Search Results (1,019)

Molecularly imprinted polymer (MIP) biosensors offer an attractive strategy for selective biomolecule detection, yet imprinting proteins with structural fidelity remains a major challenge. In this work, we present a rationally designed electrochemical biosensor for matrix metal-loproteinase-8 (MMP-8), a key salivary biomarker of periodontal disease. By integrating graphene oxide (GO) with electropolymerized poly(eriochrome black T, EBT) films on screen-printed carbon electrodes, the partially reduced GO interface enhanced electrical conductivity and facilitated the formation of well-defined poly(EBT) films with re-designed polymerization route, while template extraction generated artificial antibody-like sites capable of specific protein binding. The MIP-based electrodes were comprehensively validated through morphological, spectroscopic, and electrochemical analyses, demonstrating stable and selective recognition of MMP-8 against structurally similar interferents. Complementary density functional theory (DFT) modeling revealed energetically favorable interactions between the EBT monomer and catalytic residues of MMP-8, providing molecular-level insights into imprinting specificity. These experimental and computational findings highlight the importance of rational monomer selection and nanomaterial-assisted polymerization in achieving selective protein imprinting. This work presents a systematic approach that integrates electrochemical engineering, nanomaterial interfaces, and computational validation to address long-standing challenges in protein-based MIP biosensors. By bridging molecular design with practical sensing performance, this study advances the translational potential of MIP-based electrochemical biosensors for point-of-care applications. Full article

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

Advanced oxidation processes (AOPs) utilizing peroxymonosulfate (PMS) have recently gained attention for effectively removing organic dyes. Biochar, a carbon-based material, can act as a catalyst carrier for PMS activation. This study developed a nitrogen-doped biochar catalyst (NCMR800–2) from waste Chinese medicine residue (CMR) through one-step pyrolysis to efficiently remove Rhodamine B (RhB) from wastewater. Results indicate that NCMR800–2 rapidly achieved complete removal of 20 mg/L Rhodamine B (RhB), the primary focus of this study, within 30 min, while maintaining high degradation efficiencies for other pollutants and significantly outperforming the unmodified material. The material demonstrates strong resistance to ionic interference and operates effectively across a wide pH range. Quenching experiments and in situ testing identified singlet oxygen (¹O₂) as the primary active species in RhB degradation. Electrochemical analysis showed that nitrogen doping significantly enhanced the electrical conductivity and electron transfer efficiency of the catalyst, facilitating PMS decomposition and RhB degradation. Liquid chromatography–mass spectrometry (LC-MS) identified intermediate products in the RhB degradation process. Seed germination experiments and TEST toxicity software confirmed a significant reduction in the toxicity of degradation products. In conclusion, this study presents a cost-effective, efficient catalyst with promising applications for removing persistent organic dyes. Full article

To explore the advantages and limitations of employing square-wave modulated single-frequency signals in electric field radiated susceptibility testing, critical interference effect tests using both single-frequency continuous waves and square-wave modulated single-frequency radiation fields were conducted, respectively, at four susceptible frequencies (98, 262, 326, 404 MHz) of a linear voltage regulator and two susceptible frequencies (26, 36 MHz) of a switching-mode power supply. The variation law of critical interference field strength according to the modulation period was determined. The test results demonstrate that the output interruption in the tested power supplies was not only determined by the interference field strength and frequency but also significantly influenced by the repetition period of the interference signal. Square-wave modulated single-frequency interference provides superior characterization of the time-domain response characteristics of the equipment under testing when compared to conventional single-frequency continuous wave interference. However, RS103 only employs a modulated signal with a 1 ms repetition period, making it insufficient to fully characterize the actual susceptible characteristics of the tested equipment. Therefore, it requires supplementary evaluation through critical interference testing using single-frequency continuous waves. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article

Electric vehicle (EV) drivetrains often suffer from degraded control precision due to resolver zero-position deviation. This issue becomes particularly critical under diverse automotive-grade operating conditions, posing challenges for achieving reliable and efficient drivetrain performance. To tackle this problem, we propose a dual-mode PID dynamic compensation control methodology. This approach establishes a divide-and-conquer framework that differentiates between weak-magnetic and non-weak-magnetic regions. It integrates current loop feedback with a fuzzy self-tuning mechanism, enabling real-time dynamic compensation of the resolver’s initial angle. To ensure system stability under extreme automotive conditions (−40 °C to 125 °C, ±0.5 g vibration, and electromagnetic interference), a triple-redundancy architecture is implemented. This architecture combines hardware filtering, software verification, and fault diagnosis. Our contribution lies in presenting a reliable solution for intelligent EV drivetrain calibration. The proposed method effectively mitigates resolver zero-position deviation, not only enhancing drivetrain performance under challenging automotive environments but also ensuring compliance with ISO 26262 ASIL-C safety standards. This research has been validated through its implementation in a 3.5-ton commercial logistics vehicle by a leading automotive manufacturer, demonstrating its practical viability and potential for widespread adoption in the EV industry. Full article

To improve the reliability and fault tolerance of electric helicopter propulsion systems, this paper presents a master–slave fault-tolerant control method based on an extended state observer (ESO) for dual-redundant electric propulsion systems that addresses dynamic coupling disturbances. First, the control architecture puts the master motor in speed loop mode and puts the slave motor in torque loop mode with an ESO to estimate disturbances and compensate for mechanical coupling torque through feedforward control based on Lyapunov stability theory. Second, a least squares parameter identification method establishes a current-torque mapping model to ensure consistent dual-motor output. Then, fault-tolerant switching is implemented, transitioning from normal torque mode coordination to independent speed mode with adaptive PI adjustment during faults. Experimental validation shows that the total torque stabilizes at 240 N·m, and the synchronization error remains within ±0.5 N·m during normal operation. Under single-motor fault scenarios, the ESO detects disturbances within 15 ms with >95% accuracy. The system speed decreases to a minimum of 2280 rpm (5% deviation) and recovers within 3.5 s. Compared to traditional PI control, this method improves torque synchronization by 65.4%, speed stability by 62.6%, and dynamic response by 51.2%. Finally, the results validate that the method effectively suppresses coupling interference and meets aviation safety standards, providing reliable, fault-tolerant solutions for electric helicopter propulsion. Full article

A green and cost-effective method was employed to efficiently synthesize conductive silica–silver (SiO₂/PCPA/Ag) core–shell structured microspheres. The SiO₂ microspheres were initially functionalized with poly(catechol-polyamine), followed by the in situ reduction of Ag ions to Ag nanoparticles on the surface of the SiO₂ microspheres using an electroless plating process. Analysis using scanning electron microscopy confirmed the successful formation of a dense and uniform silver layer on the surface of the SiO₂ microspheres. The valence state of the silver present on the surface of the SiO₂ microspheres was determined to be zero through analyses conducted using an X-ray photoelectron spectrometer and X-ray diffractometer. Consequently, the SiO₂/PCPA/Ag microspheres, upon initial preparation, demonstrated a notable conductivity of 1005 S/cm, which was further enhanced to 1612 S/cm following additional heat treatment aimed at rectifying defects within the silver layer. The resulting rubber composites displayed a low electrical resistivity of 5.4 × 10⁻³ Ω·cm and exhibited a significant electromagnetic interference (EMI) shielding effectiveness exceeding 100 dB against both X-band and Ku-band frequencies, suggesting promising potential for utilization as a material for conducting and EMI shielding purposes. Full article

From the perspectives of theoretical design and practical application, the existing fault diagnosis methods with the complex identification process owing to manual feature extraction and the insufficient feature extraction for time series data and weak fault signal is not suitable for AC/DC microgrids. Thus, this paper proposes a fault diagnosis method that integrates a convolutional neural network (CNN) with a long short-term memory (LSTM) network and attention mechanisms. The method employs a multi-scale convolution-based weight layer (Weight Layer 1) to extract features of faults from different dimensions, performing feature fusion to enrich the fault characteristics of the AC/DC microgrid. Additionally, a hybrid attention block-based weight layer (Weight Layer 2) is designed to enable the model to adaptively focus on the most significant features, thereby improving the extraction and utilization of critical information, which enhances both classification accuracy and model generalization. By cascading LSTM layers, the model effectively captures temporal dependencies within the features, allowing the model to extract critical information from the temporal evolution of electrical signals, thus enhancing both classification accuracy and robustness. Simulation results indicate that the proposed method achieves a classification accuracy of up to 99.5%, with fault identification accuracy for noisy signals under 10 dB noise interference reaching 92.5%, demonstrating strong noise immunity. Full article

The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and destructive nature. In contrast, recent developments in nondestructive testing (NDT) technologies have enabled precise, automated, and real-time evaluation of eggshell characteristics. This review systematically summarizes state-of-the-art NDT techniques including acoustic resonance, ultrasonic imaging, terahertz spectroscopy, machine vision, and electrical property sensing. Deep learning and sensor fusion methods are highlighted for their superior accuracy in microcrack detection (up to 99.4%) and shell strength prediction. We further discuss emerging challenges such as noise interference, signal variability, and scalability for industrial deployment. The integration of explainable AI, multimodal data acquisition, and edge computing is proposed as a future direction to develop intelligent, scalable, and cost-effective eggshell inspection systems. This comprehensive analysis provides a valuable reference for advancing nondestructive quality control in poultry product supply chains. Full article

This study employs CST simulations to analyze the electromagnetic response and cable coupling characteristics of electric vertical takeoff and landing (eVTOL) aircraft under lightning conditions. Based on the SAE ARP5414B standard, lightning zoning was carried out, and three typical strike scenarios—the nose, wing, and vertical tail—were established. Referring to representative lightning current waveforms in SAE ARP5412B, Component A was selected as the primary excitation source. On this basis, the L9(3³) orthogonal design method was applied to evaluate the influence of cable structure, length, and routing method on the induced current. The results show that nose attachment produces the strongest coupling to the airframe. Shielded cables effectively reduce the induced current in the conductor core by diverting most of the coupled current through the shielding layer, while unshielded single-core cables demonstrate the weakest resistance to interference. The induced current increases with cable length, and Z-shaped wall-mounted routing produces stronger coupling than straight or suspended routing. This research provides a systematic approach for evaluating indirect lightning effects in eVTOL and offers engineering guidance for electromagnetic protection and cable design. Full article

Effective diagnosis of distribution network faults is crucial to ensuring the reliability of power systems. However, the bidirectional power flow caused by the integration of new energy limits the effectiveness of traditional detection methods. Although data-driven approaches are not restricted by power flow direction, their performance is heavily dependent on the quantity and quality of training samples. In addition, factors such as measurement noise, variable fault impedance, and volatile photovoltaic output complicate fault information. To address this, we present a new fault diagnosis model named the dynamic, adaptive, and coupled dual-field-encoding graph neural network (DACDFE-GNN), which introduces a dynamic aggregation module to assign different weights to reduce noise interference and fully integrates information from observable nodes. On this basis, the coupled dual-field-encoding module is proposed, which encodes topological information and physical–electrical domain information as part of the initial features, thereby capturing fault features and learning the law of feature propagation. The experimental results for the IEEE 34- and IEEE 123-node feeder systems indicate that the proposed model surpasses recent fault diagnosis methods in detection performance, particularly regarding its low training sample rate. Full article

The television white space (TVWS) spectrum presents a promising opportunity to extend wireless broadband access, particularly in rural, underserved, and hard-to-reach communities. To leverage this potential, low-power radio communication equipment must efficiently utilise the TVWS spectrum on a secondary basis while ensuring strict compliance with regulatory requirements to prevent harmful interference to primary services. This paper presents a comparative performance analysis of TVWS radio equipment from three original equipment manufacturers (OEMs). The equipment under test was identified to reflect each OEM, as follows: OEM 1 and OEM 2 from South Korea and OEM 3 from the USA. We evaluated their performance in two real-world field scenarios, namely outdoor short-distance and outdoor long-distance. The evaluation was based on the following key metrics: (i) spectrum utilisation efficiency (SUE), (ii) received signal strength (RSS), (iii) downlink throughput, and (iv) connectivity to the Geo-Location Spectrum Database (GLSD) in compliance with the South African TVWS regulatory framework. The overall preliminary experimental results indicate that in both scenarios, white space devices (WSDs) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11af Standard demonstrated better performance than those based on the 3rd Generation Partnership Project Long-Term Evolution-Advanced (3GPP LTE-A) Standard in terms of the SUE, downlink throughput, and RSS metrics. All WSDs under test demonstrated sufficient compliance with the regulatory requirement metric. Full article