Search Results (154)

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

This prospective, single-arm, exploratory postmarketing study preliminarily evaluated the clinical response and plasma biomarker changes in 18 client-owned dogs with naturally occurring osteoarthritis (OA) treated with sodium hyaluronate (Bonharen). Patients received intravenous injections of Bonharen Intravenous at a dose of 0.15 mL/kg (1.3–1.6 mg/kg hyaluronic acid once a week for consecutive five weeks). Clinical parameters (lameness, joint pain, mobility, swelling) were assessed weekly and two weeks after the final dose was given via standardized scoring. The plasma concentrations of selected inflammatory, cartilage-related, and oxidative stress biomarkers were measured before treatment and two weeks after the final dose. Clinical improvement in lameness and/or joint pain on palpation was observed in nearly half of the patients. No clinical deterioration was recorded at any time point. Physical activity increased in all patients with reduced baseline activity. Significant decreases in the plasma levels of prostaglandin E₂, Δ17-6-keto prostaglandin F1α, malondialdehyde, and hyaluronan were detected, indicating reduced systemic inflammation and oxidative stress. In addition, an increase in plasma hydroxybutyrate and decrease in the collagen-breakdown marker prolyl-hydroxyproline were observed. No adverse effects were reported. These findings suggest that intravenous hyaluronic acid (Bonharen) may represent a safe and promising component to multimodal OA management in dogs and demonstrate the feasibility of integrating plasma biomarkers in canine OA studies. Full article

This study aimed to evaluate the effectiveness of a multimodal trust assessment approach that integrated electroencephalography (EEG) and self-report questionnaires compared with unimodal methods within the context of lower-limb rehabilitation training. Twenty-one mobility-impaired participants performed tasks using handrails, walkers, and stairs. Synchronized EEG, questionnaire, and behavioral data were collected. EEG trust scores were derived from the alpha-beta power ratio, while subjective trust was assessed via questionnaire. An adaptive neuro-fuzzy inference system was used to fuse these into a composite score. Analyses included variance, correlation, and classification consistency against behavioral ground. Results showed that EEG-based scores had higher dynamic sensitivity (Spearman’s ρ = 0.55) but greater dispersion (Kruskal–Wallis H-test: p = 0.001). Questionnaires were more stable but less temporally precise (ρ = 0.40). The fused method achieved stronger behavioral correlation (ρ = 0.59) and higher classification consistency (κ = 0.69). Cases with discordant unimodal results revealed complementary strengths: EEG captured real-time neural states despite motion artifacts, while questionnaires offered contextual insight prone to bias. Multimodal fusion through fuzzy logic mitigated the limitations of isolated assessment methods. These preliminary findings support integrated measures for adaptive rehabilitation monitoring, though further research with a larger cohort is needed due to the small sample size. Full article

Background and objectives: Pain is a prevalent and often underestimated issue in children with cerebral palsy (CP). When left untreated, pain can result in secondary complications such as reduced mobility and mental health challenges, which negatively impact social activity, participation, and overall quality of life. This review explores the complex mechanisms underlying pain in CP, highlights contributing factors, and places particular emphasis on diagnostic challenges and multimodal pain management strategies. Methods: Three scientific databases and, additionally, guideline repositories (2015–2025) were searched, yielding 1335 records. Following a two-step deduplication process, 850 unique items remained. Eighty-five full texts were assessed, of which 49 studies were included. These comprised one randomised controlled trial, 16 non-randomised studies, 12 systematic reviews, 8 non-systematic reviews, and 12 guidelines or consensus statements. Methodological quality was appraised with AMSTAR-2 where applicable, and Oxford levels of evidence were assigned to all studies. Results: Study quality was variable: 25% were systematic reviews, with only one randomised controlled trial. This literature identifies overlapping nociceptive, neuropathic, and nociplastic mechanisms of pain development. Classification remains inconsistent, though the International Classification of Diseases provides a useful framework. Only five assessment tools have been validated for this population. Interventions were reported in 45% of studies, predominantly pharmacological (27%) and physiotherapeutic (23%). Evidence gaps remain substantial. Conclusions: This review highlights the complexity of pain in children and adolescents with cerebral palsy and the need for a biopsychosocial approach to assessment and management. Evidence supports individualised, multimodal strategies integrating physical therapies, contextual supports, and, where appropriate, medical or surgical interventions. Clinical implementation remains inconsistent due to limited high-quality evidence, inadequate assessment tools, and poor interdisciplinary integration. Full article

The need to address long-term sustainability goals while ensuring short-term resilience to unexpected disruptions is placing an increasing challenge on urban mobility systems. This study organizes an analytical framework that compares and integrates the concepts of sustainability and resilience in urban mobility. A scoping review and thematic analysis were conducted to identify and compare the definitions, dimensions, and operational features of these two paradigms. The results reveal that, although they are conceptually distinct, sustainability and resilience share subjects of analysis, including multimodality and diversity of transport modes, the impacts of climate change, and social equity issues. However, they also present tensions between the dimensions of efficiency and redundancy, speed of recovery and sustainability of implemented solutions, and new vulnerabilities introduced by sustainable technologies. These synergies and trade-offs underscore the necessity of an integrated, systemic and holistic approach to urban mobility planning. The study emphasizes that building resilient and sustainable urban mobility requires coherent policies across government levels, technical capacity, public engagement, and comprehensive indicators. Recommendations for future research include developing integrated metrics and planning tools to support evidence-based decision-making. Full article

As cities strive for human-centered and fine-tuned development, Agent-Based Modeling (ABM) has emerged as a powerful tool for simulating pedestrian behavior and optimizing walkable neighborhood design. This study presents a comparative bibliometric analysis of ABM applications in block-scale walkability research from 2015 to 2024, drawing on both Chinese- and English-language literature. Using visualization tools such as VOSviewer, the analysis reveals divergences in national trajectories, methodological approaches, and institutional logics. Chinese research demonstrates a policy-driven growth pattern, particularly following the introduction of the “15-Minute Community Life Circle” initiative, with an emphasis on neighborhood renewal, age-friendly design, and transit-oriented planning. In contrast, international studies show a steady output driven by technological innovation, integrating methods such as deep learning, semantic segmentation, and behavioral simulation to address climate resilience, equity, and mobility complexity. The study also classifies ABM applications into five key application domains, highlighting how Chinese and international studies differ in focus, data inputs, and implementation strategies. Despite these differences, both research streams recognize the value of ABM in transport planning, public health, and low-carbon urbanism. Key challenges identified include data scarcity, algorithmic limitations, and ethical concerns. The study concludes with future research directions, including multimodal data fusion, integration with extended reality, and the development of privacy-aware, cross-cultural modeling standards. These findings reinforce ABM’s potential as a smart urban simulation tool for advancing adaptive, human-centered, and sustainable neighborhood planning. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

Urban mobility plays a vital role in shaping sustainable cities, yet the effectiveness of public transportation is often undermined by poor last-mile connectivity (LMC). In the National Capital Region (NCR) of Delhi, despite the Delhi Metro Rail serving as a key transit system, limited integration with surrounding areas hinders accessibility, which particularly affects women, elderly adults, and socioeconomically disadvantaged groups. This study evaluates LMC performance at two key metro stations, Nehru Place and Botanical Garden, using a mixed-methods approach that includes user surveys, spatial survey, thematic analysis, and infrastructure scoring across five critical pillars: accessibility, safety and comfort, intermodality, service availability, and inclusivity. The findings communicate notable contrasts. Botanical Garden exhibits strong intermodal linkages, pedestrian-friendly design, and supportive signage, while Nehru Place indicates a need for infrastructural improvements, safety advancement and upgrades, and strengthened universal design features. These disparities limit effective metro usage and discourage a shift from private to public transport. The study highlights the importance of user-centered, multimodal solutions and the need for cohesive urban governance to address LMC gaps. By identifying barriers and opportunities for improvement, this research paper contributes to the formulation of more inclusive and sustainable urban transport strategies in Indian metropolitan regions. Full article

Lumbar spine pain (LBP) is a leading cause of disability worldwide and remains a major challenge in clinical practice. Among non-invasive treatment strategies, manual therapy plays a central role, offering individualized interventions that target both biomechanical dysfunction and pain. This narrative review compares three commonly used physiotherapeutic approaches—Kaltenborn-Evjenth mobilization, the McKenzie method, and high-velocity low-amplitude (HVLA) manipulation—based on current evidence regarding their effectiveness, safety, and clinical application. A total of 32 randomized controlled trials, systematic reviews, and meta-analyses published between 2003 and 2024 were analyzed. The Kaltenborn-Evjenth method demonstrated notable effectiveness in improving range of motion and reducing chronic pain, particularly in patients with segmental hypomobility. The McKenzie method showed strong outcomes in both acute and chronic LBP, especially in cases involving symptom centralization and high patient engagement. HVLA techniques offered rapid symptom relief in acute phases but required careful patient selection due to their mechanical intensity. The findings suggest that no single method is universally superior. Instead, optimal outcomes are achieved through individualized treatment plans that integrate multiple techniques based on clinical presentation, pain chronicity, and functional limitations. Multimodal strategies that combine manual therapy with exercise and patient education appear to be the most effective in managing LBP and preventing recurrence. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

The acceleration of global population aging has driven a surge in demand for health monitoring among older adults. However, traditional mobility assessment methods mostly rely on invasive measurements or laboratory-grade equipment, making it difficult to achieve continuous monitoring in daily scenarios. This study investigated the correlation between dynamic gait characteristics and static body metrics to enhance the understanding of elderly mobility and overall health. A sensor-based framework was implemented, which utilizes the Short Physical Performance Battery (SPPB), combined with PoseNet (a vision-based sensor) for dynamic gait analysis, and the InBody bioelectrical impedance device for static body composition assessment. Key variables comprised the dynamic metric mean directional shift and static metrics, including skeletal muscle index (SMI), skeletal muscle mass (SMM), body fat percentage (PBF), visceral fat area (VFA), and intracellular water. Nineteen elderly participants aged 60–89 years underwent assessments; among them, 16 were males (84.21%), and 3 were females (15.79%), 50% were in the 80–89 age group, 95% did not live alone, and 90% were married. Dynamic gait data were analyzed for center displacement and horizontal directional shifts. A Pearson correlation analysis revealed that the mean directional shift positively correlated with SMI ($\rho =0.561$, $p<0.01$), SMM ($\rho =0.496$, $p<0.01$), and intracellular water ($\rho =0.497$, $p<0.01$), highlighting the role of muscle strength in movement adaptability. Conversely, negative correlations were found with PBF ($\rho =-0.256$) and VFA ($\rho =-0.342$, $p<0.05$), suggesting that greater fat mass impedes dynamic mobility. This multimodal integration of dynamic movement patterns and static physiological metrics may enhance health monitoring comprehensiveness, particularly for early sarcopenia risk detection. The findings demonstrate the framework’s potential, indicating mean directional shift as a valuable dynamic health indicator. Full article

Multimodal transportation systems, particularly those combining metro and bikesharing, have become central to addressing the first- and last-mile connectivity challenges in urban environments. This study presents a comprehensive data-driven framework to analyze the spatiotemporal interplay between metro and dockless bikesharing usage using real-world data from Tianjin, China. Two primary methods are employed: K-means clustering is used to categorize metro stations and bike usage zones based on temporal demand features, and non-negative Tucker decomposition is applied to a three-way tensor (day, hour, station) to extract latent mobility modes. These modes capture recurrent commuting and leisure behaviors, and their alignment across modes is assessed using Jaccard similarity indices. Our findings reveal distinct usage typologies, including mismatched (misalignment of jobs and residences), employment-oriented, and comprehensive zones, and highlight strong temporal coordination between metro and bikesharing during peak hours, contrasted by spatial divergence during off-peak periods. The analysis also uncovers asymmetries in peripheral stations, suggesting differentiated planning needs. This framework offers a scalable and interpretable approach to mining multimodal travel patterns and provides practical implications for station-area design, dynamic bike rebalancing, and integrated mobility governance. The methodology and insights contribute to the broader effort of data-driven smart city planning, especially in rapidly urbanizing contexts. Full article

Gulf cities have embarked on ambitious public transport infrastructure initiatives in recent decades to foster more livable and sustainable cities. This investigation explores the interpretations and implementation of Transit-Oriented Development (TOD) principles in two prototypical urban districts: Doha’s West Bay, Qatar, and Riyadh’s King Abdullah Financial District (KAFD), Saudi Arabia. By following a comparative case study approach, the study explores how retrofitted (West Bay) and purpose-built (KAFD) TOD configurations fare regarding land use mix, density, connectivity, transit access, and environmental responsiveness. The comparative methodology was selected to specifically capture the spatial, climatic, and socio-economic complexities of TOD implementation in hyper-arid urban environments. Based on qualitative evidence from stakeholder interviews, spatial assessments, and geospatial indicators—such as metro access buffers, building shape compactness, and TOD proximity classification—the investigation reflects both common challenges and localized adaptations in hot-desert Urbanism. It emerges that, while benefiting from integrated planning and multimodal connectivity, KAFD’s pedestrian realm is delimited by climatic constraints and inactive active transport networks. West Bay, on the other hand, features fragmented public spaces and low TOD cohesion because of automotive planning heritages. However, it holds potential for retrofit through infill development and tactical Urbanism. The results provide transferable insights that can inform TOD strategies in other Gulf and international contexts facing similar sustainability and mobility challenges. By finalizing strategic recommendations for urban livability improvement through context-adaptive TOD approaches in Gulf cities, the study contributes to the wider discussion of sustainable Urbanism in rapidly changing environments and supplies a reproducible assessment frame for future TOD planning. This study contributes new knowledge by advancing a context-adaptive TOD framework tailored to the unique conditions of hyper-arid Gulf cities. Full article

Background: Falls are a leading cause of morbidity and loss of independence among older adults, and occupational therapy (OT) offers a unique, multidimensional approach to fall prevention. This systematic review evaluates the effectiveness of OT-based interventions for improving balance, mobility, functional performance, and psychological outcomes related to fall risk in older adults. Methods: This review followed PRISMA (2020) guidelines. A comprehensive search of PubMed, Scopus, Dialnet, and OTseeker was conducted from March to May 2025. The inclusion criteria targeted studies involving non-pharmacological, OT-led interventions in adults aged ≥65. Seventeen studies were selected, including randomized controlled trials, pilot studies, and quasi-experimental designs. The data extraction and quality appraisal were performed independently by two reviewers. Results: The included interventions varied among exercise-based programs (e.g., Tai Chi, Pilates), virtual reality training, home safety modifications, cognitive–behavioral therapy, and wearable technologies. Most of the studies reported significant improvements in postural balance, fear of falling, and functional independence. Environmental adaptations and educational strategies also yielded positive outcomes. However, a real-world fall incidence reduction was inconsistently reported, and the methodological heterogeneity limited the meta-analytic synthesis. Conclusions: Occupational therapy contributes significantly to fall prevention through multimodal, person-centered strategies that integrate physical, cognitive, and environmental components. Future research should aim to standardize the outcome measures, include high-risk populations, and assess the long-term efficacy and cost-effectiveness of OT-led programs. Full article