Search Results (2,249)

Background and Objectives: The perioperative period is characterized by marked biological stress responses that extend beyond direct tissue injury. Innate immune activation during surgery is largely driven by molecular danger signals. Surgical trauma, ischemia–reperfusion injury, blood transfusions, mechanical ventilation, and perioperative infections are critical events that result in the release of danger signals: damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs). This narrative review examines perioperative DAMP and PAMP sources, their molecular recognition pathways, and their clinical and oncological significance. Materials and Methods: A literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science databases for English-language articles published up to July 2026. Search terms included DAMPs, PAMPs, perioperative, surgical stress, cancer surgery, innate immunity, PRRs, TLRs, inflammasome, HMGB1, mtDNA, perioperative immunosuppression, and anesthesia. Results: Danger signals interact with pattern-recognition receptors, including Toll-like receptors and inflammasome pathways, driving sterile inflammation, immune dysregulation, and postoperative organ injury. Rather than initiating these cascades, anesthetics and opioids act as modulators. In cancer surgery, heightened danger signaling and temporary immunosuppression may compromise host defenses during a vulnerable window. Conclusions: Circulating DAMPs and potentially selected PAMP-related markers warrant further investigation as biomarkers for perioperative risk stratification. Overcoming translational barriers will require standardized assays, validation of biomarker signatures, and integration of artificial intelligence-driven molecular profiling to advance personalized onco-anesthesia strategies. Full article

Understanding the cognitive architectures that enable learners to process feedback and self-regulate is fundamental to fostering intelligent second language (L2) learning, particularly as AI-based pedagogies become prevalent. Direct written corrective feedback, while common in beginner L2 writing, often triggers only surface-level processing. This study investigates whether written languaging (WL), a metacognitive activity where learners explain language problems in writing, can deepen feedback processing, thereby activating a more robust cognitive architecture for error correction. Using a within-subjects crossover design with 15 beginner Chinese-as-a-second-language (CSL) learners from a UK secondary school, we compared the immediate and transfer effects of direct feedback with WL versus direct feedback only. Results showed that the WL condition significantly reduced errors per 100 characters (Z = −2.556, p = .011, r = 0.66), with 86.7% of participants showing improvement, indicating enhanced cognitive regulation at the local level. However, the effect was hierarchical, with no significant impact on clause-level accuracy. General Certificate of Secondary Education (GCSE) writing scores improved significantly from pre-to-post-test (Z = −3.342, p = .001, r = 0.89), demonstrating transfer to subsequent performance. Qualitative analyses revealed that learners’ attention focused predominantly on characters (48.4%) and vocabulary (29.8%), with engagement moderated by proficiency and motivation. This study provides empirical evidence for a cognitive architecture of feedback processing, wherein WL functions as a metacognitive amplifier. This architecture is operationalised as a three-stage processing sequence noticing, hypothesis-testing, and metalinguistic reflection, with writing accuracy and WL texts serving as observable proxies for the underlying cognitive processes. Specifically, our findings reveal that WL’s facilitative effects are hierarchical, strongest at the level of local error reduction and not yet extending to clause-level syntactic accuracy, and are moderated by individual differences in proficiency and motivation. These empirical insights offer a cognitive-psychological foundation that could inform the future design of adaptive feedback systems; however, as this study did not involve any AI system, these implications are theoretical and await empirical validation in AI-mediated learning environments. 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

Artificial intelligence (AI) is increasingly transforming recruitment and selection. This conceptual study aims to develop an integrative socio-technical framework explaining HR professionals’ behavioral intention to adopt AI-based recruitment systems. Using a theory-driven conceptual review, the study integrates the Technology Acceptance Model (TAM), selected technological and organizational dimensions of the Technology–Organization–Environment (TOE) framework, and a socio-technical systems perspective. The framework combines perceived usefulness and relative advantage; trust, transparency, data privacy concerns, job replacement anxiety, and resistance to change; and HR readiness and top management support. Trust, transparency, perceived usefulness, and HR readiness are theorized to support behavioral intention; relative advantage is proposed to strengthen perceived usefulness; data privacy concerns are proposed to weaken trust; and job replacement anxiety and resistance to change are theorized to negatively influence behavioral intention. Top management support is conceptualized as moderating the resistance–intention relationship. The framework extends additive adoption models by emphasizing reciprocal socio-technical adaptation among technological characteristics, human interpretations, organizational practices, and governance conditions. As a conceptual model, it requires empirical validation and provides a basis for future research and responsible AI implementation in recruitment. Full article

Extrusion-based additive manufacturing has emerged as a transformative route for 3D-printed continuous-fiber-reinforced polymer composites (3DP-CFRPCs), offering unprecedented opportunities to engineer lightweight structures with programmable mechanical and multifunctional properties. However, existing studies largely treat constituent materials, printing processes, structural design, and functional integration as isolated research topics, limiting the development of robust design principles for intelligent composite systems. This review proposes an architecture-centric smart architecture framework that extends conventional material–process–structure–property relationships by explicitly incorporating reinforcement-path architecture, interface/defect evolution, multifunctional states, and cyber–physical feedback as coupled design and state variables. Within this framework, we critically synthesize the coupled roles of matrix rheology, fiber impregnation, interfacial bonding, and fiber trajectory engineering in determining printability, defect evolution, and mechanical performance. Recent advances in co-extrusion technologies, multi-axis printing, topology optimization, and programmable fiber placement are further discussed as enabling strategies for architected composite design. Particular emphasis is placed on defect-controlled reliability, multifunctional integration, and emerging AI-enabled digital twins that facilitate closed-loop process optimization and intelligent manufacturing. Finally, the remaining challenges regarding scalability, repeatability, and intelligent composite architectures are outlined to accelerate the transition from laboratory-scale demonstrations to industrial deployment. Full article

The increasing integration of generative artificial intelligence (AI) in English as a Foreign Language (EFL) education requires a clearer understanding of the factors associated with students’ adoption intentions. Guided by the Technology Acceptance Model (TAM), this explanatory sequential mixed-methods study examines how prompt engineering competence and motivational identity are associated with students’ behavioral intention (BI) to use generative AI tools, with perceived usability conceptualized as a second-order construct comprising perceived usefulness (PU) and perceived ease of use (PEU). Survey data from 470 undergraduate students were analyzed using structural equation modeling. The results indicate that both prompt engineering competence (PEC) and motivational identity (MI) are associated with the perceived usability of AI tools (PUAI), which in turn predicts BI. Mediation analysis yielded findings consistent with a full mediation pattern under the bootstrapped estimation procedure, suggesting that perceived usability mediated the relationships between prompt engineering competence, motivational identity, and behavioral intention. Follow-up interviews with 10 students provided explanatory insights, indicating that competence and motivation contribute to AI use primarily when they enhance perceptions of usefulness and ease of use. Overall, the study provides context-specific evidence extending TAM within AI-supported EFL learning and offers practical implications for fostering effective prompting skills and learner engagement. 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

Artificial intelligence is increasingly used in sport performance analysis, but its contribution to swimming biomechanics and fluid dynamics remains fragmented across wearable sensing, computer vision, predictive analytics and computational modelling. This structured narrative review synthesises current applications and evaluates how artificial intelligence can progress from describing movement to supporting mechanistically defensible interpretations of hydrodynamic propulsion, drag and performance. Evidence was identified through PubMed and Google Scholar (last search: 28 July 2026) and organised around inertial sensing, markerless pose estimation, training analytics, computational fluid dynamics, explainability and validation. Swimming cycle recognition, lap segmentation and temporal event detection are more extensively studied, but they lack adequate external validation, and direct validation of force- and flow-related outputs is weaker. Machine learning surrogates, physics-informed models and multimodal fusion offer promising routes to connect kinematics with hydrodynamic mechanisms, although small homogeneous datasets, data leakage and insufficient reporting constrain generalisability. The central contribution is the explicit distinction between kinematic observations, validated biomechanical estimates and hydrodynamic inferences. Artificial intelligence can extend biomechanical reasoning in swimming when predictions remain traceable to measurement quality, physical principles and the intended decision context. Full article

Artificial intelligence (AI) is transforming hospitality by enhancing service efficiency and customer experiences through AI-enabled services, such as chatbots, automated check-in/check-out, recommendation systems, and intelligent self-service applications. This study investigates the factors shaping tourists’ behavioural responses to AI-enabled hotel services and their subsequent online review sharing behaviour. The proposed framework extends the Model of PC Utilization (MPCU) by integrating constructs from the Motivational Model and UTAUT2 with hospitality specific factors, including perceived ease of use, operational improvement, need for human interaction, perceived risk, perceived intelligence, and trust. A quantitative design and convenience sampling were employed, with data collected through a structured questionnaire from 400 tourists staying at a four-star hotel on a Greek island during summer 2025. Binary logistic regression was used to examine the proposed relationships. The findings demonstrate differentiated associations between tourists’ perceptions of AI-enabled services and positive and negative online review-sharing behaviour, highlighting the roles of ease of use, intelligence, trust, risk, and human interaction. The study contributes by linking AI service evaluations with post-consumption electronic word of mouth and provides practical guidance for combining trustworthy, user-friendly AI with personalized human service. Full article

Intelligent monitoring of diseases and insect pests in rice and wheat has evolved from handcrafted features and conventional machine learning to deep learning, multimodal data fusion, and time-series forecasting. This review compares data acquisition and representation, unimodal recognition, multimodal fusion, temporal prediction, and field generalization with respect to data requirements, task outputs, application contexts, and the strength of supporting evidence. Conventional machine learning remains valuable for small datasets, variable interpretation, and baseline comparisons, whereas deep learning extends monitoring from classification to detection, segmentation, pest counting, and severity estimation. Multimodal and temporal models further integrate phenotypic, physiological, environmental, and pest-monitoring information to predict future risk. However, many reported gains are weakened by inadequate spatiotemporal alignment, non-independent data partitioning, limited missing-modality tests, and insufficient cross-location and cross-year validation. Future research should prioritize standardized multisite, multiyear datasets; label-efficient, mechanistically informed, and trustworthy fusion methods; lightweight deployment; and prospective field trials that link model outputs to management decisions and production outcomes. Full article

Diabetic retinal disease (DRD) remains one of the leading causes of preventable vision loss worldwide and continues to represent a major global public health challenge despite remarkable therapeutic advances over the past two decades. Intravitreal anti-vascular endothelial growth factor (VEGF) therapy has revolutionized the management of diabetic macular edema and proliferative diabetic retinopathy, significantly improving visual outcomes and reducing vision-threatening complications. Nevertheless, a substantial proportion of patients exhibit incomplete anatomical or functional responses, require frequent lifelong intravitreal injections, or experience disease progression despite adequate VEGF suppression, underscoring the biological heterogeneity of DRD. Growing evidence recognizes DRD as a chronic neurovascular disorder extending beyond a purely microvascular disease. Inflammation, oxidative stress, mitochondrial dysfunction, neurodegeneration, vascular instability, and progressive retinal ischemia interact throughout disease progression. Concurrently, advances in multimodal retinal imaging—including optical coherence tomography (OCT), OCT angiography (OCTA), ultra-widefield imaging, and artificial intelligence-assisted image analysis—have enabled the identification of imaging biomarkers that improve diagnosis, prognostication, and individualized therapeutic decision-making. The therapeutic landscape has expanded considerably beyond conventional anti-VEGF therapy. Corticosteroid implants, dual-pathway inhibitors, sustained drug-delivery systems, vitreoretinal surgery, laser photocoagulation, and emerging pharmacological approaches have broadened treatment options. Increasing evidence supports tailoring therapy according to retinal phenotype, imaging biomarkers, inflammatory status, ischemic burden, and systemic metabolic factors rather than relying on uniform treatment algorithms. This review summarizes recent advances in the understanding and management of DRD, emphasizing pathophysiology, multimodal imaging biomarkers, pharmacological therapies and delivery systems, vitreoretinal surgery, systemic risk-factor optimization, artificial intelligence, and emerging therapeutic strategies. Finally, we introduce Integrated Precision Care (IPC) as a proposed conceptual and research framework that integrates conventional disease staging with multimodal retinal imaging, biological phenotyping, systemic risk assessment, treatment burden, and patient-specific characteristics. IPC is not intended to replace established stage-specific guidelines or to function as a prospectively validated therapeutic algorithm; rather, it provides a hypothesis-generating structure for testing whether multidimensional integration can improve risk stratification, therapeutic selection, durability, patient-centered outcomes, and long-term vision. Full article

As artificial intelligence becomes increasingly integrated into education, intelligent tutoring systems (ITSs) are emerging as important tools for personalized language learning. However, high learner dropout rates indicate that sustaining continuous intention remains a key challenge. This study examines the mechanisms and differential effects shaping IELTS learners’ continuous intention to use ITSs in listening and reading contexts, using an AI test-preparation assistant and 494 valid responses from 522 Credamo surveys. PLS-SEM results show that perceived usefulness, perceived ease of use, perceived enjoyment, and self-efficacy significantly improve learning attitude, while learning attitude and self-efficacy further strengthen continuous intention. Multi-group analysis reveals contextual differences: perceived enjoyment has a stronger effect in listening (β = 0.340, p < 0.001) than in reading (β = 0.178, p < 0.01), whereas perceived usefulness has a stronger effect in reading (β = 0.424, p < 0.001) than in listening (β = 0.226, p < 0.001). ANN results confirm that enjoyment is the strongest predictor of learning attitude in listening (normalized importance = 100%), while usefulness is the key predictor in reading (normalized importance = 100%). The study extends the TAM by incorporating self-efficacy and perceived enjoyment and highlights the importance of context-specific ITS design for AI-supported language learning. Full article

Artificial intelligence (AI) is increasingly being investigated in prostate cancer (PCa) diagnosis and characterization, offering novel approaches to improve detection and risk stratification. This narrative review summarizes current evidence regarding the application of AI across the major stages of PCa management, with particular emphasis on radiomics and pathomics. Radiomics enables the extraction of high-dimensional quantitative features from medical imaging modalities, including ultrasound, computed tomography, multiparametric magnetic resonance imaging (mpMRI), and prostate-specific membrane antigen positron emission tomography (PSMA PET), providing imaging biomarkers that extend beyond conventional visual interpretation. Numerous studies have demonstrated that AI-based radiomic models improve the detection of clinically significant PCa, characterize tumor aggressiveness, predict extracapsular extension, and support individualized treatment selection. Among available imaging modalities, mpMRI remains the cornerstone for radiomics owing to its superior soft-tissue characterization, whereas PSMA PET radiomics has shown particular promise for assessing biologically aggressive disease and metastatic spread. Pathomics has further expanded the role of AI by enabling automated tumor detection, grading, quantification, and identification of adverse pathological features, with promising performance reported in selected retrospective validation studies. Despite encouraging results, widespread clinical implementation remains limited by heterogeneous imaging protocols, variability in data acquisition and annotation, lack of standardized workflows, insufficient prospective multicenter validation, and ethical and regulatory challenges. The aim of this review was to summarize current evidence on AI-based imaging analysis, radiomics, and pathomics for PCa detection, characterization, risk stratification, and pathological assessment, while highlighting the methodological challenges that currently limit clinical implementation. Full article

Background/Objectives: Antimicrobial resistance has renewed interest in live therapeutic phages, including engineered candidates and defined phage cocktails, as antibacterial strategies. However, clinical translation requires evidence extending beyond phage isolation and computational prediction. This review examines how phage genomics and bioinformatics can support phage-based antibacterial development while remaining aligned with pharmaceutical requirements for safety, quality, pharmacology, and clinical validation. Methods: This narrative review synthesizes the literature on the generation, interpretation, and experimental validation of genomic and bioinformatic evidence for live therapeutic phages. Representative approaches for viral identification, genome-quality assessment, annotation, comparative genomics, lytic–temperate lifestyle classification, host prediction, receptor analysis, antiphage-defence profiling, and artificial-intelligence-assisted prioritization were evaluated according to their outputs, principal failure modes, validation requirements, and supported development decisions. Results: Current tools address distinct analytical tasks. Examples include VIBRANT and geNomad for viral identification, CheckV and PhageTerm for genome-quality and termini assessment, Pharokka, PHANOTATE, and PHROGs for gene prediction and annotation, PhageAI, BACPHLIP, and PhaTYP for lytic–temperate lifestyle prediction, iPHoP and CRISPR spacer matching for host prioritization, and PADLOC and DefenseFinder for bacterial defence profiling. Their outputs differ in taxonomic resolution, reference coverage, training-data dependence, and biological interpretation. The review maps these outputs to proportionate validation requirements while integrating formulation and PK/PD context, sequence-to-product traceability, intellectual-property documentation, minimum-information reporting, and resistance-responsive redesign. Conclusions: This review presents a practical sequence-to-product framework for interpreting contemporary phage-bioinformatics methods. By separating computational discovery from isolate-level activity, product quality, pharmacological evidence, and clinical monitoring, it clarifies the decisions supported at each stage and the additional evidence required before candidate progression, product use, or redesign. Full article

Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer’s overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer’s operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network. Full article