Search Results (14,938)

The rapid detection of organophosphorus (OP) compounds is crucial for safeguarding human health and ensuring food safety. This study presents a novel wearable electrochemical biosensor that integrates miniaturized screen-printed electrodes with wearable devices to achieve real-time, on-site OP detection. The biosensor was fabricated by constructing a screen-printed carbon electrode (SPCE) on a thermoplastic polyurethane (TPU) substrate, sequentially modified with graphene (GR), gold nanoparticles (AuNPs), and organophosphorus hydrolase (OPH), and finally encapsulated with Nafion. This SPCE/GR/AuNPs/OPH/Nafion configuration yields a highly flexible and portable device. The detection principle relies on the enzymatic hydrolysis of methyl paraoxon (MPOX) by OPH, generating p-nitrophenol (PNP), which is quantitatively measured via square wave voltammetry (SWV). The sensor exhibits a broad linear detection range (30–400 μM) with a strong linear correlation (R² = 0.995) and a low detection limit (0.321 μM). It demonstrates excellent selectivity against common interfering substances, including urea, sucrose, and various metal ions. Application to real-world samples such as cabbage and tap water yielded high recoveries (107.2% for cabbage and 101.2% for tap water), with relative standard deviations (RSDs) below 8%. Furthermore, the biosensor maintains robust flexibility and mechanical resilience, with less than 5% signal loss after 100 bending cycles, confirming its suitability for wearable applications and reliable operation under mechanical stress. This innovative, flexible electrochemical biosensor provides a powerful and reliable platform for rapid OP detection, particularly in complex testing environments. Full article

Skeleton-based action recognition is a critical technology for intelligent sports analysis. Although the human skeletal structure exhibits inherent bilateral symmetry, sensor noise on resource-constrained edge devices frequently induces geometric distortion and topological asymmetry. Consequently, achieving a balance between high accuracy and real-time performance remains a significant challenge. To this end, we propose EMS-GCN, an Efficient Multi-scale Shift Graph Convolutional Network that integrates geometric priors. Specifically, we design a Gaussian kernel-driven topology refinement module to mitigate structural noise inherent in sensor data. By leveraging geometric symmetry and Gaussian distances among nodes, this module dynamically constrains graph topology learning, thereby effectively rectifying the structural asymmetry and ambiguity induced by noise. Furthermore, we construct a Multi-scale Shift Linear Attention (MSLA) module to replace computationally intensive temporal convolutions. Leveraging temporal shift invariance, this module captures multi-scale contexts via parameter-free shift operations. Furthermore, we introduce a linear temporal attention mechanism to model global temporal dependencies with linear complexity, effectively resolving the information asymmetry inherent in long-range interactions. Finally, EMS-GCN incorporates a dual-branch attention structure to adaptively calibrate feature responses. Extensive experiments demonstrate that our model maintains high recognition accuracy with only 0.56M parameters, representing a reduction of over 60% compared to mainstream baselines. These results validate the efficacy of leveraging geometric and temporal symmetries to enhance real-time sports analysis. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems. Full article

Urban water distribution systems (WDSs) face increasing threats from accidental or intentional contaminant intrusion events. While contamination warning systems using water quality sensors enable early detection and rapid response to contamination events, traditional sensor placement approaches often rely on a single or limited performance metric, overlooking the multidimensional nature of system resilience. This study presents a multidimensional resilience-based framework for the optimal placement of water quality sensors in urban WDSs, integrating hydraulic and water quality simulations using the EPANET-MATLAB toolkit with a genetic algorithm (GA) optimization process. For Anytown Water Distribution Network, four distinct functionalities were formulated to capture different aspects of system performance during contamination events, and an integrated-multidimensional resilience metric was proposed as a collective measure. Results demonstrated that the optimal sensor configurations varied significantly depending on the selected functionality. However, the integrated multidimensional resilience-based approach yielded more balanced and effective sensor placements, simultaneously enhancing resilience levels for all individual functionalities. Furthermore, the findings indicated that adding more sensors beyond a certain number offers marginal improvements in system resilience, suggesting that sensor deployment should be guided by monitoring objectives (e.g., resilience) rather than simply increasing sensor numbers. The findings and discussion suggest practical insights for utilities to enhance water supply services with safe quality and system security against contamination threats in urban WDSs. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article

In this article, the motion control of ferromagnetic particles through varying a non-invasive magnetic field is addressed. Within an experimental test bench, three experiments are proposed to verify motion control, which consist of control of the distance between electromagnets, retention of particles over the flow, and manipulation of the direction of particle flow at a “Y”-type bifurcation emulating an “OR” gate. At each experimental stage, instrumented test benches were integrated with current, distance, and flow sensors, enabling measurement and feedback of the system’s physical variables. These benches were configured using pulse-width-modulation (PWM) and Proportional–Integral–Derivative (PID) controllers to regulate the current supplied to the electromagnets and, thereby, control the intensity of the induced electromagnetic field according to the requirements of each experiment. Different study cases were defined to analyze the operational limits of the system by varying the current influencing the electromagnetic field and the configuration of the electromagnets. The results describe the response of the magnetic field, the induced force, and the behavior of the suspended particles under each condition, providing elements to characterize the performance of the electromagnetic system in operational scenarios and contributing to the understanding of the phenomena associated with the non-invasive manipulation of ferromagnetic particles by means of controlled magnetic fields. Full article

Noise disturbances can cause conflicts in several areas, such as residences, civil constructions, highways, subways, and airports, measured by different scales of acoustic comfort for community well-being evaluation. These disturbances also have signatures such as frequency, amplitude, and temporal patterns to compare acoustic comfort with real-time parameters. In addition, acoustic sensors should be chosen based on accuracy, price, and calibration method, and acoustic insulation should be applied with the aim of achieving reliable measurements in indoor and outdoor environments for sustainable urban living. In some situations, the lack of noise control can lead to several human disorders, from hearing loss to cardiovascular complications. Therefore, legislation and regulation should be carefully studied and applied to achieve an equilibrium between energy-efficient and healthy building designs in entertainment, work, and rest activities with measured parameters visualized through the design of interface tools that should enable the collection and organization of sound data, with proper presentation for the final user. Finally, intellectual property registrations bring recent industrial applications with aspects of noise mitigation. All these features constitute noise disturbance mitigation in a multi-dimensional integration framework of technology, health, and law to improve the quality of life in human-centric environments. Full article

Against the background of highly interconnected global capital markets and rapidly propagating cross-lingual information streams, traditional anomaly detection paradigms based solely on single-modality numerical time-series sensors are insufficient for forward-looking risk sensing. From the perspective of artificial intelligence-driven sensing, this study proposes a multilingual semantic–numerical collaborative Transformer framework to construct a unified multimodal financial sensing architecture for intelligent anomaly sensing and risk perception. Within the proposed sensing paradigm, multilingual texts are conceptualized as semantic sensors that continuously emit event-driven sensing signals, while market prices, trading volumes, and order book dynamics are modeled as heterogeneous numerical sensor streams reflecting behavioral market sensing responses. These heterogeneous sensors are jointly integrated through a cross-modal sensor fusion architecture. A cross-modal temporal alignment attention mechanism is designed to explicitly model dynamic lag structures between semantic sensing signals and numerical sensor responses, enabling temporally adaptive sensor-level alignment and fusion. To enhance sensing robustness, a multilingual semantic noise-robust encoding module is introduced to suppress unreliable textual sensor noise and stabilize cross-lingual semantic sensing representations. Furthermore, a semantic–numerical collaborative risk fusion module is constructed within a shared latent sensing space to achieve adaptive sensor contribution weighting and cross-sensor feature coupling, thereby improving anomaly sensing accuracy and robustness under complex multimodal sensing environments. Extensive experiments conducted on real-world multi-market financial sensing datasets demonstrate that the proposed artificial intelligence-driven sensing framework significantly outperforms representative statistical and deep learning baselines. The framework achieves a Precision of $0.852$, Recall of $0.781$, F1-score of $0.815$, and an AUC of $0.892$, while substantially improving early warning time in practical risk sensing scenarios. In cross-market transfer settings, the proposed sensing architecture maintains stable anomaly sensing performance under bidirectional domain shifts, with AUC consistently exceeding $0.86$, indicating strong structural generalization across heterogeneous sensing environments. Ablation analysis further verifies that temporal sensor alignment, semantic sensor denoising, and collaborative cross-sensor risk coupling contribute independently and synergistically to the overall sensing performance. Overall, this study establishes a scalable multimodal intelligent sensing framework for dynamic financial anomaly sensing, providing an effective artificial intelligence-driven sensing solution for cross-market risk surveillance and adaptive financial signal sensing. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

Prolonged sitting with poor posture is associated with musculoskeletal disorders, reduced productivity, and long-term health risks. Many existing posture monitoring systems predominantly rely on single-modality sensing, such as pressure or vision-based approaches, limiting their ability to capture both static alignment and dynamic micro-movements. This study proposes a multimodal smart-skin system integrating pressure, temperature, and vibration sensors for sitting posture recognition. A total of 42 sensors distributed across 14 anatomical locations were deployed, generating 15,037 samples collected over three independent sessions to evaluate cross-session temporal generalization across nine posture classes under controlled experimental conditions. Two deep learning architectures—Temporal Convolutional Networks with Attention (TCN + Attn) and Convolutional Neural Network–Long Short-Term Memory (CNN − LSTM)—were compared under Leave-One-Session-Out (LOSO) cross-validation. TCN + Attn achieved 85.23% LOSO accuracy, outperforming CNN − LSTM by 2.56 percentage points while reducing training time by 36.7% and inference latency by 33.9%. Ablation analysis revealed that temperature sensing was the most discriminative unimodal modality (71.5% accuracy), and full multimodal fusion improved LOSO accuracy by 22.93% compared to pressure-only configurations. These results demonstrate the feasibility of multimodal smart-skin sensing combined with temporal convolutional modeling for cross-session posture recognition and indicate potential for efficient real-time, privacy-preserving ergonomic monitoring. This study should be interpreted as a controlled, single-subject proof-of-concept, and further validation in multi-subject and real-world environments is required to establish broader generalizability. Full article

The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through embedding-based semantic analysis. Using abstracts from 86,674 mobile industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise in policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based changepoint detection with topic lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design. Full article

Beekeeping is a profitable and mind-relaxing practice; however, monitoring beehives poses significant challenges, such as consuming time and potentially disturbing hive equilibrium, which may lead to colony collapse. Developing precision beekeeping (PB) systems is crucial to assist beekeepers in decision-making, automate redundant hive maintenance, and enhance the security and comfort of bee life. This review systematically explores research on PB systems, based on a keyword-driven search of Scopus and Web of Science databases, yielding 46 relevant publications. The analysis highlights a notable increase in research activity in the field since 2016. The integration of advanced technologies, including machine learning, cloud computing, IoT, and scenario-based communication methods, has proven instrumental in predicting hive states such as queen status, enemy attacks, readiness for harvest, swarming events, and population decline. Commonly measured parameters include hive weight, temperature, and relative humidity, with various sensors employed to ensure precision while minimizing bee disturbance. Additionally, bee traffic monitoring has emerged as a critical approach to assessing hive health. Most studies focus on honeybees rather than stingless bees and, in the context of enemy identification, Varroa destructor is the primary target. This review underscores the potential of novel technologies to revolutionize apiculture and enhance hive management practices. Full article

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems. Full article

Background: Stroke-related impairments in balance and gait are among the most common and disabling sequelae, significantly limiting functional independence and increasing fall risk. This study investigated the effects of short-term dynamic balance training on balance and gait in post-stroke hemiplegic patients. Methods: In this randomized controlled pilot trial, 16 post-stroke hemiplegic patients (intervention group, n = 8; control group, n = 8; mean age ≈ 58 years; predominantly male) were assigned to either a control group receiving conventional rehabilitation or an intervention group receiving additional daily dynamic balance training using the Prokin-252 system (30 min/day, 5 days/week, 3 weeks). Primary outcome measures included balance performance (Berg Balance Scale, mini-BESTest, single-leg stance), center-of-pressure (COP) parameters, gait performance (Timed Up and Go Test), and surface electromyography (sEMG) activity. Results: Following the intervention, both groups demonstrated improvements; however, the intervention group showed significantly greater gains in balance and gait outcomes. Specifically, Berg Balance Scale scores improved significantly (p = 0.012), as did mini-BESTest scores (p = 0.004). Eyes-closed single-leg stance time increased significantly on both sides (p < 0.05). COP analysis revealed reductions in sway area and trajectory length under challenging conditions. sEMG analysis indicated increased activation of the affected-side gluteus medius. In terms of gait performance, the intervention group demonstrated greater improvements in Timed Up and Go Test performance (p = 0.002), dual-task walking, and gait phase symmetry. Conclusions: Supplementing conventional rehabilitation with dynamic balance training effectively enhances balance and gait function in post-stroke patients, potentially through improved neuromuscular control. The integration of sensor-based COP analysis and sEMG provides additional mechanistic insight into rehabilitation outcomes. Full article