Search Results (6,863)

Rapid hemostasis assessment is essential for managing acute coagulopathy, guiding anticoagulant therapy, and monitoring cardiac surgery patients. Although viscoelastic testing provides valuable guidance for early intervention, its widespread adoption is constrained by large blood usage (~2 mL), prolonged turnaround times (several hours), long assay durations (~25–40 min), and high costs. To overcome these limitations, we developed an innovative, all-in-one magnetoelastic (ME) sensing chip that enables systematic coagulation kinetics monitoring using only 46 μL of whole blood within 15 min. Featuring prepackaged lyophilized reagents and pumpless blood loading, this user-friendly chip is highly cost-effective for disposable use. The experimental results demonstrated good reproducibility for on-chip extrinsic coagulation activation, with clotting parameters maintaining coefficients of variation under 10%. Furthermore, clotting parameters derived from heparin monitoring results exhibited a strong linear correlation that compares favorably with the clinical standard (r = 0.986). Finally, sensitivity evaluation toward various blood components validated the sensor’s dual-mode characterization strategy for identifying coagulation factors and fibrin-related coagulopathies. The proposed ME sensing chip holds promise for bedside testing and flexible, on-demand coagulation monitoring across diverse clinical scenarios. Full article

Electrical bioimpedance (BioZ) has emerged as a promising technique for the non-invasive monitoring of physiological parameters, owing to its ability to map functional activity into electrical changes. Particularly, impedance plethysmography (IPG) is used to track blood volume changes associated with cardiac activity. However, developing flexible, low-cost devices with enough sensitivity to serve as high-precision for IPGs remains an open challenge. In this work, we introduce laser-induced graphene (LIG) electrodes as an attractive alternative for IPG measurements. The electrodes were fabricated by generating LIG on a polyimide substrate using a 405 nm laser diode and were subsequently characterized morphologically, structurally, and electrically to produce a wrist-worn cardiac impedance sensor (WCIS). The design of the WCIS is based on interdigitated electrodes to detect IPG variations at the radial artery, from which the heart rate is estimated. We show experimental results on IPG signal analysis and its validation against electrocardiogram (ECG) signals as the gold standard. As a result, a mean absolute error (MAE) of 1.7 bpm, a root mean square error (RMSE) of 2.1 bpm, and a limit of agreement of approximately $\pm 6$ bpm were obtained. These outcomes demonstrate the feasibility of the WCIS as a promising, low-cost alternative for continuous, non-invasive cardiovascular monitoring in portable devices, based on the IPG principle. Full article

In single DC-link current sensor-based phase current reconstruction for permanent magnet synchronous motor (PMSM) drives, the current reconstruction dead zone caused by insufficient active voltage vector duration restricted by driver dead time, switching settling, and Analog to Digital Converter (ADC) latency degrades current sensing accuracy, particularly in low modulation and sector boundary regions. Conventional phase shift methods, while extending the sampling window through Pulse Width Modulation (PWM) pattern modification, inevitably introduce asymmetric switching sequences that generate additional phase current harmonics and may reduce the linear modulation range. This article analytically characterizes the dead zone formation mechanism across the space vector plane and proposes a cross-cycle compensation strategy based on vector approximation. The method replaces the reference voltage vector with the nearest measurable vector in the present switching cycle and compensates for the resulting voltage error in the subsequent cycle, thereby extending the sampling window while preserving precise volt-second balance without extra hardware. Experimental results demonstrate that the proposed method eliminates the current reconstruction dead zone, achieves high-fidelity phase current reconstruction, and ensures robust dynamic performance under various load conditions and transients. The feasibility and effectiveness of the single current sensor drive are thoroughly validated. Full article

Glow-discharge plasma (GDP) poling is revisited as an electrode-free method for activating piezoelectricity in poly(vinylidene fluoride) (PVDF) films. Although this method was proposed several decades ago, its effect on the properties of PVDF films has remained poorly understood. In this work, we demonstrate that GDP enables efficient poling of oriented PVDF films without pre-deposited electrodes and investigate the relationship between plasma treatment time, structural evolution, and piezoelectric response. Commercially available 25 μm-thick oriented PVDF films (PolyK) were treated in a DC glow discharge for 15 s to 15 min and characterized using FTIR, DSC, piezoresponse force microscopy, UV–Vis–NIR spectrophotometry, quasi-static d₃₃ measurements and water contact-angle measurements. GDP poling produced a side-averaged piezoelectric coefficient d₃₃ of up to ~25 pC/N within 1–5 min, with local maxima at approximately 1, 2.5, and 5 min. This behavior was accompanied by pronounced changes in the domain structure, including an increase in the ferroelectric domain size from 86 to 552 nm, while the crystallinity and electroactive phase fraction changed only moderately. Plasma treatment also increased the wettability of the plasma-facing surface, reducing the water contact angle from about 85° to 42° within 3 min. At longer treatment times (>5 min), however, the piezoelectric response decreased and the optical transparency deteriorated because of increased haze and turbidity, most likely associated with plasma-induced chemical modification of the surface layers. These results indicate that GDP poling has an effective processing window of 1–5 min. The proposed approach provides a vacuum-compatible and electrode-free route for preparing PVDF films with increased surface wettability for flexible piezoelectric sensors, wearable electronics, and integrated polymer-based devices, because it is compatible with electrode deposition on an already activated polymer surface within a single vacuum cycle. Full article

Air-void distribution is an important internal characteristic of asphalt mixtures, as it affects compaction quality, moisture susceptibility, permeability, and long-term pavement durability. Conventional air-void testing methods generally provide only an average volumetric parameter and cannot effectively describe the spatial distribution of air voids within cylindrical specimens. To address this limitation, this study proposes a capacitance-based method for characterizing the vertical and radial air-void distribution of asphalt mixtures using a saturated reference field. An annular capacitive sensor was designed for cylindrical asphalt mixture specimens, and its structural dimensions were optimized using capacitance sensitivity and sensitivity-field distribution uniformity as evaluation indicators. Asphalt mixture specimens with different gradations and compaction conditions were prepared and tested under a saturated reference-field measurement scheme. Dielectric indicators derived from capacitance measurements were used to characterize the variation in air-void distribution along the specimen height and across radial regions. Layer-wise air-void measurements were further conducted to validate the vertical distribution results, while radial partition-based indicators were introduced to quantitatively describe the air-void distribution characteristics from the center to the edge of the specimen. In addition, rotation-angle and saturated-condition stability tests were performed to evaluate the robustness of the proposed method. The results indicate that the saturated reference-field capacitance method can effectively reflect the spatial variation in air voids in asphalt mixtures and provides a low-cost, rapid, and non-destructive approach for evaluating air-void distribution characteristics in laboratory-compacted specimens. Full article

Near-field multistatic radar imaging assumes that the transmitter, receiver, and target positions, as well as the receiver time references, are known exactly. In practice they are known only approximately: receiver positions and clocks carry small survey and synchronization errors, and target locations used to initialize or refine an image are themselves approximate. This paper develops a joint self-calibration framework that estimates small corrections to receiver positions, receiver clock biases, and target positions from the same bistatic echo delays used for imaging, and ties the correction directly to image sharpness rather than to parameter accuracy alone. We derive the linearized observation model relating delay residuals to these corrections, and give a regularized (maximum a posteriori) weighted least-squares estimator that explicitly separates measurement noise from prior parameter uncertainty. We characterize the identifiability of this estimator progressively, from a single anchor (the transmitter alone, which leaves an exact three-dimensional rotational null space) to two anchors (transmitter plus one additional point, which reduces the null space to a one-parameter rotation about a fixed axis) to three anchors (transmitter, one target, and one receiver, in general position, which removes the continuous ambiguity entirely). We additionally treat the dual problem of localizing an unknown transmitter from a small number of exactly known anchors—receivers, targets, or time samples of a single moving platform—and show that collinear or coplanar anchor geometries leave an exact, uncorrectable continuous or discrete ambiguity, respectively, regardless of how many such anchors are used, with the coplanar case notably invisible to a standard rank or conditioning check. We then reformulate the calibration objective directly in terms of coherent multistatic image sharpness, evaluated using the matched-filter score already used for image formation, and propose a two-stage algorithm: a coarse linear delay-residual solve followed by phase-coherent sharpness refinement. Numerical experiments verify the predicted identifiability transitions via the singular value spectrum of the linearized system, demonstrate quadratic convergence of the proposed estimator, verify the transmitter-localization ambiguity structure—including an exact mirror-twin solution for coplanar anchors, reproducing all range measurements to floating-point precision—and demonstrate the effect of self-calibration on a simulated multistatic image of an extended (eagle-shaped) target, including the incremental effect of bandwidth, aperture/frequency windowing, and CLEAN deconvolution on the recognizability of the resulting image, as well as on the resolvability of multiple simultaneous discrete targets (two instances of the same target). We relate this formulation to, and distinguish it from, the existing literature on time-of-arrival sensor network self-calibration and on joint target-localization/clock-bias estimation, which largely target single moving targets, anchor-free minimal-data solvability, or localization accuracy rather than multistatic image focus. Full article

Quarry waste piles are heterogeneous engineered embankments that are susceptible to slope instability, yet early detection of pre-failure surface changes remains challenging due to complex surface conditions and measurement uncertainty. This study presents an integrated remote sensing-based framework for monitoring quarry waste pile instability by combining multi-temporal change detection with scale-dependent surface roughness analysis. The original contribution of the proposed framework lies in linking displacement-based change detection with multi-scale characterization of surface roughness, enabling both observed surface movement and topographical conditions associated with developing instability to be evaluated within a unified monitoring approach. Multi-epoch Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) and photogrammetric point clouds were acquired before and after documented failure events at an active quarry site at active quarry sites located northeast of Montreal, Quebec, Canada. The regional climatic conditions, characterized by seasonal freeze–thaw cycles, rapid snowmelt, and periods of heavy rainfall, can promote water infiltration and elevated pore-water pressures, thereby increasing the susceptibility of these heterogeneous waste piles to slope instability. A standardized workflow was implemented, including precision alignment using a Recursive Iterative Closest Point (R-ICP) registration strategy, vegetation filtering with a multiscale CANUPO classifier, and uncertainty quantification through a Level of Detection (LoD) analysis. The resulting LoD thresholds were 10–15 cm for LiDAR-to-LiDAR comparisons and 34–36 cm for mixed-sensor datasets. Multi-scale roughness analysis revealed that zones which later experienced instability exhibited consistently higher and more heterogeneous roughness than adjacent stable areas within a well-defined linear scale range. A roughness-based A/D indicator enabled objective delineation of hazardous zones prior to failure. Post-failure monitoring showed surface smoothing following major displacement, followed by renewed roughness increases associated with secondary movements. These results demonstrate that scale-dependent roughness provides complementary information to displacement-based change detection, enabling potentially unstable areas to be identified and prioritized before substantial displacement becomes evident. The integrated framework can assist quarry managers in targeting field inspections and monitoring efforts toward higher-risk areas and support earlier preventive actions to reduce slope-failure risk. Full article

Downhole vibrations are a primary cause of premature wear and failure of rock-cutting elements (RCEs) during well drilling. This study develops an integrated finite element (FEM) model that couples axial and torsional vibrations with the evolution of the wear flat, friction, and temperature. The model is validated against laboratory experiments on a drilling stand using MEMS accelerometers. Two types of tungsten-cobalt (WC-Co) inserts were compared: uncoated and coated with a 3–5 nm titanium nitride (TiN) layer. Thirty tests were performed on granite and sandstone under varying single-RCE weight on bit (WOB = 1.0–2.2 kN) and rotation speed (RPM = 80–120). The TiN coating, by providing a low-friction running-in surface, reduced axial RMS acceleration by 18%, torsional amplitude by 24%, and the steady-state wear rate by 27% (from 0.154 to 0.112 mm/h) in granite. Frequency spectra revealed a resonant torsional peak at 55 Hz when RPM exceeded 120, with torque fluctuations increasing by 240%. A safe operating chart was constructed, defining green (WOB 1.0–1.6 kN, RPM 80–110), yellow, and red zones. The recommended regime (WOB = 1.7 kN, RPM = 105) gives 94% of maximum rate of penetration while reducing predicted wear by 35% compared to the red zone. The model prediction errors are 8–12% for axial and 10–15% for torsional vibrations. This work demonstrates that the proposed laboratory framework, combining nanoscale TiN-coated inserts with low-cost MEMS sensors, enables improved characterization of drilling vibrations and wear and supports the development of practical operating charts for drilling optimization. Full article

Vibration monitoring of glass substrate transfer systems is crucial for ensuring the stable operation of Flat Panel Display (FPD) manufacturing equipment. Fabrication defects in the functional nanofiber membrane of flexible vibration sensors can significantly degrade sensing performance and lead to inaccurate monitoring results. To address the challenge of achieving an effective balance between detection accuracy and inference efficiency in such defect-dense scenarios characterized by large variations in defect scale, this paper proposes a novel defect detection model, termed MA-YOLO. The proposed model incorporates four key architectural enhancements: the Multi-level Bidirectional Feature Aggregation Network (MLBAN), the Multi-Receptive Field Adaptive Fusion Module (MRAF), the Morphology-Adaptive Feature Extraction Module (MA-C2f), and the Interactive Dynamic Decoupling Head (IDDH). These components collaboratively improve defect feature extraction, multi-scale feature fusion, and localization performance while maintaining a lightweight architecture and high inference speed. Experimental results on a self-constructed defect dataset demonstrate that MA-YOLO achieves a mean Average Precision (mAP@0.5) of 91.9%, which is a 3.1 percentage point improvement over the baseline model. Moreover, with only 9.15 million parameters and an inference speed of 119.05 FPS, the proposed model exhibits superior overall performance compared with several mainstream and state-of-the-art object detection methods. Full article

Mayonnaise is a high-viscosity food emulsion whose consistency evolves during shearing due to structural rearrangement and possible emulsion destabilization. This study presents a laboratory-scale proof-of-concept for adapting an established motor current-derived accumulated entropy generation (AEG) framework as a thermodynamic descriptor for monitoring mayonnaise structure changes. First, eight reference fluids were tested using a rotating-bob viscometer at shear rates of 600, 800, and 1000 s⁻¹ to establish the relationship between viscosity and motor current. The corrected current response showed a strong linear correlation with viscosity. The approach was then extended to commercially available mayonnaise samples. Due to the higher viscosity and structured nature of mayonnaise, testing was performed at 1000 s⁻¹, where stable shearing could be achieved. A modified impeller-based viscometer setup was used to continuously shear the mayonnaise and monitor the motor current in situ, while rheometer measurements were performed independently to validate the corresponding viscosity changes during shearing. The motor current decreased with shearing time, consistent with the reduction in measured viscosity. The calculated AEG increased continuously and distinguished the shear stability of different mayonnaise formulations. The viscosity degradation rates of two different mayonnaises are characterized using the degradation coefficient B introduced in the degradation–entropy generation (DEG) theorem. A higher B value indicates greater structural breakdown. These results suggest that current-derived entropic parameters (B coefficient and AEG) may serve as practical, sensor-accessible descriptors for monitoring mayonnaise consistency evolution when direct torque measurement or in-line rheology is unavailable. Full article

A study was performed on the electromagnetic response of the geological medium during the propagation of Rayleigh waves from the 2025 Kamchatka earthquakes (M8.8–7.4) at an epicentral distance of approximately 6000 km. The identities of the coseismic telluric current variations and the propagating Rayleigh wave have been revealed, with their power spectra exhibiting a pronounced quasi-line structure with dominant peaks at 0.04 Hz, 0.054 Hz, and 0.064 Hz. The high correlation of the telluric current response with the Rayleigh wave (r = 0.906, M8.8 and r = 0.83, M7.8) allowed the seismoelectric effect of the second kind to be considered the dominant mechanism for disturbance generation. Electromagnetic signals recorded by the IMS-008 induction sensor showed a lower correlation (r = 0.682), which may be attributed to the shaking of the induction sensor during the passage of the Rayleigh wave, though this does not preclude the presence of a true coseismic signal. Coseismic effects were recorded during the M7.8 and M8.8 earthquakes, but were absent during the M7.4 events. For the first time for the Northern Tien Shan region (at teleseismic distances of approximately 6000 km), key regularities in the generation of the seismoelectric effect during Rayleigh wave propagation have been identified. It is shown that the telluric current response is characterized by a threshold sensitivity to the event magnitude, with the magnitude of induced current variations strictly consistent with the intensity of the seismic wave’s dynamic impact. Utilizing telluric currents as a physical reference for the precise determination of Rayleigh wave arrival times allowed for the refinement of the Rayleigh wave group velocity (3.078–3.093 km/s), reducing calculation uncertainty to 0.5%. Full article

Internal responses of multilayer pavement structures are difficult to characterize using surface-based inspection alone. This study investigates a scaled multilayer pavement model instrumented with embedded quasi-distributed fiber Bragg grating (FBG) sensing lines to obtain baseline internal strain responses and FBG-derived relative vertical displacement distributions under controlled loading. Central single-point stepwise loading, symmetric two-point loading, and asymmetric two-point loading were applied, and FBG wavelength responses were converted into temperature-compensated strain and then into line-wise relative vertical displacement through strain–curvature conversion, curvature integration, and linear baseline correction. During loading, the ambient temperature ranged from 22.70 to 23.40 °C, and the maximum relative shift of the T-sensor was 0.008297 nm. Under 686 N central loading, SAL1 reached a maximum temperature-compensated strain of 1459.39 με and a maximum relative vertical displacement of 1.079 mm, whereas SAL2 reached 793.13 με and 0.583 mm. Under approximately 490 N asymmetric two-point loading, SAT2 reached 1699.48 με and 0.761 mm. Soil-base responses were substantially lower. Because no independent displacement measurement was acquired, the reconstructed quantity is interpreted as an FBG-derived relative deformation measure rather than an absolute displacement. The results establish intact baseline data for future, separately validated comparisons with abnormal conditions. Full article

Reliability assessment in complex technical systems often involves capturing the interdependency between multiple subsystems and the gradual loss of their functional effectiveness. This challenge becomes particularly critical in the context of Unmanned Aerial Vehicles (UAVs), where sustained operational capability is essential for the safe execution of autonomous missions. This work presents a state-based methodology to estimate the future mission capability of UAVs subject to progressive component degradation. To generate a representative dataset, around 600 simulated flight missions for each of the 70 UAV fleet members are conducted, distinguished by randomly varying degradation profiles across multiple actuators. A hidden semi-Markov model (HSMM) is trained on this data to characterize the progressive reduction in system performance over time. To improve model tractability and generalization, raw flight data is first reduced to a concise set of performance-related parameters, from which critical sensor signals, such as roll, pitch, yaw, horizontal and vertical airspeeds, and current consumption, are estimated. The approach is evaluated in comparison to Decision Tree (DT) and XGBoost (XGB) models in a 5-fold cross-validation analysis. It enables the identification of system-wide dependencies between degradation patterns and mission-relevant behavior. By linking current operational states to the likelihood of meeting future performance requirements, it offers a quantitative basis for predictive reliability assessment. The best performance is achieved by an XGB model, whose mission capability estimation roughly doubles the number of conducted missions up to a failure by avoiding risky missions in comparison to unfiltered mission acceptance. Full article

Hilly orchards are characterized by undulating terrain, canopy occlusion, irregular tree-row structures, and marked variations in ground adhesion conditions, which challenge the autonomous navigation of agricultural machinery by degrading perception and localization, increasing reference path uncertainty, and reducing closed-loop robustness. Perception and localization errors can propagate progressively along the chain of “sensor observation–vehicle pose estimation/environmental-structure perception–reference path generation–path-tracking controller inputs–vehicle closed-loop response,” ultimately affecting path-tracking accuracy, control smoothness, and operational safety. This review examines perception and localization error propagation and robust path-tracking control for agricultural machinery in hilly orchards. The review first summarizes the navigation roles and error characteristics of key observation sources and then analyzes how perception, localization, and reference path generation provide state variables, reference variables, and safety constraints to controllers. It subsequently compares typical path-tracking methods under multi-source disturbances and actuator constraints and discusses key challenges and an integrated robust design direction coupling perception and localization, reference path generation, and path-tracking control. The proposed integrated framework represents a synthesis-derived design direction rather than a complete architecture that has already been experimentally validated in hilly orchard field environments. Full article

Terahertz (THz) technology has emerged as a versatile platform enabling advancements across materials characterization, sensing, wireless communication, and biomedical diagnostics. This review provides a unified perspective on these application domains by highlighting the central role of terahertz time-domain spectroscopy (THz-TDS) as a fundamental tool for probing material electrodynamics. THz-TDS enables simultaneous measurement of amplitude and phase of the electric field, allowing contact-free direct extraction of complex permittivity, conductivity and other dielectric properties. Building on this capability, the review connects material-level properties to device and system-level functionalities, including metamaterial-based sensors, graphene-enabled reconfigurable intelligent surfaces (RISs), and beam-steering architectures relevant to 6G and beyond communication systems. Furthermore, the potential of THz techniques in biomedical applications is discussed in detail, particularly for non-invasive tumor detection through dielectric contrast mapping and imaging-based reconstruction methods. By integrating developments across these domains, this review presents THz-TDS as a unifying framework that links materials physics to emerging technologies in sensing, communication, and healthcare, offering insights into future directions for THz research and applications. The principal contribution of this review is to present a cross-domain framework that relates THz field measurements and extracted material electrodynamics to sensing, reconfigurable wavefront control, communication technologies, and biomaterials characterization. Full article