevant to K-12 inclusion. Prominent areas included tactile and three-dimensional materials, graphic accessibility, and adaptive representations. Smaller clusters addressed augmentative and alternative communication (AAC), eye-gaze speech-generating devices, sign-supported media, and accessible assessment. The emerging artificial intelligence (AI) literature included classroom interaction evidence alongside conceptual and review contributions. Across this heterogeneous literature, accessible materials were frequently considered together with teacher scaffolding, communication opportunities, and institutional support. The mapping therefore indicates that evaluation should consider learners’ contributions to scientific activity as well as access to content. The review maps reported practices and evidence gaps; it does not estimate comparative technology effectiveness. Full article

This paper proposes an original co-adaptive workflow termed Valuer-in-the-Loop, which establishes a pathway for integrating artificial intelligence within real estate valuation. Structural changes in real estate markets, driven by the rise of hybrid working, e-retail and evolving occupier behaviours, are reshaping the built environment and increasing uncertainty within real estate markets. These changes are accelerating demand for advanced property technologies capable of supporting risk analysis and investment profiling under rapidly evolving market conditions. The increasing adoption of artificial intelligence within real estate valuation raises fundamental questions regarding the relationship between computational analysis and professional judgement. While advances in automated valuation models have improved the ability to analyse large volumes of market data, valuation remains a professional activity in which the application of professional judgement is central. Adopting a deductive research design based on a novel conceptual review, the paper synthesises professional valuation standards and valuation theory to conceptualise real estate valuation as a structured workflow. Within this workflow, valuation models are positioned as quantitative tools that are used to implement valuation methods in whole or in part within a broader automated valuation system. The workflow provides an original theoretical contribution to the field by clarifying and extending the hierarchical relationship between valuation approaches, methods and models. It also newly conceptualises AI-enabled real estate valuation as a co-adaptive process, termed herein “Valuer-in-the-Loop”, through which valuation knowledge is represented through repeated interactions between the valuer and the automated valuation system. In practice, the Valuer-in-the-Loop workflow provides a conceptual basis for professional bodies and practising valuers to integrate AI within established valuation workflows, while offering a set of principles for technology developers and researchers to design co-adaptive AI systems. Full article

This five-wave survey study (2021–2025), longitudinal at school level and repeated cross-sectional at respondent level, tracked teachers’ and students’ (n = 1355 and 1756 responses, respectively) perceptions of classroom technology in four Australian secondary schools spanning a broad socioeconomic range using the UTAUT constructs as an analytical lens across the period of generative artificial intelligence’s (Gen-AI’s) arrival. Perceptions rose in both groups for three years, then diverged sharply: successive student cohorts continued rising on all four constructs, while teachers reversed, ending below their 2021 baseline on performance expectancy (d = −1.06) and social influence (d = −0.94). Linear mixed-effects models confirmed the wave–group interaction on every construct (all p < 0.001). School means were ordered consistently with socioeconomic advantage (ICSEA) at every wave, while device policy was associated with smaller differences. The reversal coincided with the emergence of Gen-AI as a dominant technology in respondents’ free-text nominations. Because the UTAUT items concern classroom technology in general, this coincidence is an association between two independently measured series. On that basis, I propose that discontinuity is a property of the relation between a technology’s capabilities and a role’s accountable tasks and is, therefore, a question to be asked separately of each school population. Full article

Artificial intelligence (AI) is moving rapidly from retrospective prediction and image analysis into treatment selection, operative planning, intraoperative guidance, and postoperative prognostication in cardiothoracic surgery. This transition raises an ethical problem that cannot be resolved by model accuracy alone: when an algorithm begins to shape a high-stakes clinical decision, the distribution of knowledge, authority, and responsibility also changes. This review synthesizes cardiothoracic and closely related medical evidence available through August 2026, with emphasis on quantitative performance, human–AI interaction, bias, patient autonomy, and liability. The available evidence is simultaneously encouraging and cautionary. Machine-learning approaches can improve predictive performance and AI-assisted thoracic planning can reduce errors and increase procedural consistency; however, these gains have not consistently translated into superior patient outcomes. Human–AI studies similarly demonstrate that improved accuracy may coexist with automation bias and overacceptance of algorithmic recommendations. Evidence of demographic performance disparities and limitations in the representativeness of training and validation datasets further raises concerns regarding fairness and equitable access to care. On this basis, we argue that cardiothoracic AI should be governed according to the level of decision influence rather than by technology type alone. We distinguish non-delegable professional duties, distributed system responsibilities, and non-transferable patient authority, and propose an Ethical Heart Team Framework for converting algorithmic output into ethically defensible clinical action. Full article

Taekwondo has progressively incorporated electronic scoring, video replay, wearable sensing, artificial intelligence (AI)-based motion analysis, and immersive training. However, these technologies are commonly studied as isolated tools rather than as components of an integrated physical–digital sport system. This structured integrative review synthesizes taekwondo-specific research, recent sport-technology literature, and foundational engineering studies available through August 2026 to develop a conceptual framework for Phygital Taekwondo. We define Phygital Taekwondo as a closed-loop sport ecosystem in which physical practice and competition are captured through sensing, transformed into task-relevant digital representations, interpreted through AI and motion intelligence, and returned to athletes and other stakeholders through feedback, simulation, decision support, or shared interaction. The framework connects athletes, coaches, referees, venues, spectators, broadcasters, and governing organizations across physical and digital environments. Applications are organized across training and coaching, competition and performance analysis, officiating support, broadcasting and spectatorship, and connected participation. Major constraints include high-speed tracking and occlusion, end-to-end latency, interoperability, privacy and cybersecurity, explainability, fairness, and standardization. The review therefore positions digital transformation as a sport-system integration problem and proposes boundary conditions, operational design principles, a five-level validation hierarchy, and a staged research roadmap for trustworthy, adaptive, and scalable phygital taekwondo systems. Full article

Keratoconus (KC) is a progressive corneal ectasia characterized by stromal thinning, steepening, and biomechanical instability. Although historically considered primarily a structural disorder, current evidence supports a multifactorial pathogenesis involving complex interactions among biomechanical, molecular, cellular, inflammatory, neurobiological, and environmental mechanisms. This narrative review provides an updated overview of KC pathophysiology, integrating current evidence across these interconnected domains. Focal reductions in corneal stiffness, altered viscoelasticity, collagen disorganization, and lamellar slippage contribute to progressive deformation under physiological stress. Oxidative stress and mitochondrial dysfunction promote reactive oxygen and nitrogen species accumulation, impaired antioxidant defenses, keratocyte apoptosis, and abnormal cellular metabolism. Dysregulated extracellular matrix turnover, characterized by increased matrix metalloproteinase activity, reduced inhibitor enzymes, altered cross-linking, and aberrant growth factor signaling, further compromises stromal integrity. Chronic low-grade para-inflammation, neurotrophic imbalance, and subbasal nerve plexus alterations may amplify proteolysis and defective tissue repair. Genetic and epigenetic susceptibility interacts with environmental and behavioral modifiers. Together, these processes form pathways that converge on focal stromal weakening and cone formation. Emerging technologies, including advanced biomechanical imaging, molecular biomarkers, multi-omics approaches, and artificial intelligence, may enable earlier detection and improve risk stratification. Further understanding the pathophysiology of KC may ultimately support the development of targeted therapies aimed at modifying the underlying disease mechanisms rather than addressing the structural consequences solely. Full article

Variations in the electrical signals of rock masses can reflect the development of internal fractures, pore fluid migration, and damage evolution, showing broad potential for the monitoring and early warning of engineering rock mass disasters. In recent years, with the continuous development of electrical resistivity tomography, time-lapse electrical monitoring, digital rock technology, and multiscale numerical simulation, research on rock mass electrical properties has gradually expanded from traditional resistivity measurements to fracture evolution characterization, damage identification, and instability precursor prediction. However, the electrical responses of rock masses are governed by multiple interacting factors, and different physical processes may produce similar or even opposing electrical anomalies. Existing studies have largely addressed conduction theories, monitoring methods, and engineering applications as separate aspects, while a systematic understanding of the mechanisms governing rock-mass electrical responses under different conditions, as well as their intrinsic relationships with conductive network evolution, remains lacking. This review is mainly based on 153 representative publications indexed in the Web of Science Core Collection from 1998 to 2026 and systematically summarizes research progress on the electrical responses of rock masses over nearly three decades. With the conductive network as the central theme, this review comprehensively analyzes multiphase conduction theories, electrical monitoring techniques, conductive network evolution mechanisms, and engineering applications. Rock mass electrical responses originate from the charge transport process within the internal multiphase conductive network. The propagation of fractures, variation of pore structures, fluid migration, and multi-physics coupling continuously change the number, connectivity, and spatial distribution of conductive paths, thereby resulting in dynamic variations of electrical parameters such as resistivity. As rock masses evolve from stable damage to critical instability, the conductive network gradually shifts from local adjustment to rapid reconstruction and critical connectivity, accompanied by abrupt changes in resistivity, enhanced electrical anisotropy, and temporal anomalies. Even during quiet periods of acoustic emission, resistivity can continuously reflect crack propagation and conductive network reconstruction, providing complementary information for the identification of instability precursors. The main contribution of this review is to link the multiphase conduction mechanisms, electrical monitoring methods, resistivity variation characteristics, and damage-to-instability processes of rock masses and to systematically summarize the intrinsic relationships among charge transport, conductive network reconstruction, macroscopic electrical responses, and rock damage and instability. On this basis, an integrated analytical framework from conduction mechanisms to damage characterization and instability precursor identification is established, providing new insights into establishing quantitative relationships among conductive network structure, charge transport processes, and electrical responses, as well as developing electrical theories and intelligent monitoring and early-warning methods for rock masses under multiscale and multi-physics coupling conditions. Full article

The rapid advancement of generative AI (artificial intelligence) is transforming digital environments and reshaping how travellers search for travel-related information. Despite the growing adoption of AI technologies within the tourism industry, limited academic research has examined how generative AI influences pre-trip information search and decision-making behaviour, particularly among Generation Z in the context of culinary tourism. This study investigates how Generation Z integrates generative AI into the pre-trip planning phase of culinary trips and how AI supports decision-making processes. A qualitative research design was applied using a digital diary approach consisting of pre- and post-interviews combined with a structured diary task. Ten purposively selected Generation Z participants documented their real-time interactions with generative AI tools during the planning of a culinary trip. The collected data were analysed in MAXQDA24 using a combined inductive–deductive coding approach. The findings reveal that generative AI primarily functions as an inspirational and efficiency-enhancing co-creation tool during early travel planning stages. Participants used AI to generate destination-specific food recommendations, identify local dishes, discover restaurants and structure culinary itineraries. AI-generated suggestions were typically verified using additional platforms before final decisions were made. The study contributes to a deeper understanding of how generative AI integrates into various stages of the pre-trip planning process and how it complements traditional information sources within the context of culinary tourism and gastronomic activities. Full article

The tourism industry faces increasing demand for personalized travel services alongside environmental sustainability requirements. Recent developments in generative artificial intelligence and large language models provide mechanisms for assisting travelers with itinerary planning, although their integration with tourism data services and sustainability criteria remains under investigation. This paper presents the design and technical evaluation of a conversational agent for sustainability-aware tourism planning in a Technology Readiness Level (TRL) 4 experimental environment. The system combines large language model-based interaction with an external flight information service to generate structured itineraries covering transportation, accommodation, and activities. Sustainability considerations include externally supplied flight emissions information and qualitative recommendation rules for other itinerary components. The controlled evaluation examines functional correctness, natural language processing, external service coordination, response time, and the inclusion of sustainability information. The results indicate that the components can be integrated under the evaluated conditions, while also identifying limitations related to heterogeneous data sources, environmental impact estimation, and the absence of real-world user deployment. The findings concern technical feasibility and do not demonstrate behavioral change or reductions in trip-related emissions. Full article