Search Results (7,083)

Background/Objectives: Advanced technologies in type 1 diabetes mellitus (T1DM) management have reshaped the strategies used to achieve optimal glucose control. Continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID) systems are effective alternatives to multiple daily injections (MDI). This study aims to evaluate glycemic regulation in children and adolescents transitioning from MDI to insulin pumps and to raise awareness among patients and their families regarding the benefits of these systems. Methods: 50 pediatric patients with T1DM (24 males, 26 females; mean age 10.76 ± 3.2 years) were evaluated. Cycle 1 established MDI metrics 3 months pre-transition. In cycle 2, patients transitioned either to an AID system (Medtronic MiniMed 780G, (Northridge, CA, USA), 78%), or a non-automated system (Omnipod DASH, 22%). Data were assessed at 3 and 6 months post-initiation. Parameters assessed were glycosylated hemoglobin (HbA1c), time in range (TIR), time above range (TAR), time below range (TBR), glucose management indicator (GMI) and coefficient of variation (CV). Results: The cohort exhibited a statistically significant increase in TIR (p = 0.0038) with mean values of 70.9% at 3 months and 73.2% at 6 months. TAR significantly reduced (p = 0.033) to 26.5% and 24.3% at 3 and 6 months, respectively. Sub-analysis in the AID group revealed a marked increase in TIR (p = 0.0001) alongside significant reductions in TAR (p = 0.0009) and GMI (p = 0.03). Conclusions: Transitioning from MDI to insulin pump therapy, particularly AID systems, leads to modest but significant improvements in specific sensor metrics (TIR, TAR) in real-world clinical practice. The consistency of these results across age groups indicates that AID systems can successfully overcome pediatric and adolescent diabetes management challenges. Full article

Magnetostrictive sensors based on the inverse magnetostrictive effect offer the advantages of wireless passive operation and structural simplicity; however, achieving both high sensitivity and superior linearity remains a persistent challenge. This study presents a magnetostrictive pressure sensor incorporating a tunable Poisson’s ratio (TPR) chiral auxetic honeycomb substructure, designed to linearize the stress response of the sensing material. A theoretical model linking substructure design parameters to sensor output linearity was derived and validated through finite element simulations. The fabricated substructure exhibited a stable negative Poisson’s ratio (−1.278 to −1.213) within its elastic regime and a highly linear axial-to-transverse strain relationship (x = 1.214y + 0.113). The sensor achieved a calibration linearity of R² = 0.99745, a continuous linear force response up to 118.7 N while the corresponding voltage variation reached 10.75 mV, and a maximum hysteresis error of 5.495% over eight loading cycles. Bearing press-fit force monitoring experiments confirmed practical viability under industrial conditions, with R² exceeding at least 0.995 for dry assembly between multiple bearing types and maintaining R² > 0.994 under lubricated conditions. The proposed TPR substructure approach establishes a reference framework for linearity enhancement in inverse magnetostrictive force sensors. Full article

Multimodal image fusion is a promising technology designed to fuse information from different medical sensors, which offer structured insights for disease diagnosis and treatment. However, existing 2D-centric fusion methods fail to capture 3D spatial continuity, and conventional wavelet-based approaches lack adaptability to diverse lesion regions and suffer from background artifacts. To address this issue, we propose a 3D multiscale wavelet convolutional neural network for multimodal medical image fusion. Specifically, a 3D Discrete Wavelet Transformation (3D DWT) is introduced to decompose input volumes into multi-frequency bands, isolating anatomical structures and lesion details while reducing 3D spatial redundancy. We embed hierarchical multiple frequency band into a Global and Local Feature Calibration (GLFC) module to adaptively enhance single-modal features by fusing global contextual information and local details. Furthermore, a pyramid group-wise multiscale feature interaction is proposed for capturing complementary features across different spatial scales. Finally, a voxel-wise weighted averaging strategy reconstructs the fused image by adaptively assigning contributions to each modality at every spatial position, effectively eliminating artifacts and improving the visual fidelity of the result. Extensive experiments on the BraTS2020 and Hecktor datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) fusion methods in both subjective visual quality and objective metrics. Moreover, downstream segmentation validation confirms that fused images from our method significantly improve tumor segmentation accuracy. The source code and pre-trained models will be publicly available. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

The NMR resonator is the sensor responsible for transmitting RF pulses and receiving detection signals, and its tuning and matching are crucial to acquiring high-sensitivity NMR signals. Automated tuning and matching (ATM) is therefore essential for rapid, accurate, and continuously efficient testing. Existing NMR ATM methods still primarily rely on iterative search strategies, whose dominant cost arises from repeated hardware measurements and waiting periods, often requiring multiple measurement cycles before convergence. The emergence of in situ NMR detection of high-concentration ionic samples has further increased the demand for real-time, rapid ATM with a large dynamic range, posing a major challenge to conventional approaches. This paper proposes a physics-informed few-shot learning method for automatic tuning and matching over wideband and multi-resonance-frequency NMR scenarios. The tuning-and-matching problem is formulated as a structure and frequency-conditioned function regression task, and a conditional neural process (CNP) is introduced to learn cross-task priors and directly predict the states of tunable components from only a small number of real-machine context measurements. A physics regularizer based on the local sensitivity of the input impedance is further designed to impose stronger penalties on errors under high-Q narrowband operating conditions without relying on proprietary analytical circuit models. Simulation studies and real NMR experiments are conducted on multiple circuit topologies and multiple target frequencies using only a small number of NMR samples. The results demonstrate consistent improvements in key metrics, including accuracy of tuning and matching and the number of collected real-machine samples required per task. In particular, with only 100 sampled tuning/matching capacitor points and 20 on-hardware collected samples, the proposed method already delivers satisfactory tuning-and-matching performance. The method achieves an attractive accuracy–cost tradeoff across both cross-topology and cross-frequency scenarios, and shows strong potential for few-shot, rapid, real-time detection. Full article

The present study provides a multi-faceted validation and refinement of a distributed system architecture designed to improve emergency response in resource-constrained urban areas. The architecture integrates IoT sensors, edge computing, field-programmable gate arrays and distributed shortest-path algorithms to enhance resilience and operational efficiency. As a primary validation strategy, a survey of 78 Cameroonian experts in software engineering, distributed systems, urban planning and emergency technologies was conducted. The survey yielded quantitative and qualitative data across multiple analytical dimensions, including subgroup analysis and a transferability assessment covering Nigeria, Senegal, and Kenya. The statistical analysis confirmed that the architecture is technically feasible, adaptable to local constraints, and has the potential to reduce response times. As a secondary validation strategy, a simulation-based study was conducted using iFogSim on smart-city models ranging from 25 to 100 nodes, encompassing five experiments: result consistency, geographic sensitivity, concurrent incident management, path-caching efficiency, and scalability analysis. The simulation results quantitatively corroborate the expert assessments, demonstrating low end-to-end latency and sustained throughput with realistic urban load conditions. Key challenges identified include interoperability, urban data structuring, financial sustainability and inter-institutional coordination. Experts have proposed a hierarchical structure of priority actions and concrete recommendations for engineers, researchers and policymakers. The combined findings validate the architecture and establish a replicable expert-simulation evaluation framework applicable to analogous distributed emergency-response systems in comparable resource-constrained contexts. The empirical results further constitute a reference baseline for the design and implementation of similar architectures. Full article

Hydrogel-based sensors have emerged as transformative soft-sensing platforms, featuring tissue-matched compliance, high water content, stimuli responsiveness, and chemical tunability, properties which are unachievable with conventional rigid sensors. Despite substantial advances, the existing reviews focus on individual polymer categories, discrete transduction mechanisms, or targeted standalone applications, failing to establish an integrated pipeline from material design to final sensing performance. This review fills these crucial gaps by systematically correlating polymer chemistry, crosslinking tactics, and fabrication protocols with the selection of transduction mechanisms and resultant sensing performance across biomedical and environmental fields. We conduct a critical assessment of natural and synthetic polymers together with chemical, physical, and hybrid composite crosslinking methodologies. Multiple sensing modalities, including piezoresistive, capacitive, thermogalvanic, electrochemical, colorimetric, ratiometric fluorescence, and piezoionic sensing are elaborated alongside representative quantitative performance parameters. Emerging platforms, including self-powered thermogalvanic sensors, SERS-integrated biosensors, and MXene/MOF composites, are highlighted as underexplored frontiers. In addition, persistent bottlenecks including dehydration-derived signal drift, inferior long-term operational stability, unsatisfactory target selectivity, and obstacles toward large-scale manufacturability are rigorously analyzed. Ultimately, this review constructs a holistic unified framework bridging polymer molecular design, fabrication engineering, signal transduction, and practical end-use applications, laying a clear developmental roadmap for next-generation flexible and smart hydrogel-based sensing systems. Full article

Monitoring biodiversity in coastal and marine ecosystems is essential for supporting conservation strategies, sustaining ecosystem services, and meeting policy commitments at multiple scales, including the European Union’s Habitats Directive, Sustainable Development Goal 14 (SDG 14, Life Below Water), and the Kunming–Montreal Global Biodiversity Framework (GBF). However, many benthic habitats remain insufficiently mapped or monitored due to the spatial, temporal, and logistical limitations of traditional field-based approaches. Optical Remote Sensing (ORS), based on the use of optical sensors to retrieve spectral information from shallow-water environments, has emerged as a powerful tool for mapping and monitoring these ecosystems. This study presents a systematic review aimed at providing a comprehensive synthesis of above-water ORS applications for benthic biodiversity and habitat monitoring over the period 2014–2023. A total of 179 peer-reviewed studies were analyzed to identify temporal trends, geographic patterns, target ecosystems, and methodological workflows. The review considered observation platforms including satellite, airborne, unmanned aerial vehicles (UAVs), and field spectrometry systems, together with key preprocessing procedures required for reliable benthic detection, such as atmospheric correction, water column correction, and sunglint removal, alongside validation using independent measurements. The analysis reveals a rapid expansion of ORS applications, with a strong geographic concentration in tropical and subtropical regions. Studies focusing on specific benthic groups predominantly target coral reefs and seagrass ecosystems, although many adopt integrative benthic habitat classifications that incorporate multiple benthic components at the habitat level. However, significant limitations persist, including inconsistent preprocessing workflows, limited reporting transparency, and the underrepresentation of several ecologically important taxa (e.g., annelids, mollusks, echinoderms). Despite these challenges, ORS has become a cornerstone of large-scale and repeatable coastal monitoring. By analyzing methodological practices, ecological targets, and geographic biases, this review provides a critical foundation for improving the robustness, scalability, and global applicability of ORS in benthic habitat mapping, biodiversity monitoring, and ecosystem-based management. Full article

Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually—channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff’s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal–polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of $6.8\%$ and a tensile linearity $R^2>0.99$. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves $87.0\%\pm 8.4\%$ accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than $100\mathsf{\mu }\mathrm{s}$. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring. Full article

Physical activity is a key lifestyle factor for mental health prevention, yet the influence of accelerometer placement on mood prediction remains unclear. We merged high-resolution acceleration data and Ecological Momentary Assessment (EMA) mood reports from 259 healthy participants across three ambulatory studies (SedMood, 24 hrCog, HO). Additionally, 15 min pre-assessment movement windows consisting of raw triaxial acceleration (64 Hz) from hip, thigh, chest, and wrist sensors were paired with six-item mood EMA queries. Features (e.g., mean, entropy, spectral power) were extracted and fed into gradient-boosted decision tree models (XGBoost), trained separately for energetic arousal, valence, and calmness. Performance was measured using the metrics MAE, RMSE and R². Within individual studies, chest and hip sensors achieved the highest performance, followed by wrist and thigh. In the combined dataset, hip sensors again outperformed thigh (R² 0.38 vs. 0.20). Multi-sensor models rarely surpassed the best single-sensor configuration and sometimes reduced accuracy. These results suggest that sensor location modestly impacts mood-prediction performance, with hip and chest offering the most reliable signals, while adding sensors does not reliably enhance predictive power. Future work should explore larger, homogenous datasets and location-specific feature engineering to refine wearable-based mental health monitoring. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Accurate and non-destructive diagnosis of leaf nitrogen content (LNC) is critical for improving nitrogen use efficiency in greenhouse cucumber production. However, strong physiological variation across growth stages limits the reliability of single-sensor approaches. In this study, leaf spectral reflectance and soil plant analysis development (SPAD) measurements were collected under four nitrogen levels (0, 135, 270, and 540 kg·ha⁻¹) at early, mid-, and late fruiting stages. Multiple machine learning models were developed using raw spectral bands (SP), vegetation indices (VIs), and SPAD data, and evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). Results showed that SPAD showed a significant positive correlation with LNC across all stages, with the strongest relationship observed at the mid-fruiting stage (R = 0.7975). Model performance exhibited clear stage dependence. Using single features, the best R² reached 0.800 (SP, early stage) and 0.794 (VI, early stage), but declined substantially at later stages. In contrast, integrating SPAD with spectral features significantly improved prediction accuracy, particularly at mid- and late stages. For example, the RF model based on SP + SPAD achieved R² values of 0.917 and 0.901 at the mid- and late fruiting stages, respectively, with low RMSE and RE. Similarly, the VI + SPAD combination achieved R² up to 0.893 at the late stage. Moreover, optimal algorithms varied across growth stages: SVR performed best at the early stage (R² = 0.819), RF at the mid stage (R² = 0.889), and XGBoost at the late stage (R² = 0.842) under full feature fusion. These results demonstrate that model accuracy is jointly regulated by growth stage, feature composition, and algorithm selection. Overall, this study highlights that a growth-stage-specific data fusion strategy integrating SPAD and spectral features is essential for robust LNC estimation, providing a practical framework for precision nitrogen management in greenhouse cucumber production. Full article