Search Results (83)

Geoelectric field observations play a vital role in geophysical exploration, geological disaster early warning, and underground resource detection. Traditional contact non-polarisable electrodes, which require burial and electrolyte coupling, are hindered by several issues, such as limited adaptability to challenging terrain, significant electrode potential drift, and high susceptibility to environmental interference. Existing non-contact electric field sensors often exhibit insufficient coupling capacitance, poor impedance matching for ultra-weak high-impedance signals, and inadequate low-frequency noise suppression, rendering them unsuitable for the precise acquisition of natural microvolt-level geoelectric field signals. To address these challenges, this study introduces an innovative non-contact geoelectric field sensor with a three-layer composite structure. The sensor operates based on the principle of a parallel-plate capacitor, with a conductive silver paste layer at the top acting as the signal acquisition electrode plate, which forms an equivalent parallel-plate capacitance model with the ground to achieve non-contact capacitive coupling for geoelectric field detection. The intermediate layer uses lead zirconate titanate (PZT) piezoelectric ceramics as a support medium with a high dielectric constant. At the bottom is a silicon-based, flexible, sensitive ground-contacting layer with high elasticity, which allows it to adapt to micro-level surface irregularities, eliminating air gaps between the electrode plate and the ground, increasing plate-to-ground coupling capacitance, and ensuring the stability of the capacitance. The three-layer structure was created using a dry-press sintering integration approach, which eliminates interlayer bonding materials while ensuring consistent dielectric performance and efficient charge transfer. Additionally, a specialised signal-conditioning circuit was designed to match the ultra-high-impedance sensitive unit, utilising the ADA4528-2 ultra-low-noise precision operational amplifier, which achieved low-loss conversion and strong noise suppression for ultra-weak high-impedance charge signals. The circuit simulation results demonstrate that the designed circuit achieves an input impedance of no less than 10 TΩ, an effective operating bandwidth from 0.02 Hz to 20 kHz, and a voltage noise density lower than 1.5 μV/√Hz at 10 Hz, fully covering the ultra-low-frequency effective band of natural geoelectric fields. Field experiments comparing artificial and natural field signals revealed that the proposed sensor could be quickly deployed by simply attaching it to the ground without burial. Its time-domain waveform consistency and frequency-domain component matching were nearly identical to those of commercial standard solid non-polarisable electrodes, with a cross-correlation coefficient greater than 0.98, indicating no significant potential drift or power-frequency interference. By structurally eliminating the inherent electrode potential difference, the sensor offers advantages such as ease of deployment, strong environmental adaptability, high precision for weak signal acquisition, and excellent engineering substitutability. It is well suited for long-term geoelectric field observations in complex field scenarios, including deserts, Gobi areas, and frozen soil regions, and provides a high-performance, novel sensing solution for geoelectric field detection in extreme environments. Full article

Background: Anorexia Nervosa (AN) triggers profound metabolic and psychological adaptations during adolescence. This study investigated the relationships between resting energy expenditure (REE), body composition, physical activity (PA), and specific psychometric phenotypes in adolescent girls with AN compared to healthy, normal-weight (NW) peers. Methods: Sixteen adolescent girls with severe AN (16.1 ± 1.2 year) and 16 age-matched NW controls (15.6 ± 1.5 year) underwent clinical evaluations: anthropometry, bioelectrical impedance analysis for fat-free mass (FFM) and fat mass (FM), indirect calorimetry for REE, the short-form International Physical Activity Questionnaire (IPAQ), and the Eating Disorder Inventory-3 (EDI-3). Results: Girls with AN exhibited significantly lower BMI (−20.3%, ES: 1.94, large), FFM (−18.3%, ES: 1.92, large), and FM (−25.7%, ES: 0.79, medium) than controls (p < 0.003). Absolute REE was drastically lower (−40.4%, ES: 3.53, large, p < 0.001). This hypometabolic state persisted after adjusting REE for body weight (−34.9%) or FFM (−32.5%, p < 0.001), consistent with adaptive thermogenesis. The AN group displayed resting hypotension and 79.0% (ES: 1.84, large, p < 0.001) lower total PA energy expenditure due to a lack of structured moderate-to-vigorous exercise; however, walking expenditure did not differ. Psychometrically, the AN cohort showed significantly higher (p < 0.001) values for drive for thinness (+316.9%, ES: 3.16, large), body dissatisfaction (+232.3%, ES: 2.30, large), and severe general psychological maladjustment, including higher interoceptive deficits (+148.2%, ES: 1.26, large). Conclusions: Adolescents with AN exhibit profound metabolic suppression via adaptive thermogenesis, together with an apparent dissociation between walking-related activity and hypotension. Since cognitive distortions and distress vary independently of anthropometric severity, clinical recovery must extend beyond nutritional rehabilitation; targeted psychotherapy may therefore be necessary to address persistent psychological maladjustment. Full article

An antenna-level bias-line-free, optically controlled pattern-reconfigurable planar antenna for wideband sub-6 GHz applications is presented. The proposed antenna consists of four tapered-slot radiating units arranged in a compact planar configuration and excited through a reconfigurable feeding network incorporating four light-dependent resistors (LDRs). By selectively illuminating LDRs using aligned LEDs, the corresponding feed arms are activated without DC-bias lines embedded in the antenna itself, enabling pattern switching while avoiding a complicated biasing network. The antenna supports ten radiation states, including four principal directions (0°, 90°, 180°, and 270°), four diagonal directions (45°, 135°, 225°, and 315°), and two dual-beam excitations (0°/180° or 90°/270°). Measured results show impedance matching over 3.3–6.7 GHz for all states (at −10 dB |S₁₁| reference), with a realized gain of 5.8–6.3 dBi and a total radiation efficiency of 86–90% across the operating band. The proposed approach offers a compact, wideband, and practically implementable platform for bias-free pattern reconfiguration suitable for coverage and interference adaptation in sub-6 GHz wireless systems. Full article

This paper presents an adaptive combining power amplifier (PA) implemented in the ETGaN15 process. The proposed architecture eliminates the λ/4 impedance transformers required in conventional back-off efficiency enhancement techniques, employing instead a combining network composed solely of microstrip lines and capacitors. The proposed circuit dynamically performs load modulation and power combining according to the input power level, simplifying the overall impedance matching structure while improving efficiency in the output back-off region. The measurement results demonstrate a small-signal gain of 8 dB and a peak output power of 34 dBm across the 10 GHz band. The PA achieves a peak power-added efficiency (PAE) of 28% and a peak drain efficiency (DE) of 41%, while maintaining a PAE of 23% and a DE of 28% at a 6-dB output power back-off (OBO). Full article

In the leveling process of ultra-high-strength steel plates, sample scarcity—driven by high prototyping costs and small-batch production—coupled with a narrow and unevenly distributed feasible region due to high yield-to-tensile ratios and limited ductility, impedes the balanced optimization of plate shape quality and energy consumption. To address this issue, this paper develops an optimization framework for the balanced trade-off between these two objectives. First, a high-precision response surface model based on Box–Behnken experimental design and finite element simulation was constructed using initial wave height, entry roll reduction, exit roll reduction, and leveling speed as key process parameters; peak residual stress difference (characterizing potential sheet quality) and leveling energy consumption as co-optimization objectives; and post-leveling flatness as a constraint. Next, by introducing the NSGA-II multi-objective genetic algorithm, the Pareto optimal solution set for the quality and energy efficiency objectives was obtained, clearly revealing the trade-off relationship between the two; furthermore, the TOPSIS decision-making method was employed to select the comprehensive optimal process scheme that achieves a balance between quality and energy efficiency from the Pareto solution set. An adaptive recommendation curve for the leveling process parameters of MS1500 ultra-high-strength steel plates was established, covering an initial wave height range of 10.5–14.6 mm, thereby enabling intelligent parameter matching based on different incoming material conditions. Finally, industrial validation demonstrated that this optimized scheme significantly reduced leveling energy consumption while ensuring that post-leveling flatness meets the high-quality requirement of less than 3.5 mm·m⁻¹. This achieves a balanced optimization of quality and energy efficiency. This study provides a reliable theoretical basis and practical engineering solution for the efficient and environmentally friendly leveling production of ultra-high-strength steel. Full article

To address the problems in high-speed railway communication, such as large signal penetration loss through carriages, difficulty in long-distance strip coverage, and limited coverage range of traditional base station antennas, this paper designs a cylindrical Luneburg lens antenna operating at the 1800/FA frequency bands. A dual-polarized feed antenna with a dipole structure is designed, loaded with X-shaped metal strips for out-of-band suppression, and integrated with a four-layer dielectric stratified cylindrical Luneburg lens, which uses its graded permittivity distribution to achieve beam focusing, enhance gain, narrow the horizontal beamwidth, and maintain a wide vertical beamwidth. Simulation results show that the lens can stably improve the gain by about 5 dBi; measured results indicate that the antenna has port isolation higher than 35 dB, good impedance matching, and measured gain of 12.4–13.3 dBi within the 1.7–2.1 GHz band, which is highly consistent with the simulation. This antenna can effectively adapt to the long-distance strip coverage scenario along high-speed railways, reduce the base station deployment density, and provide an engineering solution for the optimization of high-speed railway communication coverage. Full article

This study introduces an open-source hybrid shape optimization workflow for acoustic wave problems that integrates acoustic wave propagation analysis with a two-stage optimization strategy. A coarse Parameter Sweep (PS) is first used for feasibility screening and global exploration, followed by derivative-free local refinement using the Nelder–Mead (NM) method. The framework is demonstrated on three benchmark problems: (i) an acoustic horn optimized for improved impedance matching and reduced reflections, (ii) a noise barrier reshaped to minimize acoustic pressure in the shadow zone, and (iii) a crescent-shaped scatterer designed to attenuate downstream pressure amplitude. Across all cases, the PS–NM strategy achieved lower objective values than baseline-initialized local optimization, at the expense of increased computational cost. All analyses were performed in the open-source FEniCS environment within Jupyter Notebooks. Comparisons with published results support the accuracy and consistency of the implementation. By combining accessibility with flexibility, the framework provides a reproducible methodology for acoustic shape optimization. Potential extensions include multi-objective formulations, frequency-adaptive designs, improved constraint-handling strategies, and integration with metamaterial concepts. Full article

Focusing on the self-powering demand of aircraft engine bladed disks (blisks), this paper investigates piezoelectric vibration energy-harvesting modeling and non-linear circuit performance. A multi-sector electromechanical coupled model is established to analyze the frequency splitting and vibration localization induced by minor structural mistuning. By breaking the cyclic symmetry, mistuning severely concentrates vibration energy into a specific sector, providing a localized high-energy concentration region for optimal energy extraction. To enhance recovery efficiency and load adaptability, three interface circuit topologies—Standard Energy-Harvesting (SEH), Parallel Synchronized Switch Harvesting on Inductor (P-SSHI), and Double Synchronized Switch Harvesting (D-SSHI)—are comparatively analyzed. Through wideband spatial–spectral dynamic response and steady-state impedance matching analyses, the non-linear energy conversion and transfer mechanisms are systematically characterized. Results demonstrate that synchronized switching circuits significantly improve energy transmission via forced voltage inversion, accompanied by a notable equivalent stiffness enhancement effect induced by electromechanical coupling. Furthermore, the D-SSHI topology not only exhibits substantial advantages in peak power extraction, but also, owing to its internal LC energy decoupling mechanism, forms a broad load-independent power plateau across an extremely wide impedance range. This research provides robust theoretical foundations for designing highly resilient self-powered intelligent blades under extreme operating conditions. Full article

The convergence of artificial intelligence, biotechnology, and the Internet of Bio-Nano Things (IoBNT) is enabling the creation of a new generation of intelligent in-body medical devices for continuous diagnosis and monitoring. In this context, a compact, wideband, circularly polarized conformal microstrip antenna is proposed for capsule endoscopy applications. The antenna is integrated along the inner wall of a 10 mm-diameter capsule and achieves an impedance bandwidth of 2.06–5.39 GHz (89.39%), maintaining stable matching under varying biological tissue conditions. A 3 dB axial ratio bandwidth (ARBW) of 2.31–3.14 GHz (30.45%) ensures reliable circular polarization and robust wireless communication in lossy and dynamic in-body environments. To extend beyond conventional electromagnetic analysis, a physics-informed neural network (PINN) framework is introduced to model the thermal response of biological tissues based on the governing bioheat equation. This AI-driven approach enables fast and generalizable prediction of temperature rise under varying operational conditions without repeated numerical simulations. At 2.45 GHz, the antenna exhibits a maximum gain of $-31.1$ dBi with a radiation efficiency of approximately $-34$ dB, consistent with in-body propagation constraints. Simulation and experimental results in realistic tissue phantoms, including muscle, small intestine, large intestine, and stomach, confirm stable wideband and polarization performance. Specific absorption rate (SAR) analysis demonstrates compliance with IEEE C95.1-2019 safety limits, while link budget evaluation validates reliable telemetry over a 1–3 m communication range. The integration of advanced antenna design with physics-informed machine learning provides a scalable framework for intelligent, safe, and adaptive IoBNT-enabled capsule endoscopy systems. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

In impedance imaging, the incompatibility and nonlinearity of the inverse problem lead to problems such as blurred boundaries and severe artifacts in the reconstructed images, making it difficult to meet the requirements for precise identification of multi-layer tissue structures in the legs. To this end, this paper proposes a post-processing algorithm for leg EIT that integrates the boundary attention mechanism, with a Wasserstein generative adversarial network as the training framework, cyclic residual U-Net as the generator, and the boundary attention module embedded in the RecurrentBlock. This leads to adaptive enhancement of the ability to extract organizational boundary features through a three-path fusion of spatial attention, channel attention, and learnable Laplacian edge enhancement. A leg anatomy prior constraint loss function was designed, integrating six constraints—pixel loss, edge loss, hierarchical tissue constraint, total variation regularization, structural similarity loss, and histogram matching—to guide the reconstruction results to conform to the multi-layered tissue structure features of the leg. A simulation dataset of leg sections containing multiple tissues such as skin, fat, muscle, bone, blood vessels, and nerves was constructed, and the pre-reconstructed images were obtained using the hybrid total variation regularization algorithm as the network input. The simulation results show that, under noise-free and different signal-to-noise ratio conditions, the proposed BAM-R2UNet algorithm achieves the best performance in RMSE, SSIM and PSNR metrics compared with HTV, DnCNN and standard U-Net algorithms, can remove artifacts, accurately restore the boundary and conductivity distribution of leg tissues, and has stronger anti-noise robustness. Full article

The efficiency of wireless systems critically depends on the ability of antennas to transfer power from the transmitter circuitry into free space. Although maximum power transfer is theoretically achieved under perfect impedance matching, IoT devices rarely meet this condition due to the ever-changing conditions of the surrounding environment. As a result, a portion of the transmitted power is reflected, reducing the effectively radiated power and degrading system performance. In addition to these radiated losses, load mismatch at the power amplifier output can lead to gain degradation, increased power dissipation, and impaired performance of linearization schemes such as digital predistortion. Such an effect is well-known but has never been quantified. The purpose of this paper is to quantify not only the losses arising from reflection due to impedance mismatch but also those associated with the reduction in amplifier gain by considering both antenna- and amplifier-level perspectives. Theoretical calculations of mismatch losses are first developed and analysed. These results are subsequently validated in an idealised environment, followed by experimental demonstrations in realistic device scenarios, where substantial discrepancies with theoretical predictions and controlled measurements are observed. The findings quantitatively separate and superimpose, for the first time in a unified experimental framework, the radiative mismatch losses (antenna and matching network) from the additional power amplifier gain degradation under realistic load conditions. This demonstrates that passive antenna measurements alone significantly underestimate the total radiated power loss in practical IoT devices. The results emphasise the need to account for real-world operating conditions when evaluating mismatch-induced losses and highlight the importance of co-design and adaptive strategies for both antennas and power amplifiers in future wireless and IoT systems. Full article

Early fault vibration signals from rolling bearings are typically nonlinear, non-stationary, and heavily obscured by background noise, which severely impedes the accurate extraction of fault features. To overcome the limitations of traditional stochastic resonance (SR)—specifically the small-parameter restriction for high-frequency signals and the subjectivity in parameter selection—this paper proposes an adaptive SR envelope spectroscopy method based on particle swarm optimization (PSO) and local mean decomposition (LMD). First, a variable-scale transformation is introduced to compress the high-frequency fault signals into the effective frequency band required by the adiabatic approximation theory. Second, utilizing the global search capability of PSO, the potential well parameters of the bistable system are adaptively optimized by maximizing the output signal-to-noise ratio (SNR), thereby achieving optimal matching between the nonlinear system and the input signal. Finally, the enhanced signal is decomposed by LMD, and the sensitive components are selected for envelope spectrum analysis to identify fault characteristics. Experimental validation using the Case Western Reserve University bearing dataset demonstrates that the proposed method effectively amplifies weak fault signals under strong noise conditions, exhibiting superior feature extraction accuracy and noise robustness compared to traditional methods. Full article

Flexible wearable electronics have shown strong potential for medical and health monitoring; however, conventional materials often fail to simultaneously satisfy the requirements of signal stability, wear comfort, and environmental adaptability under dynamic use conditions. To address this issue, this study proposes a data-driven material selection framework for flexible wearable sensors based on the extreme gradient boosting (XGBoost) algorithm. The model integrates user perception, material physical parameters, and environmental coupling performance indicators to enable intelligent material matching and recommendation. Experimental results show that the proposed model achieves a recommendation accuracy of 94.5%, outperforming conventional comparison methods. Among the candidate materials, silver nanowires (AgNWs) exhibit superior overall performance, including a higher signal-to-noise ratio, lower skin-contact impedance, and stronger sweat resistance. In physiological monitoring experiments, the maximum deviation of the sensor response was below 3% under both static and motion conditions. In environmental coupling tests, the recommended material improved the system signal-to-noise ratio by 68% and reduced 24-h sensitivity decay by 75%. These results indicate that the proposed XGBoost-based framework can effectively support material selection for flexible wearable sensors and improve signal reliability and environmental adaptability in complex application scenarios. Full article

The design and implementation of a dynamic frequency sweeping algorithm for a 3 kW RF power source are underpinned by theoretical principles aimed at optimizing impedance matching under pulse-mode operation. The algorithm dynamically adjusts the output frequency within a predefined range to align the source impedance Z_source with the conjugate of the load impedance Z*_load, maximizing the power transfer efficiency and minimizing the reflection coefficient Γ. This is achieved by leveraging the maximum power transfer theorem and adapting to dynamic load variations, such as those induced by the plasma state transitions. The algorithm incorporates adaptive step size adjustments based on the rate of change of Γ, predictive frequency initialization using historical data, and real-time impedance monitoring to ensure efficient convergence within the constrained pulse “ON” time (TON). Integration with pulse mode requires synchronization with the pulse signal, fast convergence, and optimized search strategies. Experimental validation on a 13.56 MHz, 3 kW Automatic Sweep Generator testbed operating at 20 kHz pulse modulation with a 50% duty cycle demonstrates a linear and stable sweep, achieving impedance matching and low reflected power within 5.0172 ms. These findings highlight the algorithm’s potential for high-precision applications, such as RF plasma excitation, and underscore the importance of adaptive techniques in dynamic RF systems. Full article