Search Results (3,139)

Gait recognition using wearable sensor data is crucial for healthcare, rehabilitation, and monitoring neurological and musculoskeletal disorders. This study proposes a deep learning framework for gait classification using inertial measurements from four body-mounted IMU sensors (head, lower back, and both feet). The data were collected from a publicly available, clinically annotated dataset comprising 1356 gait trials from 260 individuals with diverse pathologies. The framework, G-MASA-TCN (Gait Multi-Anchor, Space-Aware Temporal Convolutional Network), integrates multi-scale temporal fusion, graph-informed spatial modeling, and residual dilated convolutions to extract discriminative gait signatures. To ensure both high performance and interpretability, Integrated Gradients is incorporated as an explainable AI (XAI) method, providing sensor-level and temporal attributes that reveal the features driving model decisions. The framework is evaluated via repeated cross-validation experiments, reporting detailed metrics with cross-run statistical analysis (mean ± standard deviation) to assess robustness. Results show that G-MASA-TCN achieves 98% classification accuracy for neurological, orthopedic, and healthy cohorts, demonstrating superior stability and resilience compared to baseline architectures, including Gated Recurrent Unit (GRU), Transformer neural networks, and standard TCNs, and 98.4% accuracy in identifying individual subjects based on gait. Furthermore, the model offers clinically meaningful insights into which sensors and gait phases contribute most to its predictions. This work presents an accurate, interpretable, and reliable tool for gait pathology recognition, with potential for translation to real-world clinical settings. Full article

Background: Computer-guided static implant surgery (CGSIS) is widely adopted to enhance the precision of dental implant placement. However, significant heterogeneity in reported accuracy values complicates evidence-based clinical decision-making. This variance is likely attributable to a fundamental lack of standardization in the methodologies used to assess dimensional accuracy. Objective: This scoping review aimed to systematically map, synthesize, and analyze the clinical methodologies used to quantify the dimensional accuracy of CGSIS. Methods: The review was conducted in accordance with the PRISMA-ScR guidelines. A systematic search of PubMed/MEDLINE, Scopus, and Embase was performed from inception to October 2025. Clinical studies quantitatively comparing planned versus achieved implant positions in human patients were included. Data were charted on study design, guide support type, data acquisition methods, reference systems for superimposition, measurement software, and accuracy metrics. Results: The analysis of 21 included studies revealed extensive methodological heterogeneity. Key findings included the predominant use of two distinct reference systems: post-operative CBCT (n = 12) and intraoral scanning with scan bodies (n = 6). A variety of proprietary and third-party software packages (e.g., coDiagnostiX, Geomagic, Mimics) were employed for superimposition, utilizing different alignment algorithms. Critically, this heterogeneity in measurement approach directly manifests in widely varying reported values for core accuracy metrics. In addition, the definitions and reporting of core accuracy metrics—specifically global coronal deviation (range of reported means: 0.55–1.70 mm), global apical deviation (0.76–2.50 mm), and angular deviation (2.11–7.14°)—were inconsistent. For example, these metrics were also reported using different statistical summaries (e.g., means with standard deviations or medians with interquartile ranges). Conclusions: The comparability and synthesis of evidence on CGSIS accuracy are significantly limited by non-standardized measurement approaches. The reported ranges of deviation values are a direct consequence of this methodological heterogeneity, not a comparison of implant system performance. Our findings highlight an urgent need for a consensus-based minimum reporting standard for future clinical research in this field to ensure reliable and translatable evidence. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

Background: Breast cancer-related lymphedema is a common long-term complication of breast cancer treatment that affects physical functioning, emotional well-being, and quality of life. Although the Lymphedema Quality of Life Questionnaire-Arm (LYMQoL-Arm) is widely used internationally, no Croatian version has been available. The primary objective of this study was to translate and validate the Lymphedema Quality of Life Questionnaire-Upper Limb-Croatian (LYMQoL-UL-CRO) version and evaluate its psychometric properties. A secondary objective was to examine associations between its scores and the relative volume change (RVC) of the affected limb to assess construct validity further. Methods: A retrospective cross-sectional study was conducted in 87 women at least six months post-treatment. The questionnaire was translated using a forward-backward procedure. Participants completed the LYMQoL-UL-CRO, the Short Form-36 Health Survey (SF-36), Pain Intensity Numerical Rating Scale, and underwent clinical examination and limb-volume assessment. Test–retest reliability was assessed in 68 participants after 10 days. Psychometric analyses included internal consistency, intraclass correlation coefficients, measurement error indices, construct and discriminant validity tests, exploratory factor analysis, and evaluation of floor and ceiling effects. Results: LYMQoL-UL-CRO domains demonstrated acceptable to strong internal consistency and moderate test–retest reliability, with low measurement error. Strong negative correlations with the SF-36 Physical Component Summary supported construct validity, and participants with RVC ≥ 5% reported worse scores, supporting discriminant validity. Exploratory factor analysis confirmed the original four-factor structure, and no floor or ceiling effects were observed. Conclusions: The LYMQoL-UL-CRO is a reliable, valid, and culturally appropriate tool for assessing quality of life in Croatian breast cancer survivors with upper-limb lymphedema. Full article

Image-guided surgical procedures demand tracking systems that combine high accuracy, low latency, and minimal footprint to ensure safe and precise navigation in the operating room. To address these requirements, we developed a monocular optical tracking system based on a single near-infrared camera with directional illumination and compact retroreflective markers designed for short-range measurement. Small dodecahedral markers carrying fiducial patterns on each face were fabricated to enable robust detection in confined and variably illuminated surgical environments. Their non-metallic construction ensures compatibility with CT and MRI, and they can be sterilized using standard autoclave procedures. Multiple fiducial families, detection strategies, and optical hardware configurations were systematically assessed to optimize accuracy, depth of field, and latency. Among the evaluated options, the ArUco MIP_36h12 family provided the best overall performance, yielding a translational error of 0.44 ± 0.20 mm and a rotational error of 0.35 ± 0.16° across a working distance of 30–70 cm, based on static position estimates, with a total system latency of 32 ± 8 ms. These results indicate that the proposed system offers a compact, versatile, and precise solution suitable for high-accuracy navigated and image-guided surgery. Full article

Foodborne diseases remain a major global challenge because pathogenic microorganisms persist in food systems, often protected by biofilms and increasing resistance to conventional chemical preservatives and sanitizers. Control strategies that were effective in the past are becoming less reliable in complex processing environments, creating a need for more precise and adaptable food-safety approaches. This review examines emerging technologies that shift food safety from broad, reactive control toward targeted, data-driven intervention. Biological tools, including bacteriophages, phage-derived enzymes, bacteriocins, quorum-sensing inhibitors, and gene-guided antimicrobial systems, are discussed for their capacity to selectively control specific pathogens while limiting unintended effects on beneficial microbiota. The review also addresses nano-enabled strategies that improve antimicrobial stability, delivery, and performance, along with plant-derived and microbial bioactive compounds that support clean-label and sustainable preservation. In parallel, advances in anti-biofilm surface engineering, such as nano-textured, contact-active, and responsive materials, are examined as preventive measures to reduce microbial attachment and persistence on food-contact surfaces. Beyond individual interventions, this review emphasizes integration within coordinated multi-hurdle systems supported by real-time monitoring and predictive analytics. Emerging digital frameworks, including digital twins of food-processing lines, are highlighted as tools to link detection, risk prediction, and targeted control. Finally, remaining knowledge gaps, regulatory challenges, and research priorities are identified, highlighting the need for realistic testing, long-term safety evaluation, standardized validation, and collaborative efforts to translate precision food-safety technologies into dependable real-world applications. Full article

The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. Although Artificial Intelligence (AI) is widely promoted as a lever for profitability, the mechanisms through which AI adoption translate into financial outcomes remain insufficiently specified in the oil and gas literature. Grounded in the Resource-Based View and Technology Adoption Theory, this study combines bibliometric mapping of 201 Scopus-indexed publications (2010–2025) with a focused comparative case analysis of important players (BP and Shell), based on publicly reported operational and financial indicators (e.g., operating cost, uptime-related evidence, and return on average capital employed—ROACE). Keyword co-occurrence analysis identifies five thematic clusters showing that efficiency-oriented AI use cases (optimization, automation, predictive maintenance, and digital twins) dominate the research landscape. A thematic synthesis of five highly cited studies further indicates that AI-enabled operational improvements are most consistently linked to measurable cost, productivity, or revenue effects. Case evidence suggests that large-scale predictive maintenance and digital twin programs can support capital efficiency by reducing unplanned downtime and structural costs, contributing to more resilient ROACE trajectories amid price swings. Overall, the findings support a conceptual pathway in which operational efficiency is a primary channel through which AI can create financial value, while underscoring the need for future firm-level empirical mediation tests using standardized KPIs. Full article

This study empirically examines how organizational learning influences problem-solving competency and organizational effectiveness in the context of online travel agencies (OTAs) and tests the moderating role of digital empowerment. Using agency lists registered under Korea’s Tourism Promotion Act, we employed stratified sampling by region and simple random sampling within strata. Data collection was commissioned by the Tourism/Leisure HRD Council. A survey was carried out from 2 to 19 June 2025; of the 210 questionnaires returned, 204 valid responses were analyzed. Measures were adapted from prior studies on a five-point Likert scale. Analyses conducted in SPSS 27.0 included descriptive statistics, exploratory factor analysis (EFA), reliability testing (Cronbach’s α), correlation analysis, and simple and hierarchical regressions. The results indicate that (1) organizational learning has a significant positive effect on problem-solving competency (β = 0.541, p < 0.001, R² = 0.293); (2) organizational learning positively affects organizational effectiveness (β = 0.436, p < 0.001, R² = 0.190); and (3) problem-solving competency positively influences organizational effectiveness (β = 0.624, p < 0.001, R² = 0.389). Regarding moderation, digital empowerment did not significantly moderate the organizational learning → problem-solving link but did significantly moderate the organizational learning → organizational effectiveness relationship (p < 0.05), suggesting that digital empowerment enhances the conversion efficiency of learning into performance. Theoretically, this study substantiates the learning–problem-solving–performance mechanism in a service/tourism setting and identifies digital empowerment as a catalytic moderator that strengthens the translation of learning into organizational outcomes. Practically, the findings imply that OTAs can amplify organizational effectiveness by building digital empowerment structures—data-driven decision systems, process automation, and real-time customer-response capabilities—which enable learned knowledge to materialize into performance. Future research should incorporate digital maturity, leadership, customer orientation, and related variables into extended models. Full article

Background: Chinese adolescents face a dual burden of malnutrition, yet the weight-status-specific mechanisms underlying physical activity (PA) participation remain underexplored. Methods: We conducted a cross-sectional study among 1573 adolescents (aged 9–15 years) in Shangrao City, China. Validated scales measured social-ecological factors (family/peer support, physical environment), psychological factors (stage of change, self-efficacy, decisional balance), and PA participation. Data preprocessing utilized full information maximum likelihood to handle missing values. Confirmatory factor analysis was performed to validate the measurement model, followed by multi-group structural equation modeling to analyze pathway configurations across underweight (n = 187), normal-weight (n = 1070), and overweight/obese (n = 316) groups. Mediation effects were tested using bootstrapping with 5000 resamples. Results: Clear weight-specific patterns emerged. Normal-weight adolescents presented a fully functional comprehensive model where PA was predicted by the stage of change (β = 0.211, p < 0.001), friend support (β = 0.120, p < 0.001), self-efficacy (β = 0.092, p < 0.05), and perceived benefits (β = 0.095, p < 0.01). Underweight adolescents primarily relied on internal readiness driven by stage of change (β = 0.270, p < 0.001) and self-efficacy (β = 0.164, p < 0.05), with family support only indirectly influencing participation via psychological mediators. In contrast, overweight/obese adolescents showed a “socially dependent” pattern: friend support directly predicted PA levels (β = 0.136, p < 0.05), significantly enhanced self-efficacy (β = 0.370, p < 0.01), and effectively lowered perceived barriers (β = −0.165, p < 0.05). Additionally, the physical environment strongly impacted perceived benefits (β = 0.471, p < 0.01) but did not translate into action. Conclusions: These findings underscore the significant differences in PA determinants across the spectrum of malnutrition, necessitating targeted public health interventions to support the Healthy China 2030 initiative. Full article

Hydrodynamic cavitation (HC) is a green and readily scalable platform for the recovery and upgrading of bioactives from agri-food and forestry byproducts. This expert-led narrative review examines HC processing of citrus and pomegranate peels, softwoods, and plant protein systems, emphasizing process performance, ingredient functionality, and realistic routes to market, and contrasts HC with other green extraction technologies. Pilot-scale evidence repeatedly supports water-only operation with high solids and short residence times; in most practical deployments, energy demand is dominated by downstream water removal rather than the extraction step itself, which favors low water-to-biomass ratios. A distinctive outcome of HC is the spontaneous formation of stable pectin–flavonoid–terpene phytocomplexes with improved apparent solubility and bioaccessibility, and early studies indicate that HC may also facilitate protein–polyphenol complexation while lowering anti-nutritional factors. Two translational pathways appear near term: (i) blending HC-derived dry extracts with commercial dry protein isolates to deliver measurable functional benefits at low inclusion levels and (ii) HC-based extraction of plant proteins to obtain digestion-friendly isolates and conjugate-ready ingredients. Priority gaps include harmonized reporting of specific energy consumption and operating metrics, explicit solvent/byproduct mass balances, matched-scale benchmarking against subcritical water extraction and pulsed electric field, and evidence from continuous multi-ton operation. Overall, HC is a strong candidate unit operation for circular biorefineries, provided that energy accounting, quality retention, and regulatory documentation are handled rigorously. Full article

Obesity-associated inflammation underlies much of cardiometabolic pathology, reflecting the convergence of chronic, low-grade systemic immune activation with region-specific maladaptation of adipose depots. Among these, epicardial adipose tissue (EAT)—a visceral fat layer contiguous with the myocardium and sharing its microvasculature—functions as a cardio-proximal immunometabolic interface that influences atrial fibrillation, heart failure with preserved ejection fraction, and coronary atherogenesis through paracrine crosstalk. These relationships extend beyond crude measures of adiposity, emphasizing the primacy of local inflammatory signaling, adipokine flux, and fibro-inflammatory remodeling at the EAT–myocardium interface. Of importance, substantial weight reduction only partially reverses obesity-imprinted transcriptional and epigenetic programs across subcutaneous, visceral, and epicardial depots, supporting the concept of an enduring adipose memory that sustains cardiovascular (CV) risk despite metabolic improvement. Accordingly, therapeutic strategies should move beyond weight-centric management toward mechanism-guided interventions. Resolution pharmacology—leveraging specialized pro-resolving mediators and their cognate G-protein-coupled receptors—offers a biologically plausible means to terminate inflammation and reprogram immune–stromal interactions within adipose and CV tissues. Although preclinical studies report favorable effects on vascular remodeling, myocardial injury, and arrhythmic vulnerability, clinical translation is constrained by pharmacokinetic liabilities of native mediators and by incomplete validation of biomarkers for target engagement. This review integrates mechanistic, depot-resolved, and therapeutic evidence to inform the design of next-generation anti-inflammatory strategies for obesity-related CV disease. Full article

Chronic diseases disproportionately impact Indigenous peoples in Australia, with type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) representing leading causes of morbidity and mortality. Despite evidence supporting community-based exercise interventions for T2DM management, no culturally adapted programs utilizing wearable technology have been co-designed specifically with Indigenous Australian communities. This study protocol aims to determine if wearable-based exercise interventions can effectively prevent CVD development and manage T2DM progression in Indigenous Australians through culturally safe, community-led approaches. The PrIDE study protocol describes a mixed-methods translational research design incorporating Indigenous and Western methodologies across three phases: (1) co-designing culturally adapted exercise programs and assessment tools, (2) implementing interventions with wearable monitoring, and (3) conducting evaluation and scale-up assessment. Sixty-four Indigenous Australian adults with T2DM will be recruited across remote, rural/regional sites to self-select into either individual or group exercise programs using the Withings ScanWatch 2. Primary outcomes include cardiovascular risk factors, physical fitness, and health self-efficacy measured using culturally adapted tools. Indigenous governance structures will ensure cultural safety and community ownership throughout. The PrIDE protocol presents a novel approach to improving health equity while advancing understanding of wearable technology integration in Indigenous healthcare, informing future larger-scale trials and policy development. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features. Full article

Obstructive sleep apnea (OSA) is a prevalent disorder characterized by repeated upper airway collapse during sleep, significantly impacting quality of life. Orthodontists are increasingly recognized for their role in screening and managing anatomical factors contributing to airway obstruction. One such intervention is rapid maxillary expansion (RME), originally developed to address transverse maxillary deficiencies but now also studied for its influence on nasal and oropharyngeal airway dimensions. This literature review evaluates the effects of maxillary palatal expansion on airway volume and respiratory function. Evidence consistently shows increases in nasal cavity volume and reductions in nasal airway resistance, particularly in patients treated before the peak of skeletal growth. Some studies reported improvements in sleep outcomes and enhanced oxygen saturation following MARPE in adults with OSA. Results regarding changes in oropharyngeal volume were more variable, with several studies showing significant expansion. Factors influencing outcomes include patient age, skeletal maturity, appliance type, and aging modality. Hybrid and bone-borne expanders generally demonstrated greater skeletal effects compared to tooth-borne devices, though statistical significance was not always reached. While MARPE has shown promising results in non-obese adults with OSA, long-term stability of airway improvements and translation into consistent functional respiratory benefits remain uncertain. Overall, maxillary expansion demonstrates measurable skeletal and airway changes, with the greatest respiratory improvements in growing patients and selected adult populations. Incorporating patient-reported outcomes and standardized polysomnographic measures in future trials will be critical to determine whether these structural gains consistently translate into durable improvements in sleep-disordered breathing and quality of life. Full article