Search Results (305)

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article

Background/Objectives: Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent chemicals widely detected in aquatic systems and drinking water. Perfluorononanoic acid (PFNA), a long-chain PFAS, has been reported globally in environmental matrices and fish tissues. Although PFNA has been linked to developmental, metabolic, and neurological toxicity, its effects on lipid-related pathways and neurotoxicity remain poorly characterized. Methods: This study evaluated the developmental and neurotoxic effects of PFNA exposure in zebrafish embryos and larvae following a 7-day exposure to environmentally relevant PFNA concentrations. Results: PFNA exposure did not significantly affect survival or deformity rates at the concentrations tested. Apoptosis was significantly increased in larvae exposed to 1 µg/L PFNA compared to controls, whereas reactive oxygen species levels were unaffected. Two concentrations (0.1 µg/L and 10 µg/L) were further examined for transcriptomic responses, and the transcriptome response was largely different for each concentration. Low-dose PFNA exposure primarily affected lipid transport, cholesterol metabolism, sphingolipid signaling, and neurodegeneration-related pathways, whereas high-dose PFNA altered transcripts related to synaptic signaling, axon guidance, and thyroid hormone synthesis. Hypoactivity was observed in the movement of larval zebrafish based on a Visual Motor Response test. Conclusions: Taken together, PFNA exposure induced molecular changes related to neurotoxicity and lipid metabolism in zebrafish, which may contribute to adverse neurodevelopmental outcomes. Full article

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses. Full article

Background: Ancient depictions of Pankration techniques have traditionally been interpreted through qualitative comparison with modern combat sports, without systematic biomechanical evaluation. The present study examines whether postural configurations derived from archeological artifacts are geometrically compatible with a continuous sagittal-plane trajectory under constrained inverse kinematics. Methods: A reduced planar humanoid model with three active rotational degrees of freedom was implemented in MATLAB Simulink(2024b), and artifact-derived initial and terminal postures were treated as boundary conditions. An analytical inverse kinematics solution was used to generate a continuous end-effector trajectory, from which joint kinematics and center-of-gravity displacement were computed. Motion capture data from ten participants were used solely to assess whether the generated trajectory is physically executable within human joint limits. Results: The results demonstrated strong agreement in selected local horizontal joint trajectories, while larger discrepancies were observed in vertical motion and global center-of-gravity behavior, reflecting the limitations of the reduced model. Conclusions: The study provides a reproducible framework for evaluating the kinematic feasibility of artifact-derived movements under explicitly defined constraints, limited to the assessment of geometric compatibility and physical executability. Full article

Wilson’s disease (WD) is an autosomal recessive disorder of copper metabolism caused by ATP7B mutations, characterized by hepatic copper accumulation and multisystem involvement. Several rare inherited and acquired conditions can closely mimic WD, posing diagnostic challenges and the risk of inappropriate therapy. By examining neuroimaging patterns and distinguishing between diagnostic criteria, this narrative review provides a comprehensive synthesis of WD-mimicking disorders, emphasizing their molecular mechanisms, clinical phenotypes, and biochemical features. WD-mimicking disorders encompass ATP7A-related neurodegenerations (Menkes disease, occipital horn syndrome, X-linked distal hereditary motor neuropathy), MEDNIK syndrome, Huppke–Brendel syndrome, aceruloplasminemia, congenital disorders of glycosylation, primary familial intrahepatic cholestasis type 3, and acquired copper deficiency syndromes. Mechanisms include systemic copper deficiency, impaired intracellular trafficking, defective ceruloplasmin biosynthesis, secondary hepatic copper accumulation, and abnormal glycosylation. Clinical features range from neurodevelopmental delay, movement disorders, and hepatic dysfunction to dermatologic, hematologic, and connective-tissue abnormalities. Biochemical profiles may overlap with WD, particularly low serum ceruloplasmin and total copper, altered urinary copper excretion, and elevated hepatic copper in some disorders. Neuroimaging and genetic testing provide critical discriminative value. Management is largely supportive, with disease-specific therapies available in selected conditions, such as subcutaneous copper in Menkes disease or monosaccharide supplementation in certain congenital disorders of glycosylation subtypes. Accurate differentiation between WD and WD-mimicking disorders requires careful integration of clinical, biochemical, imaging, and molecular data. Recognition of distinctive features and understanding underlying pathophysiology are essential to avoid misdiagnosis and inappropriate anti-copper therapy, optimize management, and improve patient outcomes. Full article

Vegetated field borders are widely promoted as tools to enhance biodiversity and strengthen biological control in agroecosystems. However, their role in pest dynamics remains conceptually fragmented and empirically inconsistent. Here, we develop a unified framework explaining how crop border vegetation influences pest populations through four interlinked ecological mechanisms. First, borders act as host reservoirs and selective filters, providing alternative hosts and overwintering habitat that enhance pest persistence across crop cycles. Second, borders modify pest colonization dynamics by shaping movement, aggregation, and host-location behavior at crop edges. Third, borders restructure multitrophic networks, simultaneously supporting natural enemies, alternative prey, vectors, and pathogens, generating nonlinear effects on pest suppression. Fourth, repeated disturbance and management function as selective filters, determining which plant functional groups dominate borders and, consequently, which pest and natural enemy communities are maintained. To ground this framework, we conduct a structured synthesis of published empirical and conceptual studies on crop-border vegetation, including weed and arthropod surveys, and classify them according to the proposed mechanisms. Our synthesis reveals a strong emphasis on multitrophic effects, whereas colonization processes and disturbance filtering are comparatively underexplored. Across mechanisms, plant identity and dominance structure consistently emerge as stronger predictors of pest outcomes than species richness alone. We argue that borders are not inherently beneficial or harmful but function as selectively structured ecological interfaces shaped by management history and species composition. By integrating temporal persistence, spatial behavior, network interactions, and anthropogenic filtering, our framework provides a predictive basis for IPM-oriented design of field borders, enabling management strategies that reduce pest carryover, disrupt colonization pathways, and enhance biological control while maintaining ecosystem services. This article is part of the theme issue “The Biology, Ecology, and Management of Plant Pests”. Full article

Although the ATP-binding cassette (ABC) transporter superfamily of proteins typically facilitates the movement of compounds across cellular membranes, the ABC E subfamily (ABCE) influences protein synthesis via non-transporter roles in ribosome biogenesis. Despite this essential role, our understanding of the impact that ABCE proteins have on insect physiology is limited. Here, we identified and characterized the ABCE gene from Lygus hesperus, a major agricultural pest of crops in North America. LhABCE transcripts were constitutively expressed throughout development and were present in all adult tissues tested. RNA interference (RNAi)-mediated knockdown of LhABCE transcripts in fifth instar nymphs resulted in high nymphal mortality and an incomplete molt. LhABCE knockdown in adults disrupted gametogenesis and reduced longevity. In females, oogenesis was impaired and oocytes did not progress beyond the pre-vitellogenic phase. In males, LhABCE knockdown reduced both spermatozoa abundance and male fertility. LhABCE knockdown, however, had little to no impact on hemolymph protein levels or the levels of circulating vitellogenin. Taken together, the results indicate that LhABCE is critical for the normal progression of processes like molting and gametogenesis that require coordinated bursts of protein synthesis and suggest that ABCE may play an important role in the mechanisms underlying those bursts. Full article

Background/Objectives: Visuomotor interception requires aligning action with the future state of moving targets under sensory and motor delays. This constraint provides a tractable framework to examine how predictive and feedback-driven processes interact. This narrative review evaluates theoretical and empirical accounts of interception, with emphasis on how prediction and online control are integrated across behavioral and neural levels. Methods: We conducted a narrative synthesis of behavioral, eye-tracking, computational, and neurophysiological studies on visuomotor interception. Literature was identified through searches of PubMed, Web of Science, and Google Scholar using search terms including “visuomotor interception,” “predictive motor control,” “eye–hand coordination,” “time-to-contact,” “sensorimotor delay,” and related combinations. Studies published between 1986 and 2026 were considered, with emphasis on peer-reviewed empirical and theoretical work. Preprints were included only when directly relevant and are identified as such. The review compares internal model, ecological, and hybrid frameworks, and organizes evidence around spatial (“where”) and temporal (“when”) components of control. Results: Across paradigms, interception behavior is not well accounted for by purely predictive or reactive mechanisms. Instead, trajectories reflect a continuous interaction between anticipatory guidance and online correction. Spatial and temporal components show partial dissociation across tasks and manipulations. Available evidence supports the involvement of distributed circuits, including parietal, frontal, cerebellar, and subcortical systems, while indicating that eye movements play an active role in both information sampling and motor planning. Conclusions: Interception is best understood as the product of interacting biological, environmental, and learned constraints. Similar behavioral signatures can arise from distinct mechanisms, arguing against a unitary account. Progress requires integrating behavioral analyses with model-based and neural approaches to dissociate underlying computations. Full article

Traditional deadlift guidelines prioritize maintaining a neutral spine to prevent low back injuries. However, recent evidence questions whether moderate spinal flexion under load is inherently harmful, especially among trained individuals. This article proposes a modern, multifactorial framework for deadlift-related injury prevention that moves beyond rigid postural prescriptions. It integrates biomechanical evidence, load management strategies, movement variability principles, and dynamic trunk control. This narrative review synthesizes literature identified through structured searches of PubMed, Scopus, and Google Scholar, prioritizing peer-reviewed studies examining spinal biomechanics, load management, motor control, and injury epidemiology. Evidence suggests that trained lifters often exhibit natural lumbar flexion without clear prospective evidence of increased injury risk. Abrupt increases in training load appear to be consistently associated with elevated injury incidence, although relationships remain probabilistic and context-dependent. While technical factors, including spinal posture, may influence local tissue loading, current evidence suggests that rapid changes in training exposure and cumulative load management appear to be more consistent predictors of injury risk than isolated deviations from an externally defined “neutral” alignment. Movement variability appears protective, and dynamic trunk control is more functionally relevant than static core strength. A paradigm shift is needed in how deadlifts are coached and programmed. Injury prevention should emphasize progressive loading, adaptive movement strategies, and dynamic stability, rather than rigid technique enforcement. Rather than systematically appraising all available evidence, this review offers an interpretative synthesis to guide modern, evidence-informed coaching and rehabilitation practice. Full article

School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article

Background/Objectives: Children and adolescents on the autism spectrum often experience delays in both gross and fine motor skills, which can limit their participation in physical activity and everyday tasks. Methods: This narrative review synthesizes evidence from 88 peer-reviewed studies examining fundamental motor skills, broader motor competence, and perceived motor competence in individuals aged 3–18 years with a formal diagnosis of autism. Results: Across the literature, children with autism consistently demonstrate lower proficiency in locomotor and object control skills compared with their typically developing peers, while perceived competence emerges as an important factor influencing motivation and engagement. Intervention studies—most commonly school-based or structured physical activity programs—generally report short-term improvements in motor performance, although outcomes vary depending on study design, dosage, and assessment tools. The review also highlights substantial methodological heterogeneity and a notable lack of evidence concerning adolescents, underscoring the need for longitudinal and developmentally sensitive research. Conclusions: Practical implications are discussed for creating supportive movement environments in educational and adapted physical activity settings. This review follows a narrative synthesis approach informed by a structured search strategy. Full article

This systematic review aimed to assess whether the initial apical position of impacted maxillary canines, evaluated using cone-beam computed tomography [CBCT], influences three-dimensional root displacement during orthodontic traction. An extensive literature search was conducted in PubMed/MEDLINE, Web of Science, Embase, Scopus, and the Cochrane Library up to November 2025. Prospective and retrospective clinical studies including pre-treatment CBCT assessment and reporting either direct apical displacement or CBCT-derived three-dimensional position parameters were considered eligible. Study selection, data extraction, and quality appraisal were carried out independently by two reviewers. Seven studies met the inclusion criteria. Substantial heterogeneity was observed in imaging protocols, reference systems, traction mechanics, and outcome measures, precluding quantitative synthesis. Only two studies directly quantified three-dimensional apical displacement using CBCT–CBCT or CBCT–STL superimposition methods, predominantly suggesting bodily movement patterns; although, this is based on limited direct evidence, with velocities ranging from 0.29 to 0.84 mm/month. The remaining studies provided indirect evidence based on angular changes, positional parameters, or traction duration. Taken together, the available evidence suggests that unfavorable initial apical positions, including palatal or bicortical impactions and increased root angulation, may be associated with greater biomechanical complexity and longer traction duration. Although CBCT-based three-dimensional evaluation provides clinically relevant diagnostic information, standardized measurement protocols are required to improve comparability and reproducibility across studies. Full article

Logic synthesis is a critical stage in the VLSI design flow. Logic synthesis methods without considering physical information would result in inferior solutions with timing violations and fail to meet high-performance design requirements. In this paper, we present an analytical placement algorithm that generates timing-friendly physical information to promote high-performance logic synthesis solutions. To address the crucial congestion issue, we first propose a fence-region-aware density model. Then, a boundary-based quadratic penalty model is constructed to ensure the cells do not violate the legal boundaries. Finally, we develop a Polak–Ribière-based placement algorithm to guide the cell movement while optimizing circuit timing. Compared to the advanced placement work, the experimental results on industrial benchmarks show that our proposed algorithm achieves 7% WNS improvement and 12% TNS optimization with 3% better logic depth. Full article

Ports globally face intensifying pressure due to capacity constraints and chronic operational inefficiencies, yet a significant portion of hinterland transport capacity remains critically underutilized. This study addresses the existing research gap concerning the lack of an integrated perspective on empty truck movements by synthesizing interdisciplinary solutions from maritime logistics and supply chain collaboration. Grounded in a theory-driven approach utilizing the resource-based view, we systematically explore and categorize diverse concepts including chassis exchange terminals, street-turn strategies, and collaborative logistics networks. An extensive synthesis of academic and industry-related studies was conducted to formulate a hierarchical framework of vehicle operations that can be applied to port strategy formulation. Our results identify a strategic roadmap for sustainable port capacity management, demonstrating how analogies like code-sharing and collaborative transportation management can be successfully adapted to minimize unutilized trips. We conclude that transitioning from fragmented operations to an integrated, multi-disciplinary strategy is essential for optimizing hinterland transport chains. This research provides a significant original contribution by offering the first unified framework for the empty truck problem, providing port authorities and maritime stakeholders with a robust strategic toolkit to enhance operational resilience, resource efficiency, and long-term sustainability in the global supply chain. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article