Search Results (1,498)

To more effectively address the wide range of safety risks faced by construction workers on job sites, machine learning (ML)–based computer vision and augmented reality (AR) technologies are increasingly being employed to enhance efficiency, safety, and productivity. However, current AR construction safety tools only provide passive information for the user to then decide how to use that information. This study leverages advanced computer vision coupled with AR to work with site managers and on-site workers to make operational safety decisions using real-time, visual information of potential hazards. A YOLOv11 model trained to detect the presence or lack of personal protective equipment was developed and tested by creating a local ML computing environment using camera feeds. The detection results were compiled and displayed in real time on a web-based interface developed with Hypertext Preprocessor and on a Microsoft HoloLens 2 heads-up display. The system was successfully field-tested on a construction site. Full article

The Developmental Origins of Health and Disease framework proposes that environmental exposures during critical periods of development can shape physiological systems and influence the risk of chronic diseases later in life, including diabetes and metabolic syndrome. Most research on metabolic programming has focused on classical metabolic organs such as the liver, pancreas, and adipose tissue. However, skeletal muscle plays a central role in systemic glucose homeostasis and metabolic flexibility, accounting for the majority of insulin-stimulated glucose uptake in the body. Because muscle metabolism is closely regulated by neural activity through the organisation of motor units, the development of the motor neuromuscular axis may represent an underexplored dimension of metabolic programming. This review examines evidence linking early-life metabolic environments to neuromuscular development and discusses how alterations in the maturation of motor neurons, neuromuscular junctions, and muscle fibre phenotype may influence long-term metabolic outcomes. Evidence from epidemiological studies, experimental models, and mechanistic research suggests that maternal metabolic disturbances, including hyperglycaemia, obesity, and systemic inflammation, can influence foetal development through metabolic and inflammatory pathways affecting both neural and muscular components of the motor system. These findings support the hypothesis that the motor neuromuscular axis may represent a structural interface linking early developmental exposures to long-term metabolic regulation and risk of metabolic syndrome. Full article

The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk assessment, and closed-loop decision support now expected in network operation. Digital twins offer a way to address this gap by linking network models to operational data and revising those models as system conditions change. After systematically searching Scopus and the Web of Science, six application areas for digital twin applications in distribution network simulations are summarized: model construction, simulation and validation platforms; asset, equipment and spatial digitalization; DER (distributed energy resource), PV, EV (electric vehicle) and prosumer integration; operation, monitoring and situational awareness; protection, fault diagnosis and resilience; and optimization, control and planning. The review examines the architectures, enabling technologies, and applications reported across this evidence base. The literature indicates a gradual shift from conceptual digital representations toward real-time simulation, hardware-in-the-loop validation, data-driven model updating, and distribution-side decision support. Persistent gaps concern low-voltage observability, data governance, model credibility assessment, standardized interfaces, cybersecurity, and closed-loop control. Full article

In the context of industrial digitalization and the widespread adoption of process automation systems, Knowledge Management Systems (KMS) play a key role in providing operational personnel with up-to-date instructions and regulations. However, the inherent ambiguity of natural language in technical documentation remains a serious obstacle, leading to incorrect operator actions, process deviations, and increased safety risks. This article investigates the integration of Large Language Models (LLMs) into KMS and its impact on user experience and human–machine interaction in industrial automation environments. A method called Semantic Latent Choice Detection is presented, designed to systematically identify interpretation ambiguities in process instructions and operator commands. Unlike existing approaches that require access to the internal model architecture (“white box”) or token-level logits, the proposed method is logit-free and operates with closed commercial LLMs (“black box”) via standard API interfaces. The method analyzes the semantic similarity of binary text blocks and polysemous terms within the context of a specific technological process. Using a metallurgical production case study, we demonstrate how the system detects hidden semantic collisions (e.g., the difference between “adding ferroalloys into the ladle” and “feeding ferroalloys onto the conveyor”) that are missed by traditional rule-based validation methods. Instead of arbitrarily selecting an interpretation, the system initiates a clarification request to the human operator, thereby reducing cognitive load, preventing erroneous automated decisions, and increasing trust in the KMS. An empirical evaluation conducted in a real-world industrial setting (unit control rooms and dispatch centers) shows a statistically significant reduction in errors related to misinterpretation of process regulations. The article contributes to the fields of automation engineering, knowledge management, and human-centered automation by proposing a novel method for validating operational instructions in high-risk industrial environments. Full article

Background/Objectives: Muscular dystrophies (MDs) are progressive neuromuscular disorders in which rehabilitation is central to management, yet conventional physical therapy in children is constrained by motivation, accessibility, and the need to adapt across disease stages. Virtual reality (VR) offers an interactive, adaptable, and home-deliverable alternative, but prior reviews focused narrowly on upper-limb outcomes in Duchenne muscular dystrophy or on motor-learning paradigms. We aimed to evaluate VR-based rehabilitation in children and adolescents with MD across feasibility/adherence, engagement and psychological outcomes, and clinical motor outcomes, and to propose a stage-based conceptual framework. Methods: PubMed, Embase, and Cochrane CENTRAL were searched on 21 April 2026, following PRISMA 2020 (PROSPERO CRD420261380539). Eligible studies enrolled children or adolescents (mean age ≤ 18 years or separable pediatric data) with any MD who received VR/AR/MR/exergame/serious-game rehabilitation. Risk of bias was assessed with RoB 2.0 and ROBINS-I, and certainty with GRADE. Given substantial heterogeneity, findings were synthesized narratively by disease stage. Results: Eight studies (2017–2024; 221 participants) met the inclusion criteria. No serious VR-related adverse events occurred, and feasibility and tolerability were consistently favorable. Engagement and psychological outcomes showed favorable trends, including sustained motivation and reduced perceived fatigue. Clinical motor outcomes were heterogeneous and stage-dependent. Conclusions: The evidence base is limited and clinically heterogeneous, precluding meta-analysis, with Low GRADE certainty for feasibility, safety, and adherence and Very low for the remaining four domains. Key limitations include small sample sizes, substantial clinical and methodological heterogeneity, and only a single advanced-stage study. The findings provisionally support a stage-dependent role for VR-based rehabilitation in pediatric MD: motor training in the ambulatory stage, upper-limb maintenance and interface-adapted training in the transitional stage, and feasibility-, engagement-, and psychological-support applications in advanced disease. Stage-stratified trials with standardized, domain-specific outcomes and explicit virtual-to-real transfer assessment are warranted. Full article

In sulfide-based all-solid-state battery (ASSB) composite cathodes, incomplete solid–solid contact and tortuous ionic/electronic transport pathways increase internal electrode resistance and complicate the interpretation of apparent impedance responses. Here, we present a distribution of relaxation times (DRT)-assisted phenomenological impedance approach for analyzing apparent impedance responses in terms of operational resistance components in composite cathodes based on LiNbO₃-coated Ni-rich layered oxide cathode active materials. Electrochemical impedance spectroscopy was performed under controlled electrode loading, state of charge (SoC), and temperature conditions. Loading-dependent DRT analysis parameterized the apparent impedance response into five operational resistance components. The high-frequency components remained nearly unchanged or increased with increasing loading, whereas the mid- to low-frequency components generally decreased, suggesting opposite loading dependences between components tentatively associated with electrode-structural constraints and interface-related processes. SoC-dependent analysis compared relatively SoC-insensitive and SoC-sensitive operational components, while temperature-dependent analysis provided additional comparative constraints for their proposed operational interpretations by comparing their apparent activation energies. Based on these operational component correlations, a semi-empirical framework was developed to describe how the loading-dependent evolution of the DRT-deconvoluted components is reflected in the apparent impedance response. This framework helps reduce the risk of misinterpreting apparent impedance as a uniquely defined interfacial resistance and provides a practical basis for diagnosing structural limitations in high-loading ASSB composite cathodes. Full article

The gut microbiome of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) is increasingly recognized as a functional interface linking dietary inputs, epithelial barrier integrity, mucosal immunity, environmental stress, disease susceptibility, and antimicrobial-resistance risk in intensive aquaculture. Based on available salmonid studies and relevant evidence from broader fish and aquaculture systems, this review synthesizes current knowledge on salmonid gut microbial composition, nutritional modulation, microbiome–mucosal immune interactions, aquaculture stressors, antibiotic exposure, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), metagenomics, multi-omics, and emerging microbiome-informed decision-support tools. Current evidence does not support a universally stable single-core microbiota in these species. Instead, community structure is shaped by developmental stage, freshwater–seawater transition, intestinal segment, digesta versus mucosa sampling, diet, temperature, stress, health status, and methodological workflow. Feed substitution and functional additives can remodel the gut microbiota, but these shifts should be interpreted alongside histology, barrier function, metabolic profiles, immune indicators, and disease-resistance phenotypes. Antibiotic exposure may reduce acute bacterial disease pressure while disturbing community structure and potentially enriching ARGs or ARG–MGE associations. Risk assessment should therefore move beyond ARG abundance toward host–ARG–MGE linkage using shotgun metagenomics, metagenome-assembled genomes, long-read sequencing, Hi-C, and externally validated multi-omics models. Machine learning and artificial intelligence approaches may support feature screening, risk stratification, and decision support, but their application in salmonid gut-health management remains at an early stage and requires external validation across sites, production stages, diets, and seasons. Full article

Concealed shallow karst along long-distance pipeline corridors can trigger subsidence, uneven settlement, and leakage, creating environmental and infrastructure hazards. In the Huyuanxi area (Fuyang District, Hangzhou, Zhejiang, China), strong electromagnetic interference and limited site access motivated an integrated electrical resistivity tomography (ERT) plus ambient-noise microtremor (SPAC) workflow for shallow-karst screening. Three ERT lines (900 m each) were deployed along the pipeline axis and at ±15 m offsets with 10 m spacing using a WDJD-4 system (100 V constant-voltage; Wenner array, 30 layers), followed by resistivity inversion; Res2Dinv v3.65 was adopted as the inversion software. The L2 norm was selected for the objective function, and the error model was set to the default error floor plus 5%. The regularization parameter was set as λ = 0.01, and adaptive gridding was used for the mesh with a minimum cell size of 0.5 m × 0.5 m. The number of iterations was set to 15, with a final root mean square (RMS) misfit of 3.2%. The depth of investigation (DOI) was calculated via the built-in algorithm of the software, yielding a maximum value of 30 m. Low-resistivity anomalies were used to focus eight perpendicular microtremor profiles (3 m spacing) acquired with SmartSolo IGU-16HR 1C and 10 geophones (5 Hz; 1 ms sampling interval) in a nested SPAC array (0.5/1/2 m radii); processing removed segments with SNR < 3 and inverted 2-D Vs structure by damped least-squares. Resistivity sections show 50–8600 Ω·m near surface, including a <100 Ω·m fracture-zone anomaly (28–30 m wide; 8–18 m depth) and a cavity-zone anomaly (55–70 m wide; 10–20 m depth). Joint interpretation places karst development mainly at 10–25 m depth near the bedrock–cover interface (~16 m). At HYXK3, microtremor versus shear-wave logging yielded a void-layer bottom depth of 21.28 m versus 20.12 m (5.76% error) and Vs of 457 versus 446 m/s (2.40% error). Example profiles show microtremor-derived depths (18.3/14.5/18.7 m) consistent with ERT (16.8/15.1/19.5 m; 4.1–8.1% errors). Drilling verification accuracy was approximately 81.7%, precision approximately 90%, recall approximately 74.0%, and the F1-score 81.1% supporting practical corridor risk screening under complex field constraints. Full article

Background: Tooth-supported fixed dental prostheses (FDPs) remain relevant when implant therapy is limited, but their mechanical behavior depends on material selection, connector design, retainer design, prosthesis configuration, and abutment support. This systematic review assessed how these factors affect fracture behavior and stress transmission in tooth-supported FDPs. Materials and Methods: PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Dentistry and Oral Sciences Source were searched for English-language studies published from 1 January 2016 to 15 May 2026. Eligible studies were in vitro mechanical, fatigue, fracture-resistance, or finite element analysis (FEA) studies of tooth-supported FDP designs. Clinical studies were screened during eligibility assessment, but no clinical study met the final inclusion criteria for primary synthesis. In vitro components were appraised with the Quality Assessment Tool for In Vitro Studies (QUIN), and FEA components were appraised with the Risk-of-bias Framework for Dental Finite Element Analysis (ROBFEAD). Findings were synthesized narratively by evidence type and biomechanical theme. Results: Twenty-nine studies were included: 11 in vitro-only studies, 14 FEA-only studies, and four combined experimental and computational studies. No eligible clinical study met the final inclusion criteria. Zirconia-based systems were the most frequent focus. Their behavior depended on connector dimensions, connector shape, framework design, span, retainer configuration, loading direction, abutment selection, periodontal support, and bone support. Larger connector dimensions or greater connector height often improved fracture resistance or reduced modeled stress in zirconia models, but connector area alone did not explain performance across all materials and designs. Conservative FDPs were sensitive to retainer geometry, adhesive-interface behavior, connector design, and abutment support. Conclusions: Current evidence is limited to laboratory and computational studies. Tooth-supported FDP biomechanics should be interpreted as a material, design, and support system, not as a material effect alone. Full article

This paper presents a risk-averse information-gap decision theory (IGDT)-based day-ahead scheduling framework for active distribution networks with high penetration of inverter-interfaced resources. The proposed day-ahead strategy coordinates active and reactive power scheduling in an active distribution network comprising renewable generation, diesel units, demand-side management, electric vehicle charging stations, and energy-storage-equipped soft open points. The corresponding deterministic operating condition is then used as the reference state for uncertainty analysis. The scheduling problem is formulated as a mixed-integer nonlinear programming (MINLP) model considering network operating constraints and voltage-dependent load characteristics. Uncertainty associated with load demand and renewable generation is addressed using the IGDT risk-averse approach to quantify admissible uncertainty. The proposed methodology is implemented on a modified IEEE 33 bus distribution system considering deterministic operation, load-demand uncertainty, renewable-generation uncertainty, and simultaneous uncertainty in both load demand and renewable generation. The optimization model is developed in GAMS and solved using the DICOPT solver. The simulation results demonstrate the capability of the proposed framework to accommodate simultaneous load-demand and renewable-generation uncertainty within a predefined operating-cost threshold while maintaining secure network operation. Full article

Drug repurposing represents a rational and resource-efficient strategy to expand the oncological armamentarium by leveraging the established pharmacology, clinical experience, and safety-monitoring frameworks of approved non-oncological agents. Clozapine (CZP), an atypical antipsychotic characterized by broad receptor pharmacology, complex biotransformation, and clinically relevant toxicological liabilities, has emerged as a candidate of interest following preclinical evidence of context-dependent anticancer activity across multiple tumor types. As such, CZP provides an informative case study at the interface between therapeutic drug action and xenobiotic behavior. This review provides a critical and integrated synthesis of the current evidence supporting the repurposing of CZP in oncology, with particular emphasis on the relationship between its molecular mechanisms, dose–exposure requirements, pharmacological complexity, and potential toxicity. Analysis of in vitro and in vivo studies across glioblastoma, non-small cell lung cancer, breast cancer, and melanoma brain metastasis models indicates that CZP can impair tumor cell proliferation and survival through a form of mechanistic plasticity. Rather than acting through a single conserved pathway, CZP appears to disrupt shared upstream processes related to pro-survival signaling, cellular stress tolerance, and metabolic homeostasis, while engaging tumor-specific downstream responses, including autophagic cell death, mitochondria-dependent apoptosis, oxidative stress, and coordinated modulation of survival and angiogenic pathways. Despite this mechanistic rationale, translation remains substantially constrained, most notably by the order of magnitude gap between anticancer-effective concentrations in vitro and clinically achievable plasma exposures, requiring careful distinction between potentially useful anticancer pharmacology and nonspecific xenobiotic-induced cellular stress and clinically unacceptable toxicity. Key limitations include the discrepancy between anticancer-effective concentrations observed in vitro and exposures achievable during standard psychiatric dosing, the limited understanding of how CZP metabolism and metabolite formation may influence efficacy and toxicity, the absence of integrated pharmacokinetic–pharmacodynamic and toxicokinetic modeling, and the lack of dedicated clinical trial evidence. To address these challenges, this review examines complementary translational strategies, including patient-derived organoids, co-culture systems, microphysiological platforms, pharmacokinetic and toxicological modeling, and computational digital twin frameworks. Together, these approaches may support a biologically informed and risk-aware evaluation of CZP, helping to identify responsive tumor contexts, anticipate exposure-related liabilities, and prioritize rational combination strategies. By integrating therapeutic potential with xenobiotic pharmacology and toxicology, this review positions CZP within the evolving landscape of precision oncology and evidence-driven drug repurposing. Full article

Objective: This systematic review aimed to evaluate the effects of biomimetic surface engineering strategies applied to dental implants on osseointegration and peri-implant bone regeneration compared with conventional implant surfaces. Materials and Methods: A comprehensive literature search was conducted in the Web of Science, PubMed, and Scopus databases in accordance with the PRISMA guidelines, covering the period from January 2021 to January 2026. A total of 12 studies, including in vivo animal experiments and in vitro investigations, that met the inclusion criteria were analyzed. Risk of bias assessment was performed using the SYR-CLE tool and the ARRIVE guidelines. Results: Biomimetic strategies, including laser texturing, sulfonation, bioactive coatings, and growth factor/peptide functionalization (e.g., BMP-2, FGF-2, and PRF), significantly increased bone–implant contact (BIC), new bone volume (BV/TV), and biomechanical stability (pullout strength and reverse torque) compared to conventional surfaces. These surfaces enhance fixation under conditions of low bone density, such as osteoporosis, and improve infection resistance through antibacterial activity. In addition, these modifications enhance cellular adhesion, osteogenic differentiation, angiogenesis, and immune modulation. Conclusions: Current experimental evidence suggests that biomimetic implant surface engineering transforms dental implants from passive biomaterials into multifunctional bioactive interfaces capable of simultaneously regulating osteogenesis, immune response, angiogenesis, and antibacterial activity. Although promising outcomes have been demonstrated in preclinical studies, standardized long-term human clinical studies are still required to validate translational potential and long-term clinical efficacy. Full article

Chitosan and chitin-derived biomaterials, including native chitosan and chemically modified derivatives, have been widely investigated across orthopedic tissue engineering, implant functionalization, infection control, local delivery, and interface repair, but the evidence is dispersed across heterogeneous formats and indications. This single-author structured narrative review synthesizes 258 unique publications and interprets chitosan through a polymer design, quantitative performance, and clinical translation framework. Literature was identified (January–May 2026) using PubMed/MEDLINE as the primary database, with targeted verification in Web of Science, Scopus, and Google Scholar; no formal risk-of-bias or certainty grading was performed. Chitosan was studied as scaffolds, hydrogels, coatings, nanoparticles, microspheres, fibers, bioadhesives, bone-cement additives, cartilage adjuncts, tendon-to-bone systems, and intervertebral disk biomaterials. The highest human clinical evidence supported BST-CarGel/chitosan–blood implant augmentation of knee marrow stimulation, where randomized, 5-year, and biopsy data favored structural repair over microfracture alone; most other applications—bone regeneration, coatings, osteomyelitis hydrogels, bone cements, tendon/rotator cuff systems, and disk biomaterials—remain preclinical or translational-preclinical. Chitosan should be interpreted as a tunable polymer platform, not a single material; translation requires chemistry-defined formulation, indication-specific mechanical qualification, clinically relevant comparators, and standardized reporting. Full article

Robo-advisors have expanded access to automated investment services, but many platforms continue to rely on relatively static onboarding procedures and limited forms of user interaction. This study examines how participants with investment experience respond to two next-generation robo-advisory design features: financial digital twins, understood as dynamic investor profiles that integrate goals, risk tolerance, cash-flow patterns, and anticipated life events, and conversational artificial intelligence (AI), understood as an interactive interface for explaining recommendations. Using a scenario-based randomized 2 × 2 online experiment, 336 adult respondents with self-reported investment experience, recruited through professional and academic networks, were assigned to one of four robo-advisor scenarios that varied the personalization architecture, standard profile versus digital twin, and the interface style, plain dashboard versus conversational AI, while holding the portfolio recommendation constant. The results show that digital-twin personalization increases perceived personalization and privacy concern, indicating that more adaptive advisory architectures may be viewed as both more relevant and more data-intensive. Conversational AI increases the perceived interactive quality of the advisory experience, while selected willingness-related patterns, especially in the combined digital-twin and conversational-AI condition, are treated as exploratory because several secondary composites displayed limited internal consistency. The strongest confirmatory emphasis is therefore placed on perceived personalization and privacy concern, and the remaining findings are best interpreted as scenario-based investor responses rather than evidence of actual adoption behavior or confirmed psychological mechanisms. The study contributes to behavioral FinTech research by clarifying the personalization–privacy tension in AI-enabled robo-advisory services and by offering design implications for more transparent, interactive, and responsibly personalized digital wealth-management systems. Full article

With the development of space-based cloud computing and on-orbit intelligent processing, higher requirements have been imposed on standardized network interconnection for spaceborne platforms. However, constrained by size, power consumption, thermal design, and structural layout, some spaceborne platforms lack physical Ethernet interfaces and therefore cannot directly support standard Internet Protocol version 6 (IPv6) communications. In addition, harsh spaceborne operating conditions, including thermal-vacuum stress and potential radiation-induced disturbances, increase the risk of link anomalies, state inconsistency, and service interruption. To address these issues, this paper proposes a reliability-enhanced IPv6 access and transmission method for spaceborne platforms without physical Ethernet interfaces. On the processor side, a network TAP interface is established to reconstruct the semantics of a standard Layer-2 network device. Combined with a cooperative central processing unit–field-programmable gate array (CPU–FPGA) link-carrying mechanism, the proposed method enables transparent IPv6 access without modifying the native Linux protocol stack. To satisfy both standard spacecraft onboard network services and high-throughput engineering data transmission, a dual-channel architecture is designed, in which the service network channel is separated from the engineering data channel. In addition, a hierarchical reliability-oriented mechanism is constructed, consisting of hardware-level fault-tolerance design, reliable link interaction, status monitoring, and redundancy takeover. Experimental validation is conducted on a CPU-FPGA prototype platform under a thermal-vacuum environment and representative abnormal operating scenarios. The results show that the proposed method can stably support IPv6 address configuration, neighbor discovery, and end-to-end communication. Under zero-packet-loss conditions, the service network channel achieves an average stable throughput of 173.8 Mb/s, while the engineering data channel achieves a stable throughput of approximately 3.4 Gb/s. The system also demonstrates good service continuity during long-duration operation and under typical abnormal scenarios. The proposed method provides a verifiable system-level solution for realizing standardized IPv6 network access and reliability-enhanced data transmission on interface-constrained spaceborne platforms. Full article