Search Results (10,978)

Low-frequency, irregular water waves with continuously changing propagation directions are difficult to harvest efficiently using conventional power generation devices. This work proposes a gyroscope-pendulum-coupled multilayer triboelectric nanogenerator (GP-TENG), in which a multi-axis gyroscope mechanism, an inertial pendulum, and a helical-structured power generation module are integrated inside a spherical floating body. The gyroscope joints enable the pendulum to respond to waves arriving from any horizontal direction, while the heave and tilting motions of the floating body jointly drive periodic contact and separation of the multilayer triboelectric materials. Motor-driven platform and water tank experiments were conducted to investigate the effects of the number of generating layers, excitation frequency, translational stroke, swing amplitude, and external resistance on the output performance. In the controlled translational tests, the maximum root-mean-square open-circuit voltage, short-circuit current, and transferred charge reached 98.6 V, 2.3 μA, and 242 nC, respectively, and a maximum output power of 16.3 μW was obtained at a load of 81 MΩ. In the water tank, the GP-TENG showed a stable response near 1.42 Hz, with maximum output power of 3.45 μW at a 60 MΩ load. The generator successfully charged the capacitor, lit up LEDs, and powered a commercial temperature and humidity sensor. These results indicate that the GP-TENG provides a compact and low-cost approach for omnidirectional low-frequency wave energy harvesting and a distributed power supply for low-power marine electronic devices. Full article

Asphalt pavements undergo progressive mechanical changes during service life due to traffic loading, temperature variations, moisture and material ageing. Embedded strain sensors can support in-situ pavement performance monitoring, but their response is strongly affected by experimental variables that must be identified before reliable long-term ageing indicators can be established. This study establishes an in-situ baseline mechanical monitoring framework for asphalt pavements using embedded resistive strain transducers. KM-100HAS sensors were installed in an asphalt test section and evaluated through controlled field campaigns. A 17-point cross-pattern loading procedure was used to validate sensor location and orientation after construction. Load-free monitoring windows were analysed to estimate strain–temperature sensitivity and assess thermal correction of static loading–recovery tests. The results showed that loading position strongly conditions the measured strain response. Passive monitoring indicated that strain–temperature sensitivity depends on both temperature level and sensor location. In the mechanical tests, normalization of the recovery branch and logarithmic fitting over the first 200 s provided a consistent recovery-shape descriptor. The resulting slope, $b_{\mathrm{l}\mathrm{o}\mathrm{g}200}$, showed a strong linear relationship with the recovery percentage after 10 min (R² = 0.855). The proposed workflow provides a standardized baseline protocol for asphalt pavement monitoring and its mechanical evolution. Full article

Spacecraft and launch-vehicle structures require lightweight multipoint monitoring under combined mechanical, thermal, and environmental loads. This study presents an analytical fiber Bragg grating (FBG) sensor-network workflow integrating reference-grating temperature compensation, regional strain assessment, opposite-surface curvature sensing, wavelength-division-multiplexing allocation, and strain-to-shape reconstruction. The deterministic compensation case is used only as a self-consistency check, whereas practical robustness is assessed through 10,000 Monte Carlo trials incorporating packaged-coefficient mismatch, temperature nonuniformity, wavelength noise, strain-transfer variation, drift, and calibration uncertainty. The calibrated estimator achieved a median strain mean absolute error of 1.73 με and a 95th-percentile error of 4.22 με. The defined finite-element benchmarks produced a maximum engine-mount truss strain of 456.2 με under the defined loads and a median full-field panel-reconstruction normalized root-mean-square error of 1.29% for 18 sensing locations with 2 με noise. Conservative WDM analysis yielded 54, 13, and 16 channels for three operating envelopes, and the prescribed random-vibration spectrum produced 6.78 g_rms. These results demonstrate a reproducible numerical proof of concept and define practical limits for compensation, spectral allocation, curvature interpretation, and inverse reconstruction; they do not constitute experimental validation or flight qualification. Full article

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

Soft magnetic materials determine the efficiency, size, thermal burden, and reliability of transformers, inductors, electrical machines, electromagnetic interference (EMI) components, and magnetic sensors. This review differs from property-by-property surveys by using a condition-aware, application-driven framework: magnetic performance is compared only together with frequency, peak magnetic flux density, temperature, waveform, direct current (DC) bias, geometry, and processing route. After a concise treatment of coercivity, permeability, saturation polarization, magnetostriction, and loss mechanisms, the major material families are quantitatively compared in terms of magnetic performance, processing, cost, and industrial maturity. The review then maps these families onto grid transformers, high-speed electrical machines, wide-bandgap power converters, integrated magnetics, wireless power transfer, aerospace electrical systems, and radiofrequency components. Particular attention is given to the trade-offs among saturation polarization, permeability, core loss, mechanical strength, thermal stability, manufacturability, and sustainability. Recent advances in strong and ductile soft magnets, wide-temperature ferrites, vortex and easy-plane composites, mixed-powder soft magnetic composites, nanocrystalline flake-ribbon cores, and additive manufacturing are assessed by technology maturity. A prioritized roadmap identifies near-term needs for standardized condition-specific data and manufacturing control, medium-term opportunities in magnetic–thermal co-design and digital twins, and longer-term prospects for adaptive, self-healing, and GHz magnetic architectures. The resulting framework is intended to support both material development and defensible industrial material selection. Full article

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

Ensuring high crop productivity while reducing nutrient losses and environmental impacts remains a central challenge of modern fertilization strategies. Controlled-release fertilizers (CRFs) have emerged as a key tool to synchronize nutrient availability with plant demand by embedding soluble nutrient sources within coatings or matrices that modulate water penetration and ion diffusion. This narrative review synthesizes recent advances in CRF materials and technologies, with particular emphasis on biodegradable and bio-based systems, hydrogels, nanostructured carriers, and extrusion-based matrix formulations. It outlines the main classes of coating and matrix materials, their release mechanisms, agronomic performance, and documented benefits for nutrient-use efficiency, crop yield and quality, and soil and water protection. The review also analyzes major scientific, technical, environmental, and economic barriers that currently limit the large-scale deployment of CRFs, including microplastic pollution from persistent polymer coatings and the difficulty of tailoring release profiles under variable field conditions. Emerging directions are highlighted, such as composite and multilayer bio-based coatings, superhydrophobic and stimuli-responsive structures, biochar- and compost-based matrices, nutrient recovery from waste streams, and integration with precision agriculture and sensor technologies. The paper identifies priorities for future research needed to translate promising CRF concepts into robust, field-validated and sustainable fertilization solutions. Full article

Needle roller bearings are characterized by rolling elements with relatively high length-to-diameter ratios and are widely used in mechanical systems with limited radial installation space. In gear transmission systems, periodic impact loads induced by gear meshing may be superimposed on steady radial loads, thereby altering the dynamic response of the bearing. However, the effects of the amplitude and frequency of periodic impact loading on the dynamic characteristics of needle roller bearings remain insufficiently understood. In this study, a needle roller bearing test rig capable of applying periodic impact loading was developed, and a multi-sensor measurement system was configured to measure outer-ring vibration, inner-ring motion, cage motion, and the friction torque of the bearing system. Dynamic tests were conducted under different amplitudes and frequencies of periodic impact loading. A reduction in bearing motion stability was observed under periodic impact loading, as evidenced by increased outer-ring vibration and enlarged cage motion in both the horizontal and vertical directions. As the loading amplitude increased, the RMS values of outer-ring acceleration, inner-ring displacement, and cage displacement increased, while an increase in the mean friction torque was also observed. The inner-ring trajectory expanded along the loading direction, and the whirling range of the cage trajectory increased. As the loading frequency increased, the RMS values of outer-ring acceleration, inner-ring displacement, and cage displacement increased, and the mean friction torque increased. In contrast, the whirling range of the cage trajectory decreased. These findings clarify the distinct effects of periodic impact-loading amplitude and frequency on bearing vibration, internal motion, and friction-torque characteristics and provide experimental support for the dynamic performance evaluation of needle roller bearings under impact conditions. Full article

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

In this research, a high-performance plasmonic refractive index ($RI$) biosensor based on an external metal deposition ($EMD$) technique and photonic crystal fiber ($PCF$) platform for potential cancer detection is presented, investigated, and linked with real-time cancer cells. Variations in the $RI$ of biological fluids are closely associated with pathological conditions, including cancer, due to changes in cellular composition and biomolecular concentration. The proposed tetra-core $PCF–SPR$ biosensor operates within the biologically relevant $RI$ range of $1.33–1.37$, enabling the detection of subtle $RI$ variations corresponding to various cancerous cells. The sensing mechanism of the proposed sensor is based on surface plasmon resonance ($SPR$) and analyzed using coupled mode light theory for both $x\text{-}$ and $y\text{-}$ polarized modes. Key sensing performance parameters, including confinement loss ($CL$), wavelength sensitivity ($WS$), amplitude sensitivity ($AS$), sensor resolution ($SR$), and figure of merit ($FOM$) are systematically evaluated. The biosensor reports a $WS$ of $9769$ and $9069 nm/RIU$ for $x\text{-}pol.$ and $y\text{-}pol.,$ respectively, $AS$ of $623.182$ and $645.087 {RIU}^{-1}$ for $x\text{-}pol.$ and $y\text{-}pol.$ respectively, $SR$ in the order of ${10}^{-5}$ $RIU$, coefficient of determination ($R^2$) of $0.97$ and $0.96$, and $FOM$ of $60.17$ and $53.01 {RIU}^{-1}$ for $x\text{-}pol.$ and $y\text{-}pol.$, respectively. Thus, the proposed $PCF–SPR$ biosensor exhibits a dynamic range of $0.04 RIU$. The sensing results demonstrate high sensitivity and strong resonance characteristics, indicating the capability of the proposed biosensor for label-free and non-invasive detection of cancer-associated $RI$ changes in biological fluids. Thus, the presented biosensor offers a promising approach for the highly sensitive label-free detection of early-stage cancer cells by photonics sensing application. Full article

The prosthetic socket is the essential interface between the residual limb and the prosthesis. However, traditional upper-limb sockets are highly sensitive to residual-limb volume fluctuations and rely on experience-dependent manual fabrication processes. Additive manufacturing and 3D scanning technologies offer the possibility of producing personalized sockets, potentially improving accessibility, repeatability, and cost-effectiveness. Despite this potential, there is still a lack of systematic experimental validation directly comparing the fitting performance of personalized 3D-printed sockets with that of conventionally manufactured sockets. This study presents the design, fabrication, and comparative validation of a personalized 3D-printed upper-limb socket and a traditionally manufactured socket produced for the same user. The 3D-printed socket was developed from a 3D scanning of a clinically rectified plaster mold, computer-aided design, and material extrusion by thermal reaction bonding. A comparative validation framework evaluated both sockets under equivalent conditions, including mechanical testing, thermal behavior assessment, and sensorized fitting measurements. The results provide quantitative evidence on how a personalized 3D-printed socket performs relative to a traditional socket in terms of structural behavior, thermal patterns, and interface-related fitting parameters. This work contributes objective data supporting 3D-printed personalized sockets as a technically feasible and promising alternative to conventional methods. Full article

Thermal drying is a critical phase in coffee processing, significantly influencing energy consumption, moisture uniformity, storage stability, and sensory quality. Despite the superior throughput of mechanical dryers over open-sun drying, these systems often operate with limited observability and manual control. This systematic review evaluates the readiness of mechanical coffee drying for Digital Twin (DT) integration. A comprehensive search across Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 22,859 records. Following multi-stage screening, 58 studies were retained for qualitative synthesis, categorized into a primary coffee-drying corpus and a secondary transferable corpus of methods from related food-processing applications. Findings indicate that while DT-enabling components, such as CFD models, drying-kinetics models, IoT monitoring, and non-destructive sensing, are established, they remain fragmented. No fully implemented and operationally validated DT for mechanical coffee drying was identified. Based on the evidence, a hybrid reduced-order physics-based model integrated with constrained supervisory control represents the most defensible near-term architecture. Future research should prioritize standardized datasets, uncertainty-aware soft sensors, and field validation across diverse dryer topologies and operating conditions. Full article

Ultra-high-pressure and extreme-pressure metrology plays a critical role in advanced manufacturing, geoscience, high-pressure physics, and frontier materials research. As pressure ranges extend from hundreds of MPa to several GPa and beyond, pressure generation and measurement are increasingly constrained by material strength, structural deformation, the state of pressure-transmitting media, sealing reliability, sensor drift, and incomplete traceability chains, imposing higher requirements on pressure metrology. This review systematically examines measurement technologies and traceability routes for ultra-high-pressure and extreme-pressure ranges. The development of controlled-clearance piston gauges is summarized, with emphasis on uncertainty reduction, range extension, and calibration automation. Drop-weight-based primary standards for dynamic pressure are also reviewed as an established route for the traceable calibration of high-amplitude, millisecond-scale hydraulic pressure pulses. Progress in secondary ultra-high-pressure standards is reviewed, including ultra-high-pressure gauges and piezoresistive, resonant, fiber-optic, and triboelectric sensors, with focus on range extension, high-accuracy measurement, environmental adaptability, and emerging pressure-sensitive mechanisms. Measurement methods for extreme pressure, including ruby fluorescence, Raman spectroscopy, X-ray diffraction, phase-transition points, and equations of state, are compared in terms of applicability and limitations. Current challenges in ultra-high-pressure and extreme-pressure metrology are further discussed, together with the prospects of quantum pressure sensing based on nitrogen-vacancy centers in nanodiamonds, providing a reference for future research and metrological infrastructure development. Full article

Overhead-line sensor platforms must satisfy electrical and mechanical installation constraints that vary with the operating scenario. This study evaluates separate phase-conductor electrostatic and 9 mm optical ground wire (OPGW) vibration-test scenarios for the same platform. A full Maxwell potential-coefficient matrix provides an analytical reference for a 500 kV line-to-line RMS four-bundle conductor. A three-dimensional COMSOL Multiphysics 6.3 model compares the prototype enclosure scale and fastening-hole configurations. The matrix gives a maximum bare-conductor surface field of 14.33 kV/cm RMS. The phase-RMS values for the hole-free, single-hole, and double-hole cases are 8.05, 12.27, and 12.91 kV/cm RMS, respectively. These values quantify local field enhancement at the hole edge and support geometry comparison. Under a 16.5 kN tensile load, 47.71 Hz vibration, ±2.4 mm cable amplitude, and 1 × 10⁷ cycles, the OPGW test showed no visually detectable slippage or cable damage. The two scenarios provide electrical-geometry and mechanical-interface evidence under the specified analysis and test conditions. Full article

In this paper, the design and simulation of a monolithically integrated silicon carbide (SiC) MEMS piezoresistive accelerometer with an on-chip SiC field-effect transistor (SiC-FET) readout circuit are presented for applications in harsh environments. The proposed device can be fully realized on a single SiC platform, ensuring robust operation at a very high temperature (1000 K) and removing the bottlenecks associated with heterogeneous integration and silicon-based sensor electronics. Finite-element-method (FEM) simulations were performed to investigate the mechanical performance of the accelerometer, including sensitivity, cross-axis response, resonant frequency, and thermal stability. The designed accelerometer exhibits a z-axis sensitivity of 35.8 $\Omega$/g with low cross-axis sensitivities of 0.27% and 2.12% along the x- and y-axes, respectively. The eigen frequency analysis shows a fundamental resonance frequency of 1640 Hz and an operating bandwidth of 328 Hz. Thermal simulations indicate excellent stability performance from 300 K to 1000 K with resistance and displacement variations of less than 0.21% and 0.009%, respectively. A 4H-SiC MOSFET was designed and analyzed by the TCAD process and device simulations. It was shown to operate stably in a wide temperature range without breakdown. The FEM and TCAD results were incorporated into a Verilog-A model and implemented in Cadence Virtuoso to evaluate the complete sensor-readout system. The integrated SiC-FET common-source amplifier achieved a sensitivity of up to 100 mV/V at 1000 K with a non-linearity of approximately 1%. The results demonstrate the feasibility of a fully integrated all-SiC accelerometer platform for high-temperature sensing applications in aerospace, automotive, energy, and industrial environments. Full article