Search Results (9,689)

This review summarizes recent advances in intelligent spraying systems for precision agriculture. It focuses on the three interconnected components of perception, decision-making, and actuation, and compares the sensing characteristics of 2D planar field crops and three-dimensional orchard canopies. Particular emphasis is placed on evaluating the target recognition capabilities and physical limitations of machine vision, Light Detection and Ranging (LiDAR), ultrasound, and active fluorescence spectroscopy under complex field conditions. At the actuation level, the dynamic response characteristics of Pulse-Width Modulation (PWM), proportional control valves, and adaptive control algorithms are critically reviewed, revealing the influence mechanism of hydraulic transient phenomena, including water hammer effects induced by high-frequency valve switching, on droplet size distribution (DSD). The review further discusses the coupling effects between Unmanned Aerial Vehicle (UAV) airflow fields, Unmanned Ground Vehicle (UGV) motion disturbances, and spray deposition performance, and summarizes reported improvements in pesticide reduction, water conservation, and drift mitigation. Finally, the potential of cyber–physical systems (CPS) and digital twin-based frameworks for developing adaptive and closed-loop intelligent spraying systems is discussed to provide insights into future all-weather and autonomous agricultural operations. Full article

Mobile health (mHealth) apps increasingly capture the physiological, biomechanical, and psychological variables involved in sport training, but the evidence remains fragmented across single-domain reviews, leaving practitioners without a consolidated basis for selecting and deploying these tools across the training process. This scoping review aimed to identify and characterize research on mHealth apps in sport training, focusing on their performance testing, training load and recovery monitoring, technical and skill development, injury screening and prevention, and athlete self-management. It also synthesized evidence regarding their applications, intended purposes, technical characteristics, and the evidence supporting their effectiveness. Five databases (PubMed, Scopus, Web of Science, SPORTDiscus, Embase) were searched from inception to July 2026 for journal articles reporting original empirical data on app research on the sport training process in athletes. Studies involving only the promotion of physical activity or lacking human-subject testing, including commercially available apps without supporting research on their effectiveness, were excluded. Findings were synthesized narratively, and methodological quality was appraised with the Mixed Methods Appraisal Tool. Of 9476 records identified, 111 studies met the inclusion criteria and were inductively classified into ten application categories: sport skill training (n = 26), performance measurement (n = 20), vertical jump measurement (n = 18), self-reported monitoring (n = 12), physiological measurement (n = 12), musculoskeletal screening (n = 10), psychological intervention (n = 5), nutrition (n = 3), anthropometric and maturation screening (n = 3), and tactical and match analysis (n = 2). Most apps relied on built-in smartphone sensors or no sensing at all and used manual or deterministic computation; processing location went unreported in 74.8% of studies, which reflects a reporting gap rather than an architectural profile of the field, and reported that AI or machine learning labels did not track with actual method disclosure. Validation and reliability designs dominated the evidence base (52%), while randomized or controlled effectiveness trials were rare (10%). Apps generally showed good relative validity but limited absolute accuracy against criterion instruments, and wherever apps were deployed longitudinally, adherence rather than accuracy determined their real-world value. mHealth apps now support nearly every stage of sport training and can substitute for laboratory instruments in select, validated use cases, including video-based sprint and jump timing and chest-strap-paired heart-rate variability monitoring. However, the field remains organized around demonstrating measurement accuracy rather than showing that app-guided decisions improve athlete outcomes. A successful pathway for mHealth app development should progress from technical validity, through measurement reliability and responsiveness, to decision rules, and then to practitioner adoption by coaches and athletes, ultimately yielding better athlete outcomes. Full article

Metal oxide thin-film transistors (MO TFTs) have drawn wide interest in photodetectors and optoelectronic synaptic devices owing to their wide bandgap, low off-state current, high optical transparency, low-temperature processing, and large-area uniformity. Gate modulation in the TFT structure can tune the channel’s initial state and interfacial electric field, enhancing the tunability of photogenerated carrier transport, defect trapping/release, and interfacial charge regulation. This article reviews the progress of MO TFT photodetectors and optoelectronic synaptic devices, and examines the roles of light absorption, carrier transport, defect-related carrier dynamics, interfacial charge control, and persistent photoconductivity in different device functions. For photodetectors, key goals include broadening the response spectrum, reducing dark current, improving spectral selectivity, and enhancing response stability. For optoelectronic synaptic devices, post-illumination conductance retention and slow relaxation enable memory retention and synaptic weight modulation. Thus, rather than being separate, photodetection and optoelectronic synapses are functional extensions of the MO TFT optoelectronic response under different application targets. This article further discusses the synergy between these two functions in array sensing, visual preprocessing, and intelligent vision systems. Future development requires advances in targeted defect engineering, interface and structure optimization, array uniformity, standardized evaluation, and device–circuit–algorithm co-design for low-power, integrable intelligent vision hardware. Full article

Precision livestock farming (PLF) integrates sensing technologies, data acquisition (DAQ) systems, and machine learning (ML) frameworks to continuously monitor individual animals and support welfare assessment through physiological and behavioral observations. Advances in infrared thermography, radar sensing, vision-based systems, acoustic monitoring, and wearable technologies have substantially expanded the ability to collect high-resolution data describing animal responses to internal and external stimuli. However, despite considerable technological progress, a persistent gap remains between sensing performance demonstrated under controlled experimental conditions and reliable deployment within commercial livestock environments. This gap is characterized by environmental variability, unrestricted animal movement, and operational constraints within commercial environments. Using a structured review methodology, this review examines sensing modalities, embedded DAQ architectures, communication strategies, ML methodologies, data privacy, farmer adoption, and an illustrative engineering workflow through the lens of welfare-relevant physiological characteristics. Emphasis placed on the distinction between direct sensor measurements and the biological processes they represent. Sensor outputs do not directly quantify welfare, stressors, or management outcomes; rather, they provide measurements of physiological and behavioral responses that require appropriate biological context for meaningful interpretation. As a result, welfare assessment does not depend solely on the ability to acquire data, but also on the ability to accurately relate those data to underlying physiological mechanisms. Within this framework, ML serves as a critical bridge between measurement and interpretation by enabling the analysis of complex, multimodal datasets. Future advancement of welfare-oriented PLF systems will require stronger alignment among sensing methodologies, physiological understanding, and practical deployment realities to generate meaningful, scalable, and biologically grounded welfare assessments. Full article

Integrating wearable motion sensing with neuromodulation may improve understanding of how alterations in neural excitability influence running performance and biomechanics. This study investigated whether intermittent theta burst stimulation (iTBS) applied to the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), or both regions influences running performance and sensor-derived spatiotemporal parameters during a 3000 m time-trial run. Ten endurance-trained runners (7 males) completed four stimulation conditions (M1, DLPFC, M1 + DLPFC, and sham) in a randomized, sham-controlled, repeated-measures crossover design. Running performance and spatiotemporal gait parameters were continuously monitored using wearable inertial measurement units (IMUs), with analyses conducted across the initial, steady-state, and final acceleration phases of the run. The M1 + DLPFC condition resulted in the fastest mean completion time, averaging approximately three seconds faster than sham. However, these differences were not statistically significant. Sensor-derived biomechanical measures revealed significantly higher running speeds and alterations in stride time and step frequency during the initial phase following combined stimulation compared with the other conditions. Ratings of perceived exertion and spatiotemporal variability did not differ between stimulation conditions. These findings demonstrate the utility of wearable IMUs for detecting subtle phase-specific changes in running biomechanics and suggest that combined stimulation of motor and cognitive control regions may influence early-stage running performance, warranting further investigation in larger cohorts. As the complete sample consisted of only ten runners, these findings are preliminary and require confirmation in a larger sample size. Full article

Robotic joint motor drive systems must combine torque density and dynamic response with low mechanical impedance, safe interaction, and thermal robustness. This review treats gear ratio as a system-level design coordinate realized jointly by the motor, transmission, thermal path, sensing, and control. It synthesizes how ratio selection changes torque–speed capability, reflected inertia, losses, thermal duty, reducer nonidealities, backdrivability, and control bandwidth. The proposed spectrum uses nominal ratio as its primary coordinate while treating reducer topology, application domain, integration level, and compliance as distinct, overlapping descriptors. Mechanism-level conclusions are based on peer-reviewed studies; manufacturer specifications, open-source structures, and model-based engineering examples are identified and interpreted within narrower evidence boundaries. Representative robotic-joint cases connect these mechanisms to application demands, and an iterative framework translates the synthesis into checks on the task envelope, motor–reducer matching, thermal feasibility, transmission nonlinearity, sensing, and control. Relative to gearbox-centered reviews and task-specific motor–transmission optimization studies, this review provides a cross-domain decision map rather than a product ranking or universal predictive model. Full article

The integration of artificial intelligence (AI), the Internet of Things (IoT), and blockchain may provide a viable approach to tackle persistent operational and informational challenges in cleaner production. This conceptual review synthesizes existing literature and presents an integrated AI-IoT-blockchain framework mapped across the four sequential stages of cleaner production: source reduction, process control, end-of-pipe treatment and recycling, and full-chain traceability. The literature indicates that IoT enables real-time sensing, AI drives predictive and prescriptive analytics, and blockchain ensures tamper-proof record-keeping and stakeholder trust. Together, these technologies may help address long-standing barriers including fragmented data, delayed responses, and a lack of verifiability. Despite challenges such as high costs, technical fragmentation, and organizational resistance, several emerging strategies have been proposed in the literature to address these challenges. These include modular deployment, federated learning, permissioned blockchains, and regulatory sandboxes. The framework’s underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development. Full article

In modern process industries, data-driven soft sensors have become indispensable for monitoring critical process variables that are inaccessible to direct measurement. Nevertheless, the accurate modeling of industrial processes remains challenging due to their intrinsic complexities, such as time-series behaviors, measurement outliers, redundant variables, and potential concept drift. Existing approaches can address subsets of these challenges but generally lack a unified mechanism that integrates robust offline modeling, variable-importance analysis, and efficient online adaptation. To address these issues, this study proposes an online-updating robust soft sensor framework based on bidirectional long short-term memory (BiLSTM) with integrated gradients (IG) and smoothed quantile loss (SQLoss). During offline modeling, a soft-sensing model is constructed using a BiLSTM, and the proposed SQLoss is introduced to reduce the influence of outliers; the IG method is then employed to evaluate the importance of input variables, enabling input variable selection. During online operation, model parameters associated with significant variables are selectively updated based on IG-derived variable importance, thereby addressing concept drift. Finally, experimental results on an industrial desulfurization process demonstrate that, compared with the best-performing competing basic learner, the proposed SQLoss-BiLSTM-IG reduces the average root mean squared error (RMSE) and mean absolute percentage error by 5.26% and 1.87%, respectively, while increasing the average correlation coefficient by 1.94%; in the online evaluation, the proposed updating strategy achieves a mean RMSE of 2.441, demonstrating its effectiveness in handling concept drift. Moreover, the analysis of key variable importance is consistent with field experience, offering valuable insights for optimizing the desulfurization control system. Full article

Magnetic molecular thin films provide a platform for nanoscale control of spin density, morphology and low-dimensional magnetism. We use co-deposition of closely isostructural iron phthalocyanine (FePc) and metal-free phthalocyanine (H₂Pc) onto heated substrates to prepare diluted thin films with systematically varied Fe spin densities. Structural and surface characterization shows that H₂Pc incorporation modifies film growth, producing a monotonic dependence of surface roughness on dilution and a grain size minimum for mixed FePc:H₂Pc films. Vibrating sample magnetometry reveals a nonlinear suppression of the magnetic response with increasing H₂Pc content, exceeding the reduction expected from FePc concentration alone. Below 5 K, the saturation magnetization is markedly reduced in diluted films compared with undiluted FePc, suggesting that molecular packing, Fe chain length and nanoscale morphology influence the magnetic coupling strength. These findings provide insight into FePc:H₂Pc co-deposition as a route to chemically tunable magnetic molecular nanomaterials and highlight the importance of structurally compatible molecular dilution for magnetic sensing applications. Full article

Soccer requires rapid tactical decision-making, perceptual-cognitive processing, and coordinated physical execution, making it a relevant application domain for virtual reality (VR). VR systems integrate head-mounted displays, inertial measurement units, eye-tracking sensors, and electroencephalographic interfaces to support training, assessment, rehabilitation, and engagement. However, evidence remains fragmented across populations, study designs, sensing configurations, and research purposes, limiting conclusions about effectiveness and real-world application. Following PRISMA 2020, this systematic review synthesized 20 empirical studies published between January 2020 and 16 August 2026 involving school, academy, collegiate, and selected indirect adult soccer populations. Studies were classified by sensor modality, tracking configuration, evidence purpose, and outcome domain. Controlled intervention studies reported possible short-term improvements in selected sensorimotor, tactical, perceptual-cognitive, technical, and motivational outcomes. In contrast, cross-sectional and profiling studies showed that some VR tasks distinguished players by expertise, age, or competitive level; these findings support assessment or discriminative validity but do not demonstrate that VR training improves performance. Rehabilitation and injury-related evidence was limited and partly derived from adult or clinical populations, whereas engagement studies suggested potential benefits for motivation, attention, and participation. Six-degrees-of-freedom HMDs, 360° projection systems, eye tracking, and EEG generated different types of evidence, but no study directly compared hardware configurations within the same sample. The main contribution of this review is an integrated evidence-purpose and sensor-based synthesis that distinguishes training effectiveness from assessment validity across student-soccer applications. Overall, evidence remains promising but preliminary because of small and mixed populations, methodological heterogeneity, incomplete technical reporting, possible publication and language bias, short follow-up, and limited transfer to full-match performance. Standardized sensor reporting, controlled interventions, and longitudinal transfer assessments are required. Protocol registration: OSF. Full article

Tunable conductivity, heteroatom-rich composition, controllable morphology, and strong interfacial activity have resulted in carbon nanomaterials emerging as promising electrode modifiers for electrochemical sensors. As sources of sp2-rich carbon nanomaterials, conjugated polymers can be transformed through simple carbonization into carbon frameworks. Among them, polypyrrole- and polyaniline-derived carbon nanomaterials have been widely investigated for electrochemical sensing applications. In contrast, carbon nanomaterials derived from poly (3,4–ethylenedioxythiophene) have received comparatively limited attention, possibly due to the relatively high cost and established electrical conductivity of the polymer chain itself. The heteroatoms originally present in these polymers can be retained or transformed into active sites to promote electron transfer, analyte adsorption, and catalytic signal generation. This review summarizes recent progress in conjugated-polymer-derived carbon nanomaterials for electrochemical sensor applications, emphasizing precursor chemistry, morphology control, surface chemical regulation, metal and inorganic decoration, and sensing mechanisms. By clarifying the relationships among precursor structure, carbon framework, surface functionality, and electrochemical performance, this review offers guidance for designing sensitive and stable carbon-based sensing platforms. Full article

This review systematically synthesizes recent advances in road snow and ice removal equipment, with emphasis on operating mechanisms, applicable conditions, key parameters, and development trends. Mechanical ice-breaking and snow removal equipment—including rolling, rotary-cutting, impact, snowplow, and rotary brush systems—is evaluated in terms of operating principles and engineering applicability. The energy input modes and dominant performance factors of hot-air, steam, microwave, and laser deicing technologies are then summarized. In addition, the structural configurations and functional coordination of mechanically integrated and thermal–mechanical snow removal vehicles are examined. These technologies differ substantially in ice-breaking capability, operating speed, energy consumption, and risk of pavement damage. Current research is further limited by inconsistent performance metrics, insufficient validation under field operating conditions, and inadequate parameter matching among functional units. Future studies should therefore emphasize snow and ice condition sensing, adaptive regulation of operating parameters, and coordinated control of multiple functional units. This review provides a systematic basis for equipment selection, performance evaluation, and the design of integrated road snow and ice removal systems. Full article

Urban Internet-of-Things (IoT) infrastructures increasingly host distributed artificial intelligence workloads across heterogeneous edge environments, supporting applications such as urban monitoring, resource optimization, and large-scale sensing analytics. Despite the rapid adoption of edge AI, current systems lack a unified architectural layer responsible for orchestrating distributed intelligence across dynamic urban infrastructures. Existing orchestration mechanisms are typically rule-based and rely on low-level telemetry signals such as latency, node availability, or network conditions, limiting their ability to interpret the contextual meaning of system behavior and environmental events. This paper introduces Urban AI OS, a trustworthy LLM-based semantic control layer for distributed urban edge intelligence. The proposed architecture transforms heterogeneous telemetry and environmental signals into high-level semantic events through large language model reasoning, enabling context-aware orchestration decisions including adaptive topology management, workload coordination, and node role assignment. To address the reliability risks associated with LLM-assisted control, Urban AI OS employs confidence-gated execution with deterministic fallback policies, post-action monitoring, rollback, and auditable decision logging to constrain the operational impact of uncertain or erroneous LLM recommendations. By decoupling semantic reasoning from the data plane execution of AI workloads, Urban AI OS provides a model-agnostic framework for managing large-scale urban edge intelligence infrastructures. Experimental evaluation on a real-world urban IoT deployment with 50+ edge devices demonstrates that the proposed semantic operating layer improves system responsiveness by 35%, reduces unnecessary topology changes by 42%, and improves communication efficiency compared to conventional rule-based and adaptive threshold baselines, while maintaining confidence calibration through formal fallback mechanisms. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article

Biomass-derived nanoengineered carbon materials have emerged as key platforms in environmental technologies due to their high surface area, electrical conductivity, chemical stability, and sustainable synthetic route starting from renewable feedstock. This broad family comprises dimensionally nanoscale materials, such as carbon dots, carbon nanofibers, and graphene-based structures, as well as biochars, hydrochars, activated carbons, and related porous carbonaceous materials whose pore architecture, surface chemistry, or defects are deliberately engineered at the nanometer scale. Beyond their traditional role as passive supports, these materials can actively regulate adsorption phenomena, charge transport, and catalytic microenvironments through precise control of heteroatom doping, graphitic domains, and hierarchical porosity. Among current environmental priorities, carbon dioxide (CO₂) management represents one of the most pressing challenges. Biomass-derived nanocarbons offer tunable adsorption sites for selective CO₂ capture while simultaneously serving as active matrices for catalytic conversion. Tailored doped-carbon frameworks can stabilize key reaction intermediates, suppress competing pathways such as hydrogen evolution, and promote selective transformation into fuels and high-value chemicals. In addition, these materials are excellent hosts for atomically dispersed metals, dual-site catalysts, and semiconductor hybrids used in electrochemical and photocatalytic CO₂ reduction. By combining renewable sourcing with nanoscale control of reactivity, carbon materials create a bridge between environmental remediation and carbon valorization. This review critically examines recent progress in biomass-derived nanoengineered carbon materials for integrated CO₂ capture and conversion, with emphasis on structure-property-performance relationships, mechanistic roles, scalability, and sustainability. Particular attention is also devoted to catalytic conversion and electrochemical CO₂ sensing, where carbon-based and hybrid interfaces enable the transduction of CO₂ recognition into measurable electrical responses. These materials represent a promising yet underexplored pathway toward circular carbon management and the development of next-generation low-carbon chemical technologies. Full article