Search Results (22,177)

Playing-related musculoskeletal disorders (PRMDs) are highly prevalent among bass-guitar players due to sustained asymmetrical postures, repetitive finger movements, and prolonged support of instrument weight. This study proposes a Human Digital Biomechanical Twin-driven, simulation-based approach to optimize bass-guitar support systems, integrating biomechanical modelling, motion capture, and musculoskeletal simulation. A preliminary survey among 63 Italian bass-guitar players was performed to define the experimental conditions regarding posture, instrument type, and session duration. Fifteen experienced bassists participated in laboratory trials using motion capture and postural assessment tools, including MediaPipe Pose, RULA, and AnyBody Modelling System. Baseline results highlighted significant activation of the trapezius and spinal extensor muscles (19–26% MVC), confirming high ergonomic risk. Three alternative support configurations were digitally simulated, revealing that a three-point harness system (bilateral shoulder straps plus thoracic anchoring) reduced spinal stabilizer activation by 15–25% across four anthropometric percentiles. Experimental validation confirmed enhanced comfort, reduced fatigue, and improved instrument stability, with the majority of participants preferring the ergonomic configuration. These findings demonstrate the feasibility of a simulation-based, prospective, and human-centred ergonomic design framework, offering a scalable methodology to compare and optimize adaptive instrument-support systems before physical prototyping. Full article

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies. Full article

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

Retrofit strategies to improve the energy performance of buildings have gained significant importance worldwide; however, asbestos in older residential buildings is considered a serious threat to both human health and the environment. Existing studies have generally focused on the health effects of asbestos, the properties of insulation materials, or individual aspects of energy performance, while a coherent and comparative conceptual framework for sustainable retrofit systems is limited. This review aims to systematically integrate the current scientific evidence on asbestos management, alternative insulation materials, life cycle assessment (LCA), and circular economy principles to present a literature-informed conceptual decision-support framework for sustainable retrofit. The study used the PRISMA-based literature selection approach, while the evidence from different peer-reviewed studies was comparatively organized in the context of process workflows, risk considerations, lifecycle impacts, and building-physics-related findings. The literature-based results indicate that incorporating safe asbestos management, low-carbon insulation materials, and circular retrofit strategies into an integrated approach can improve energy efficiency and environmental sustainability. However, this study is not based on a validated numerical simulation, an executed optimization model, or calibrated engineering analysis, but rather on a comparative synthesis and conceptual interpretation of the existing literature and presents a decision-support framework that can guide future low-carbon and safe construction strategies. 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

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from the first aquifer, locally supported by a deeper aquifer connected to the first one near the bowl of Lubowidzkie Lake. Groundwater drainage occurs by gravity. It is relatively abundant for young glacial areas and averages 82 dm³·s⁻¹, making the springs capable of acting as a drinking water reservoir. This assessment is based on major ions and nutrients only; microbiological and trace-organic/metal analyses are required before any drinking-water designation. Spring water is important in the lake’s supply, accounting for 18.0% of the total inflow to the basin. The hydrochemical characteristics of these waters keep the lake in ecological balance. The waters from the springs are characterized by little variation in chemical composition, with the Ca-HCO₃ hydrochemical type. They represent young infiltration waters associated with direct recharge from precipitation (the average age of the water is 60 years). Currently, low nitrate and chloride suggest limited agricultural and urban influence, but phosphate levels and observed human activities warrant caution. Forest management is gradually developing in its catchment, which may result in a reduction of the spring yield and a deterioration of their quality in the future. This may result in a disturbance of the hydrological balance of structures hydraulically connected to spring recharge and to groundwater inflow (river, lake). Although the springs studied are local hydrological phenomena, their functioning and the need for protection are closely linked to global challenges in the field of sustainable development. This primarily concerns the protection of groundwater-dependent ecosystems and, more broadly, water security and increased resilience to climate change. 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

Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures—even at very low levels, termed “subconcussive”—can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain’s largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose–response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management. Full article

The deployment of Deep Learning (DL) for anomaly detection in Industrial IoT (IIoT) is critically hampered by the non-stationary nature of industrial data streams and the lack of forensic-grade explainability. This systematic review synthesizes 48 peer-reviewed studies (2021–2025) to quantify the performance collapse of static models under concept drift and to establish operational criteria distinguishing post hoc feature attribution (Type A XAI) from forensic root-cause diagnosis (Type B XAI). Our analysis reveals three critical findings: (1) static DL models suffer a 15–22% F1-score degradation across wastewater, manufacturing, and energy sectors when deployed in non-stationary environments, rendering them operationally non-viable without continuous adaptation; (2) the current literature remains saturated with Type A explainability (80% of corpus through 2023), creating a Forensic Gap where operators receive statistical correlations but lack actionable maintenance directives; and (3) emerging 2024–2025 research marks a paradigm shift toward Type B methodologies, yet no unified framework bridges real-time detection with deep causal reasoning. To address these gaps, we contribute the following: (1) a validated operational taxonomy (Cohen’s $\kappa =0.84$) with reproducible five-criterion rubric enabling forensic XAI classification; (2) the first quantitative synthesis of drift penalties in industrial deployments; and (3) a three-tier Edge-Cloud Forensic XAI architecture that achieves 70% communication payload reduction via compressed latent vectors while integrating tnGAN-based data imputation (handling 20–30% missing data) and physics-guided causal reasoning engines. Our framework decouples millisecond-level edge detection from 1–3 s cloud-based forensic diagnosis, ensuring both operational responsiveness and actionable industrial insight. We conclude that the future of safety-critical IIoT demands “Forensic-by-Design” architectures leveraging machine unlearning for drift adaptation and LLM-based natural language interfaces for operator-facing explanations, positioning Industry 5.0 to bridge the gap between algorithmic detection and human-centered decision support. Full article

Peer review is a cornerstone of authentic assessment in Digital Media education, yet its role in self-regulated learning remains underexplored, particularly as generative AI reshapes assessment. This study analyses structured peer review practices across three undergraduate units in an Australian Bachelor of Digital Media program, using artefacts created before generative AI became widespread. Guided by Zimmerman’s model of self-regulated learning and Self-Determination Theory, the analysis examines how students engage with assessment. It identifies three recurring mechanisms: rubric calibration, justified critique, and revision reflection. These mechanisms align with the forethought, performance, and self-reflection phases of learning. Together, they show how human-mediated assessment supports students’ capacity to judge quality, use feedback, and make revision decisions. The paper proposes a three-tier hybrid assessment model as a design-oriented implication rather than an empirically tested AI intervention. Full article

This paper investigates the evolution and driving mechanisms of the Coupling Coordination Degree (CCD) between urban development (UD) and ecological efficiency (EE) in the Yangtze River Delta (YRD) urban agglomeration. Using panel data for 27 cities from 2013 to 2023, a comprehensive evaluation model and the CCD model are used to measure the level of coordination across cities. A fixed-effects panel regression model is constructed to systematically examine the effects of economic development and education expenditure on CCD, focusing on the moderating role of education expenditure. The empirical results indicate that: (1) CCD in the YRD shows a clear upward trend, gradually shifting from disorder to coordinated development; (2) inter-city disparities remain evident, reflecting differences in development foundations, industrial structures, and governance capacities; (3) economic development significantly promotes CCD by providing material and institutional support for urban–ecological coordination; and (4) education expenditure strengthens this positive relationship by enhancing human capital accumulation and knowledge spillovers. The findings highlight the importance of integrating economic growth with human capital investment to promote green and high-quality urban development. Full article

Background: Viral meningoencephalitis is a frequent cause of acute central nervous system infection in children, particularly in neonates and young infants. Although etiological diagnosis has improved through molecular testing, management remains largely supportive, and intravenous immunoglobulins (IVIGs) are occasionally used in clinical practice despite limited supporting evidence. Methods: We performed a single-center retrospective observational study including pediatric patients aged 0–10 years admitted between 2016 and 2025 with molecularly confirmed viral meningoencephalitis. Demographic, clinical, microbiological, therapeutic, and follow-up data were collected. Neurological outcomes and length of hospital stay were compared between patients treated with IVIG and those who were not. Results: Twenty-nine patients were included. Enterovirus was the most frequently identified pathogen (50.0%), followed by human herpesvirus (35.7%) and human parechovirus (14.3%). IVIG was administered to 28% of patients, all with enterovirus infection. IVIG-treated patients were significantly younger at presentation and more frequently presented with apnea (42.9% vs. 0%, p = 0.014). Most patients had a favorable neurological outcome (85.7%). Unfavorable outcomes, including neurodevelopmental delay and/or epilepsy, occurred in a minority of cases (14.3%) and exclusively in enterovirus-infected patients. No significant association was found between IVIG administration and neurological outcome. Conclusions: In this real-world pediatric cohort, IVIG use was associated with more severe clinical features rather than improved neurological outcomes, underscoring the need for careful consideration and further investigation in this setting, particularly in the subgroup of infants with enterovirus encephalitis. Full article

Biodiversity, as the foundation of life on Earth, sustains the balance of ecosystems and supports human sustainable development. However, the current accelerated decline in biodiversity poses ecological threats that require urgent attention. This research based on the perspective of ethnic ecological wisdom, explores the customary practices of biological conservation among the Miao ethnic group in Southwest China, the Tao ethnic group on Orchid Island (Lanyu), Taiwan, and the Maasai ethnic group on the East African Plateau. By conducting in-depth case studies, combined with literature review and data validation, it investigates their practical value and implementation pathways in biodiversity conservation. By analyzing the ecological conservation wisdom models of the Miao, Tao and Maasai ethnic groups, it is found that the core species populations in each region have shown a positive growth trend since the gradual integration of traditional ethnic customary laws with modern ecological protection systems and practices. Drawing on the extensive experience accumulated in integrating customary law into ecological governance across the three cases, this study proposes a three-dimensional optimization pathway: at the policy level, construct a mechanism integrating customary law and diversified ecological compensation; at the community level, implement a model featuring benefit sharing, patrol mediation and digital management; and at the cultural level, strengthen the development and dissemination of ethnic ecological conservation wisdom through multidisciplinary talent training and IP-based communication of exemplary customary law outcomes. We aspire to slow the rate of global biodiversity loss and achieve a bright future of harmonious coexistence between humans and nature. Full article

Background/Objectives: Skin and soft tissue infections (SSTIs) represent a significant clinical challenge, largely due to the high prevalence of antibiotic-resistant Staphylococcus aureus, particularly methicillin-resistant S. aureus (MRSA). Treatment is further complicated by biofilm formation, which reduces antibiotic efficacy. The limitations of conventional therapies highlight the need for alternative approaches. Phage therapy has emerged as a promising biological strategy; however, its effectiveness may be constrained by factors such as phage instability and biofilm regrowth. This study aimed to enhance phage-based treatment by combining a newly isolated phage, vB_Sau-E, with lactoferrin (Lf), a multifunctional protein of the innate immune system. Methods: Phage vB_Sau-E was characterized in terms of its infection dynamics and lytic activity. Biocompatibility was further examined using human skin cell lines. The potential effect of Lf was assessed by evaluating its impact on phage infectivity and stability under a range of environmental conditions and by checkerboard assay. Results: Phage vB_Sau-E belongs to the Silviavirus genus in the Herelleviridae family. It was shown to infect 12 out of 22 tested clinical MRSA isolates, with 10 strains identified as good hosts. The phage has a ~30 min life cycle, and ~50 progeny virions are released after bacterial cell lysis. We have also observed that Lf increased plating efficiency and enhanced phage stability at a pH of 5.5 and at −20° C. It also proved to have an additive antibacterial effect, though this was observed to be strain-dependent. Conclusions: Lactoferrin functions as a stabilizing adjuvant for phage vB_Sau-E. Its additive effect supports the development of more effective, biofilm-targeting therapies for staphylococcal SSTIs. Full article

Background/Objectives: Mastication is a complex sensorimotor function involving coordination between the brainstem central pattern generator and supraspinal systems, particularly the primary motor cortex (M1). Evidence suggests a link between masticatory activity and corticomotor plasticity, but findings remain fragmented. This scoping review aimed to synthesise the human evidence on the relationships among mastication, tooth loss, dental rehabilitation, ageing, and corticomotor plasticity, with emphasis on M1 mechanisms. Methods: Following PRISMA-ScR guidelines, systematic searches were conducted in MEDLINE/PubMed, Scopus, and Web of Science using terms related to mastication, neuroplasticity, motor cortex, ageing, and rehabilitation. Eligible studies included human experimental, clinical, and observational research employing neuroimaging or neurophysiological methods. Data were extracted and synthesised using a Population–Concept–Context framework across eight conceptual domains. Results: Twenty-two heterogeneous studies (fMRI, TMS, EMG, psychophysical, histological) were included. Mastication consistently activated distributed sensorimotor networks, including M1 and the primary somatosensory cortex (S1). Peripheral sensory input and dental mechanoreception were linked to structural and functional adaptations. Corticomotor excitability was modulated by chewing, oral-motor learning, and rehabilitative interventions. Ageing was associated with altered but preserved cortical responsiveness. Associations between mastication and cognition were reported, though largely cross-sectional. Overall, findings suggested a relationship linking peripheral input, sensorimotor integration, and corticomotor plasticity, but methodological variability limited causal inference. Conclusions: Mastication is linked to modifiable corticomotor activity and supports experience-dependent neuroplasticity. However, the evidence remains largely associative and methodologically heterogeneous. Neural adaptations appear to be preserved with ageing but are influenced by systemic and environmental factors. Longitudinal, multimodal research is needed to clarify the mechanisms, causality, and clinical relevance, particularly in rehabilitation contexts. Full article