Search Results (4,467)

The artificial lateral line sensing principle provides a promising approach for underwater target perception and the navigation of underwater vehicles in complex flow environments. However, the highly nonlinear hydrodynamic mechanisms in complex flow fields make it difficult to establish accurate analytical models, which limits the development of high-precision perception and localization methods for underwater moving targets. In this study, a high-fidelity simulation model is established to characterize the pressure field variations induced by a moving source on an artificial lateral line pressure array. The influences of source velocity and sensing distance on the sensitivity and discretization characteristics of the pressure array are systematically investigated. Simulation results indicate that the sensor density of the pressure array is strongly correlated with the spatial resolution of the acquired pressure data, and a resolution of 50 sensors per meter is selected as the best-performing configuration by balancing sensing accuracy and sensor quantity. Under this configuration, the pressure distribution induced by the moving source exhibits clear and distinguishable spatiotemporal features, making it suitable for deep learning-based modeling. Furthermore, a large-scale temporal pressure dataset is constructed based on high-fidelity simulations under multiple motion directions and velocity conditions, and a spatiotemporal neural network is employed to predict the position of the underwater moving source. Experimental results demonstrate that, for straight-line underwater motion scenarios, the average localization error is within 7 cm, and a classification accuracy of 71% is achieved in practical engineering experiments. These results indicate that the proposed artificial lateral line pressure array design and deep learning-based prediction framework provide a feasible and effective solution for underwater target perception and localization in complex flow environments. Full article

Ventilation systems in agricultural settings are designed to deliver specific air exchange rates, which are often not achievable using natural ventilation. In this study, we analyzed 105 agricultural ventilation fans tested between 2015 and 2025 at the Bioenvironmental and Structural Systems (BESS) Laboratory, including 0.6, 0.9, 1.2, and 1.5 m diameter fans operating at static pressures ranging from 0 to 75 Pa. The main objective of the study is to develop and introduce guidelines to help select the most suitable ventilation fans to improve the sustainability of agricultural operations. Two web-based interactive calculators were developed to visualize fan performance relative to low- and high-performing fans of the same diameter. Our findings indicated that the ventilation efficiency ratio (VER) of the fans ranged from 2 to 50 m³ h⁻¹ W⁻¹, and larger fans consistently showed higher efficiency at typical operating pressures of 12.5 to 37.5 Pa. In general, variable-speed fans operated at 85%, rather than full capacity, achieved higher efficiency. Two cost comparison scenarios were examined. In the first scenario, the fan with a higher purchasing cost but also 35% higher efficiency resulted in a payback period of 4.1 years. In the second scenario, the difference in fan efficiencies was less than 3.5%, which did not help with recovering higher purchase costs during the 10-year analysis period. It was concluded that selecting fans solely based on purchase price can lead to higher long-term costs. To improve the sustainability of agricultural fans, VER and operating conditions need to be evaluated together and integrated into automated control strategies. Future studies can focus on integrating fans with high efficiencies into sensor-based automated ventilation control systems to quantify long-term energy savings in livestock buildings and other agricultural operations. Full article

Multi-grooved water-lubricated bearings (MGWLBs) are widely used in marine stern tube applications, where hydrodynamic performance is strongly influenced by groove geometry and operating conditions. This study presents a combined experimental and computational investigation of water film lubrication characteristics in MGWLBs with different groove geometries. An experimental test setup redesigned to replicate the operational behavior of MGWLBs was employed to record the circumferential film pressure variations under varying rotational speeds and applied loads. Detailed experimental tests were performed on a MGWLBs with filleted V-shaped grooves, where the film pressures at the bearing midplane were measured using a flush-mounted diaphragm pressure sensor mounted on a hollow shaft. The experimental results revealed a transition from localized, non-uniform pressure generation at low speeds to stable and circumferentially continuous hydrodynamic pressure fields at higher speeds and loads. CFD simulations were also conducted to analyze the influence of groove geometry on pressure distribution and flow behavior. An increase in rotational speed was shown to significantly enhance pressure magnitude, circumferential continuity, and film stability under moderate to high loading conditions. Filleted V-shaped, semicircular, and short V-shaped groove models were analyzed for a speed range of 400 to 6000 RPM. Filleted V-shaped grooves produced smooth pressure development with moderate gradients, while semicircular grooves improved pressure and velocity uniformity by limiting localized intensification. In contrast, short V-shaped grooves generated higher peak pressures due to enhanced flow acceleration at groove–land interfaces. The findings provide design guidance for selecting groove geometry and operating conditions to enhance the hydrodynamic performance of marine water-lubricated bearings. Full article

This paper presents a data-driven modeling and sensorless angle–torque prediction method for a pneumatic soft bending actuator. The actuator contains no embedded angle or torque sensors; instead, only airflow and pressure sensors located in the external control box (standard components in pneumatic systems) are used during operation. The proposed method, and therefore eliminates the need for onboard sensing and detailed valve hysteresis modeling. Based on the ideal gas law, four continuous, monotonic, and single-valued pneumatic state equations were derived and experimentally validated. As a case study, a pneumatic soft actuator was designed to generate high torque for assisting knee and ankle extension. An experimental setup with multiple sensors collected key data on air mass, internal pressure, actuator torque, and bending angle. These additional sensors were used only during dataset generation. A data-driven modeling approach was developed with training neural networks to generate four fitting functions to predict actuator behavior, including equations for angle and torque prediction. An angle-sensorless closed-loop control simulation study, incorporating a PID controller, a proportional valve delay block, and torque prediction, demonstrated the controllability and computational feasibility of the proposed model as well as the actuator’s effectiveness in supporting additional weight during squat-to-stand motion. Full article

Limitations of the cone penetration test, especially to accurately determine undrained shear strength (S_u) in soft soil deposits with high in situ stresses, have motivated the development of alternative devices, such as the T-bar and ball penetration tests, commonly referred to as flow penetrometers. These devices can estimate, in a single test, both the undrained shear strength (S_u) and the remolded strength (S_ur). When equipped with pore pressure sensors, they also provide valuable information on soil stratigraphy and consolidation parameters, making them versatile tools for characterizing soft soils. This study presents the development of two flow penetrometers, piezoball and piezo-T, highlighting relevant aspects of their design and calibration, followed by experimental campaigns conducted in two Brazilian clay deposits (Tubarão/SC and Sarapuí/RJ). Field tests enabled a direct comparison between the flow penetrometers and conventional methods, both in terms of S_u and S_ur. The investigation also examined the coefficient of consolidation of the soft soils. The results demonstrate good repeatability and consistent values for the bearing capacity factors (N_b and N_t) and remolded behavior (N_b-rem and N_t-rem). Regarding the performance of the pore pressure transducers, the piezoball test demonstrated good performance in pore pressure measurements and derived coefficients of consolidation. In contrast, despite the proposed design modifications, the piezo-T exhibited instability in the readings. Although the findings are derived from specific sites, the discussion is framed in light of the ranges reported internationally, highlighting potential local implications and reinforcing the need to expand robust geotechnical databases to support future applications. Full article

Non-invasive monitoring technologies are increasingly being explored to support cancer care, yet most existing approaches focus on isolated parameters and fail to provide a comprehensive view of patients’ health. This study presents a feasibility evaluation of an IoT-based system designed to detect treatment-related problems in oncology patients through the integration of wearable sensors, physiological measurements, and patient-reported outcomes. A monitoring kit, including a smartwatch, tensiometer, weighing scale, and mobile device, was deployed in a cohort of 26 patients undergoing oncological treatment. Data acquisition followed a structured schedule: continuous physiological recording via the smartwatch, daily blood pressure measurements, weekly weight monitoring, and structured surveys capturing treatment-related side effects. These heterogeneous data were transformed into binary clinical metrics using rule-based feature extraction algorithms, covering conditions such as insomnia, nausea, diarrhea, abdominal pain, headache, weight loss, hypertension, and fever. Clinical specialists labeled the dataset to ensure medical validity. Machine Learning models were then trained to analyze the features and generate alerts for potential treatment complications. The results demonstrate the feasibility of integrating IoT and Artificial Intelligence techniques for continuous, patient-centered monitoring in oncology, paving the way for intelligent decision-support systems that enhance early detection and clinical management. Full article

This systematic review presents a critical analysis of multifunctional nanowire sensors, with explicit selection criteria for included studies: we focus on peer-reviewed research, prioritizing studies on semiconductor (ZnO, TiO₂, Si), metal (Ag, Au), and carbon-based (CNT) nanowires that report structural innovations, performance breakthroughs, or industrial scalability. We systematically analyze their structural characteristics, advanced fabrication techniques (hydrothermal synthesis, magnetron sputtering, PECVD), and application performance across biosensing, pressure sensing, and gas monitoring. Unlike existing reviews limited to single material classes or application scenarios, this work advances the field by integrating three novel perspectives: it delivers a cross-material comparison of nanowire structure–performance relationships, incorporates an analysis of fabrication strategy scalability for industrial translation, and synthesizes unresolved challenges and future directions. Nanowire sensors exhibit superior sensitivity, rapid response, and broad detection ranges compared to conventional sensors, with significant potential to advance healthcare, environmental monitoring, and flexible electronics. Full article

Accurate engine fault classification with limited labeled data is critical for the safety and reliability of rotating machinery. This task is challenging because operating regimes are time-varying, and key variables must satisfy physical constraints, under which traditional feature classifier pipelines degrade and deep networks tend to overfit. We propose PhysGMM-MoE, a physics-aware Gaussian Mixture Model (GMM)-Mixture-of-Experts (MoE) framework for small-sample engine fault classification. At the data level, PhysGMM-MoE fits class-conditional, regime-aware GMMs and performs physically constrained, distance-based quality control to selectively augment minority classes while preserving engine operating semantics. At the model level, a heterogeneous pool of lightweight statistical experts and a lightweight Transformer-based deep expert (ECFT-Transformer) capture complementary neighborhood cues and high order multi-sensor correlations, and an L2-regularized logistic regression meta-learner fuses expert outputs via stacking. We evaluate fault classification on the 3500-DEFault diesel-engine dataset using the adopted eight-class cylinder-fault labeling (H, F1–F7) built from in-cylinder pressure statistics and torsional-vibration harmonics; although severity levels exist in the dataset, this study focuses on classification rather than severity estimation. With 40 training samples per class, PhysGMM-MoE achieves a mean accuracy of 0.9875, exceeding SMOTE+XGBoost by 0.0086, and attains the best macro precision/recall/F1 of 0.9878/0.9826/0.9889, demonstrating strong performance under the adopted small-sample setting. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems. Full article

In this study, an enzyme-free electrochemical sensor based on zinc oxide (ZnO) nanorods synthesized by the thermal decomposition of zinc acetate is presented. The suggested approach ensures simplicity, environmental friendliness, and scalability of the process without the use of an autoclave or high pressure. The morphology and structure of the samples are studied using SEM, TEM, XRD, Raman, FTIR, XPS, PL, and UV-Vis spectroscopy. It is found that heat treatment at 450 °C increases the degree of crystallinity, increases the size of crystallites, and reduces the concentration of surface defects, which leads to improved optical and electrochemical characteristics of the material. Beyond conventional sensitivity metrics, our study demonstrates that the selective detection of ascorbic acid (AA) and uric acid (UA) can be achieved by controlling the applied potential on a single ZnO electrode, an approach that leverages differences in redox energetics and surface interaction dynamics rather than complex surface functionalization. It is shown in this work that the synthesized ZnO samples subjected to heat treatment in air at 450 °C exhibit high sensitivity to ascorbic acid (9951.87 μA·mM⁻¹·cm⁻²; LoD = 1.11 μM) at a potential of 0.2 V and to uric acid (5762.48 μA·mM⁻¹·cm⁻²; LoD = 1.71 μM) in a phosphate buffer solution (pH 7) at a potential of 0.4 V with a linear range of 3 mM, offering a way to create simplified multicomponent electrochemical biosensors based on potential-controlled selectivity. Full article

Background/Objectives: Wearable technologies become increasingly important in surveillance of biometric parameters in pregnant women; however, early-stage usability data on wearable form factors specifically designed for pregnant women remain limited. This study evaluated the usability and acceptance of three non-functional wearable garment prototypes intended for future breathing exercise guidance and sleep-related applications. The prototypes incorporated sensor dummies that were technically capable of operation but intentionally deactivated for this usability pilot study. Methods: Eighteen pregnant women (second and third trimester) and twelve non-pregnant women tested three prototypes (Bra, Strap, Maternity Belt (hereafter Belt)) for 24 h. Usability was assessed using structured, participant-completed questionnaires addressing fit, material properties, comfort, and wear-related issues immediately after fitting (T0) and after 24 h of wear (T24). Analyses were descriptive and exploratory. Results: Among pregnant women, the Bra prototype showed consistently favorable usability ratings across multiple domains, particularly after extended wear, whereas the Belt demonstrated declining ratings related to fit and comfort over time. The Strap showed intermediate usability with specific strengths related to pressure and friction. In non-pregnant women, usability ratings were largely comparable between the Bra and Strap, with no clear preference pattern. No systematic differences were observed between pregnant and non-pregnant groups. Conclusions: This exploratory usability study suggests that garment form factor plays a critical role in acceptability during pregnancy. The Bra prototype demonstrated the most favorable usability profile among pregnant women, while the Belt revealed design limitations that warrant further modification. These findings provide formative guidance for the development of functional maternal wearables, with future studies integrating objective testing and validated measures to optimize performance and evaluate adherence in larger cohorts. Full article

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology. Full article

This paper presents a high-temperature silicon resonant pressure microsensor capable of stable operation up to 175 °C and 175 MPa, addressing the critical need for reliable pressure monitoring in deep well drilling and petroleum exploration. To overcome the inherent trade-off between pressure range and sensitivity in diaphragm-based sensors, the sensor incorporates V-shaped micro-beam supports that convert radial compressive stress into supplementary axial tensile stress on the resonant beams. This innovative force-transmission structure enhances both pressure resistance and positive stress sensitivity, enabling range extension while maintaining adequate sensitivity. A key feature of this work is the implementation of a frequency-ratio measurement scheme utilizing a dedicated pressure-insensitive reference resonator. This approach effectively eliminates the dependence on the stability of the external crystal oscillator frequency, a significant source of error in high-temperature environments where stable clock sources are costly or unavailable. Experimental results demonstrate that the fabricated sensor achieves a pressure sensitivity of 723.56 ppm/MPa for Resonator I and −436.60 ppm/MPa for Resonator II. The frequency-ratio output scheme maintains a measurement accuracy better than 0.02% FS (within the 0–36 MPa verification range) even when using a low-stability oscillator at 125 °C, significantly outperforming conventional direct-frequency measurement methods. The sensor’s combination of an extended pressure range, high-temperature capability, and robust frequency-ratio output offers a promising solution for high-precision pressure sensing in extreme downhole conditions. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article