Search Results (2,827)

Sleep disturbance is a central feature of depression and a proposed pathway through which Bright-Light Therapy (BLT) exerts antidepressant effects. However, little is known about how sleep reorganises day by day during BLT or whether these dynamics relate to symptom improvement. We analysed daily sleep diaries from 66 patients with depression undergoing three weeks of BLT in routine outpatient care. Generalised Additive Mixed Models characterised daily trajectories in sleep timing, continuity, duration, and Subjective Sleep Quality, and weekly changes in sleep regularity were assessed using Root Mean Square of the Successive Differences. Structural Equation Modelling examined whether within-person deviations in sleep parameters mediated changes in depressive symptoms. Sleep timing showed gradual adjustment across treatment, with a progressive 48 min advance in weekday sleep onset. Sleep regularity improved from Week 1 to Week 2 before partially reversing, and the probability of nocturnal awakenings followed a non-linear trajectory. Other sleep parameters showed weaker directional trends. Improvements in Subjective Sleep Quality accounted for a modest portion of the association between treatment progression and reductions in depressive symptoms, whereas changes in sleep timing and regularity were not associated with symptom change. These findings indicate that sleep reorganises gradually during outpatient BLT, with different sleep dimensions evolving on distinct timescales and Subjective Sleep Quality emerging as one observable component linked to symptom improvement. More broadly, the results highlight the value of day-to-day modelling for understanding sleep–mood dynamics during real-world chronotherapy. Full article

Human–computer interaction (HCI) is central to wearable technology; however, traditional interaction methods face constraints from environmental noise, privacy risks, and operational inconveniences. With the convergence of flexible electronics and artificial intelligence, smart wearable systems equipped with biomimetic biointerfaces are evolving into “external organs” that augment human capabilities, establishing a new paradigm for natural and intelligent interaction. This narrative review provides a comprehensive overview of the research progress in seamless HCI driven by wearable biointerfaces and intelligent systems. From the input perspective, we elucidate how high-fidelity physiological and motion signals are captured through biocompatible electronic skins, and subsequently decoded via intelligent algorithms capable of robust noise decoupling, cross-user generalization, and multimodal data fusion, while emphasizing algorithmic trustworthiness including privacy and interpretability. From the output perspective, we explore adaptive closed-loop feedback mechanisms, spanning both non-visual multi-sensory rendering and biomimetic actuation-based physical interventions. Finally, we discuss key engineering and algorithmic bottlenecks—such as material durability, internal latency, system integration, and trustworthiness—offering future perspectives for the development of next-generation personalized and immersive HCI systems. Full article

The rapid integration of artificial intelligence (AI) accelerates the need for continuous skill acquisition. Consequently, this increases the importance of lifelong learning while raising fundamental questions about pedagogical interaction and human social values. To remain competitive, individuals must constantly acquire new skills and enhance existing ones. The aim of the article is to evaluate the stability of individual social value systems and the role of pedagogical interaction in lifelong learning during AI integration. The study uses a quantitative survey (N = 160) with a retrospective self-assessment model based on Schwartz’s Theory of Basic Human Values. The study processed data in IBM SPSS using non-parametric tests (Wilcoxon signed-rank, Kruskal–Wallis, Kendall’s rank correlation) to analyze how digital skills and sociodemographics influence technology perception. Findings reveal core value systems remain statistically stable; AI integration causes no internal value conflict. Digital skill level, rather than age, is the most significant factor in AI perception. While participants highly rate AI’s potential to customize learning, they express concerns regarding technological dependence. In the lifelong learning ecosystem, AI is viewed as a didactic tool rather than an educator replacement, as technology cannot provide essential social interaction and emotional support. Finally, higher education fosters a critical attitude toward AI’s ethical risks. Full article

In this paper, we examine the evolving conception of the “other” in relation to human happiness, drawing on Paul Ricoeur’s philosophical account and empirical findings from the Harvard Study of Adult Development. Ricoeur situates happiness in three interrelated threads: individual fulfilment, friendship with those near to us, and just relations with distant others. The Harvard Study corroborates the significance of relationality for well-being, showing that strong social ties enhance longevity and life satisfaction. However, contemporary digitalisation and the proliferation of AI humanoid social robots challenge traditional notions of the “other.” Individuals increasingly form “meaningful” attachments, emotional bonds, and even romantic relationships with technological artefacts, raising the question of whether these non-human entities can contribute to happiness in a Ricoeurian sense. While the above dynamics are now proliferating, we argue that AI and social robots cannot be considered as the “other” in the Ricoeurian sense. Although these technologies can be considered as a virtual other, we do not defend that position in the current paper. In this paper, we explore the tensions regarding the authenticity, moral status, and ethical implications of AI and social robots in relation to human happiness. We conclude by proposing a re-evaluation of relationality, moral consideration, and the ethical frameworks underpinning human–technology interactions in the pursuit of human flourishing and happiness in the Ricoeurian sense. Full article

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Railway cable-stayed bridge construction is characterized by high complexity and substantial safety risk. Deficiencies in safety control may result in serious accidents (e.g., collapse and falls), causing significant casualties and economic losses; therefore, clarifying risk interactions and accident-causing mechanisms is essential. This study proposes a fuzzy DEMATEL–ISM approach in which fuzzy sets capture uncertainty in experts’ linguistic assessments. DEMATEL quantifies influence strengths and causal relationships among factors, and ISM constructs a multi-level hierarchy to explain accident causation. Twenty safety influencing factors are identified and grouped into five categories: management, human, material and equipment, construction technology, and environmental conditions. The obtained accident-causing mechanism comprises seven hierarchical levels: L₁: collapse and fall accidents, L₂: direct factors, L₃–L₅: indirect factors, and L₆–L₇: root factors. This mechanism is a chain of events that leads to an accident, with the nodes improper prestressing, structural deformation and differential settlement. These key nodes can be avoided by reinforcing safety management system implementation, daily supervision and inspection, and education and training on the subject of safety to ensure the safety of railway cable-stayed bridge construction. Full article

Digital transformation in higher education has increasingly shifted from a technology-centered agenda toward a multidimensional institutional process involving governance, quality assurance, process redesign, and data-driven decision-making. This study proposes and operationalizes an analytical framework for examining digital transformation in universities through an interactive Human–Machine Interface developed in Python. The framework is structured around three complementary methodological cores: ontology-based modeling, statistical reliability analysis, and multiobjective optimization. The ontology module organizes the semantic structure of digital transformation dimensions, revealing their relational hierarchy and structural relevance. The statistical module evaluates internal consistency and distributional behavior through Cronbach’s alpha, corrected item–total correlation, and density-based inspection. The optimization module formulates intervention selection as a constrained multiobjective problem, allowing the identification of efficient portfolios under cost, readiness gain, equity, and feasibility criteria. The analytical environment also incorporates interactive dashboards, VOSviewer-style relational exploration, and exportable high-resolution figures. Results show that digital transformation readiness is heterogeneous across groups, that governance-oriented dimensions occupy a central semantic role, and that institutional intervention planning benefits from Pareto-efficient decision support rather than single-criterion ranking. The study contributes a coherent bridge between conceptual models of digital transformation and an operational analytical environment capable of supporting institutional diagnosis, evidence-based prioritization, and strategic planning in regulated higher education settings. Full article

The gut microbiome, often termed the human “second genome”, profoundly influences host physiology through metabolic interactions, immune modulation, and gut–brain axis signaling. Dysbiosis is implicated in the pathogenesis of obesity, inflammatory bowel disease (IBD), malignancies, and neuropsychiatric disorders. However, traditional gut microbiota interventions, such as probiotic supplementation and fecal microbiota transplantation (FMT), still exhibit significant limitations in precision therapeutics. Probiotic intervention fails to achieve precise regulation at the strain or genetic level, and although FMT demonstrates definitive efficacy against recurrent Clostridioides difficile infection (rCDI), its therapeutic outcomes and safety profiles show marked interindividual variability in ulcerative colitis (UC), metabolic syndrome, and other diseases, with insufficient treatment specificity to meet the practical demands of clinical precision intervention. Recent advancements in genome editing technologies, particularly Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)–CRISPR-associated (Cas) proteins systems and base editors, have enabled targeted functional manipulation of specific gut commensals and optimization of community architectures. These engineered strategies, combined with sophisticated delivery systems, demonstrate substantial potential in disease treatment, diagnostic monitoring, and immune modulation. This review systematically examines core editing methodologies, innovative delivery platforms, and targeted design strategies, elucidating their applications in metabolic disorders, IBD, cancer immunotherapy, and neuropsychiatric conditions. We critically analyze current technical bottlenecks and biosafety concerns while prospecting future directions, including in situ editing, artificial intelligence (AI)-driven design, and personalized engineering. Collectively, these insights aim to facilitate the clinical translation of gut microbiome engineering from bench to bedside. Full article

In this review, the virulence factors involved in enterotoxigenic Escherichia coli (ETEC) colonization and pathogenesis are analyzed, with an emphasis on colonization factors, enterotoxins and antigenic diversity as central challenges in vaccine development. ETEC remains a major cause of diarrhea worldwide, particularly in vulnerable populations. Despite extensive research, no broadly protective licensed vaccines are available largely because of antigenic heterogeneity and the limited understanding of immune correlates of protection. We identified critical knowledge gaps in antigen prioritization and host–pathogen interactions and translational limitations that have hindered vaccine success. We critically evaluated emerging platforms (including mRNA vaccines, nanoparticles, multiepitope strategies, and reverse vaccinology) for their potential to overcome variability and increase immunogenicity. We examined the roles of ecological environmental reservoirs associated with human and animal systems, in addition to antimicrobial pressure, in shaping ETEC evolution and vaccine effectiveness within a One Health framework; moreover, we propose an integrated approach that links genomic surveillance-based vaccine ecology and next-generation vaccine technologies to support adaptive immunogen design. This review provides actionable recommendations for the development of broadly protective and translationally viable ETEC vaccines from the One Health perspective. Full article

This perspective paper develops a system-level characterization of the transportation metaverse as a persistent, policy-aware digital environment integrating digital twins, real-time data, advanced analytics, and human–machine interaction into a unified operational framework. The study presents a cross-modal review of metaverse applications in road, rail, maritime, and aviation systems, identifying common opportunities, limitations, and research challenges. It further proposes a structured metaverse-based framework for smart roads as a reference case. The framework demonstrates how persistent virtualization, parallel future scenarios, embedded governance constraints, and human-in-the-loop decision support can improve uncertainty-aware planning, management, and operations. The paper positions the metaverse not as a deployable technology, but as an emerging paradigm for transportation governance. The study provides an architectural vision and research agenda for developing more resilient, transparent, and adaptive transportation systems. Potential applications include smart road management, multimodal traffic coordination, real-time operational control, infrastructure resilience planning, and decision support for policymakers under uncertain conditions. Full article

With the rapid development of Artificial Intelligence (AI) technology, human-like AI service agents have been increasingly applied in service marketing. Online travel agency (OTA) platforms provide an important application context for such service agents in consumer-facing service interactions, such as travel planning and related services. Drawing on social cognitive theory and control theory, this study examines the psychological mechanisms underlying consumers’ intentions to adopt AI service agents. One pretest and two experiments involving 521 participants were conducted to investigate the effects of the AI service agent role on consumers’ willingness to use substitute vs. assist AI. The results show that consumers are more willing to use assist AI service agents than substitute AI service agents. This effect is mediated by human identity threat and sense of control. Moreover, higher consumer technology readiness moderates these effects, mitigating the preference for assist over substitute AI service agents. This study extends the conceptual framework of AI service agents in human–computer interaction research and offers practical implications for the effective design and deployment of AI service agents in OTA applications. Full article

In the human reproductive system, extracellular vesicles (EVs) have been recognized as playing a vital role in mediating cell–cell communication. They are considered critical for embryo development, implantation, gamete interaction, and fertilization. The various cargoes carried by EVs, depending on the physiological and pathological state of the cell, include proteins, lipids, nucleic acids, and mitochondrial components. EVs are recognized as critical carriers of redox-related signals and mitochondrial components, linking oxidative stress (OS) to reproductive failure and influencing gamete quality and embryo competence. Although considerable progress has been made, research remains poorly integrated, despite individual omics technologies providing valuable molecular insights. The use of multi-omics technologies, including transcriptomics, proteomics, metabolomics, and microbiome analysis, has been proposed as a global approach to understanding the complexities associated with EVs and discovering new biomarkers associated with infertility. ML and AI have been proposed to identify predictive signatures linked to ART effectiveness and reproductive outcomes, with a strong capacity to handle high-dimensional data. The review aims to provide an overview of current knowledge on EV-mediated redox–mitochondrial signaling in human reproduction, while highlighting the importance of emerging multi-omics and AI technologies for EV-mediated biomarker development. The review discusses the promise of EVs in the development of minimally invasive diagnostic approaches and therapeutic interventions, as well as the challenges in the standardization, integration, and clinical translation of EV-mediated research. In addition, the review proposes integrating computational approaches to better understand molecular pathways involved in the development of next-generation precision medicine in human reproduction. Full article

The rapid advancement of flexible electronics technology has endowed flexible sensors with significant application potential in fields such as wearable sensors, bionic skin, and human–machine interaction, owing to their excellent conformability, stretchability, and comfort. However, as application scenarios continue to expand and deepen, higher requirements are imposed on sensor performance in terms of sensitivity, stability, biocompatibility, environmental friendliness, and multifunctional integration. Polyurethane composites, leveraging their intrinsic characteristics, including tunable molecular structure, superior flexibility, and good biocompatibility, can effectively impart properties such as electrical conductivity, self-healing capability, and high sensitivity through compositing with various functional materials, thereby precisely aligning with the diverse demands of next-generation flexible sensors. This article systematically reviews the synthesis strategies of polyurethane composites; provides a detailed analysis of the roles of fillers—including carbon-based materials, polymers, and metal nanoparticles/nanowires—in enhancing the mechanical, electrical, and functional properties of the composites; and further summarizes the research progress of polyurethane composite-based flexible sensors in cutting-edge areas such as eco-friendly sensing, human motion monitoring, health monitoring, and bionic electronic skin. Future development trends are also discussed, aiming to provide insights for the design and development of high-performance flexible sensors. Full article

With the rapid development of flexible electronics technology, flexible wearable sensors based on Lanthanum Strontium Manganese Oxide (La_1-xSr_xMnO₃) have garnered extensive attention in recent years due to their excellent multi-functional integration, environmental stability and biocompatibility. This review systematically analyzes the preparation methods, process optimization strategies, multi-performance integration technologies, and the expansion of the application field of La_1-xSr_xMnO₃-based flexible sensors. Firstly, the basic characteristics and sensing mechanism of the La_1-xSr_xMnO₃ material were presented, including its temperature sensitivity, strain response characteristics, and magnetoresistance effect. Secondly, the fabrication process of flexible sensors was elaborately discussed, with a focus on analyzing crucial technologies, such as laser induction and transfer printing technology. Subsequently, the strategies for regulating the electrical, thermal, and mechanical properties of materials through element doping, along with the multimodal sensing integration and signal decoupling methods, were expounded. Furthermore, the actual performance of this type of sensor in fields such as health monitoring, human–computer interaction, and extreme environment applications was summarized. Finally, the challenges and future development directions of La_1-xSr_xMnO₃-based flexible sensors are outlined, providing theoretical references for the design and optimization of next-generation flexible electronic devices. Full article

This systematic review comprehensively explores the integration of Extended Reality (XR) technologies, comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), into New Zealand’s STEM education framework. In alignment with PRISMA 2020 guidelines, we systematically analyzed 127 peer-reviewed studies from the Web of Science (n = 48), Scopus (n = 57), and Dimensions (n = 22) and incorporated 15 grey literature sources, resulting in 142 studies included in the review. Our meta-analysis found substantial improvements in student conceptual understanding from XR-enhanced STEM modules. Specifically, we observed an average increase of 23.4% when compared to traditional instructional methods (95 percent Confidence Interval: 18.7 to 28.1 percent, p < 0.001). These gains were especially prominent in interactive learning environments where immersive XR applications supported deeper engagement and the visualization of abstract STEM concepts. The qualitative synthesis highlighted several key barriers that limit effective XR integration. These include technological infrastructure gaps reported in 68 percent of reviewed studies, a critical need for educator training cited by 82 percent of studies, and curriculum alignment issues present in 57 percent of cases. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018, and the qualitative component employed a deductive thematic coding approach with inter-coder reliability verification. Successful institutional implementations were also identified. At Auckland University of Technology, XR-supported courses produced a 67 percent increase in student engagement, while Wellington High School achieved a 41 percent reduction in STEM achievement gaps through targeted XR interventions. Based on the evidence, we propose a four-phase implementation framework that addresses the technological, pedagogical, and policy requirements for sustainable XR adoption. These findings highlight the role of immersive technologies in supporting human-centered digital transformation and future skills development in the transition to Industry 5.0. The review contributes evidence-based insights that support the transition from technology-driven approaches associated with Industry 4.0 to the human-centered, socially oriented priorities of Industry 5.0. It also identifies critical research gaps, particularly in long-term learning outcomes and the integration of Mātauranga Māori within XR-enabled STEM environments. Full article