Search Results (607)

Background: Amnestic mild cognitive impairment (aMCI) is considered a transitional state between normal aging and dementia, often without visible abnormalities on standard brain magnetic resonance (MR) images. The aim of the study was to analyze both microstructural and functional brain abnormalities using advanced MR techniques. Methods: The study included 27 patients with aMCI and an age-matched control group (CG) of 25 healthy subjects. All MR studies were performed on a 3T MR scanner (Philips, Ingenia) with a 32-channel head and neck coil using volumetric 3D T1 sequences, followed by a resting-state functional MRI (rs-fMRI) sequence. Volumetric analysis was performed using the Destrieux atlas to assess potential structural differences between groups. Seed-to-voxel functional connectivity analyses were conducted using the bilateral hippocampi and both anterior and posterior divisions of the parahippocampal gyri as seed regions. Results: Compared to healthy controls, reduced cortical thickness was observed in aMCI subjects in the temporal regions, frontal and orbitofrontal areas, limbic areas, parietal and sensorimotor cortices, as well as occipito-temporal regions. Additionally, significantly increased functional connectivity was observed between bilateral medial temporal lobe (MTL) regions and the right thalamus. Conclusions: Cortical thinning in various brain regions along with the increased functional connectivity between the MTL regions and the right thalamus may reflect potential compensatory mechanisms in response to initial subtle degenerative changes, emphasizing the importance of using both functional and structural imaging to detect early changes in aMCI patients. Full article

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments. Full article

During a survey of myxozoan diversity in fishes from Hunan Province, two new Myxidium species were discovered infecting the gallbladder of Sarcocheilichthys kiangsiensis Nichols, 1930 and Sarcocheilichthys parvus Nichols, 1930, in Dongting Lake, China. In both cases, myxospores were observed freely floating in the biles, with no typical plasmodia detected. Morphologically, both of them can be differentiated from previously described congeners by a combination of features, including myxospore dimensions, polar capsule shape, number of polar tubule coils and shell valve striations. BLASTn research indicated that neither species matched any available species in GenBank. The highest sequence similarity for Myxidium kiangsiensis n. sp. was 98.54% with M. asiaticum Chen et al., 2020 (PQ776264), and that for Myxidium parvusis n. sp. was 93.06% with Zschokkella guelaguetza Alama-Bermejo et al., 2023 (OQ888223). This study represents the first record of Myxidiidae infection in Sarcocheilichthys hosts. Phylogenetic analysis based on the obtained SSU rDNA sequences placed the two species in separate subclades interspersed with other Myxidium and Zschokkella species. This topology further corroborates the polyphyletic nature of these two genera. Full article

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. In terms of hardware, a novel pickup probe based on the micro-lever principle is developed. By utilizing a pivoted lever structure with an optimized arm ratio of 2.6 to 1 and a full pressure-balanced mechanism, the design physically overcomes the contact dead zone inherent in traditional pressure-compensating probes and effectively isolates low frequency common-mode interference through a lateral floating architecture. In terms of software, a joint denoising model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet thresholding is proposed. A cross-correlation coefficient criterion is introduced to adaptively screen intrinsic mode functions and eliminate residual fluid turbulence noise. Field experiments on a 1500 ft full-scale circulation loop demonstrate that the proposed probe improves the detection sensitivity of the radial breathing mode by approximately 20.6 dB compared to the baseline, while effectively eliminating stick-slip friction noise during dynamic tripping. Furthermore, the joint algorithm increases the Signal to noise Ratio by an additional 16.9 dB under typical pumping conditions of 0.5 bpm, with a normalized cross-correlation exceeding 0.96. These results verify that the proposed method effectively solves the bottleneck of weak signal detection in deep wells, providing robust technical support for CT telemetry operations. Full article

The biological effects of weak low-frequency magnetic fields (LFMFs) remain controversial, particularly regarding frequency-specific resonance-like responses. Many previous studies tested different frequencies sequentially, potentially introducing uncontrolled environmental variability. This study aimed to evaluate frequency-dependent effects of LFMFs on the growth of juvenile Daphnia magna under strictly synchronized and temperature-controlled conditions. Genetically identical neonates from a single parthenogenetic brood were simultaneously exposed to sinusoidal 50 μT magnetic fields at 20, 25, 30, 35, or 40 Hz using spatially separated Helmholtz coils integrated into a closed-loop thermal stabilization system. Body length was measured after 48, 96, and 144 h of exposure. No significant growth differences were detected after 48 h. After 96 h, a significant biological effect was observed only at 30 Hz. The most pronounced responses occurred after 144 h, with significant growth stimulation at 25, 30, and 35 Hz and a maximal effect at 30 Hz. The frequency–response relationship exhibited a dome-shaped pattern that became less sharply peaked with prolonged exposure. These findings demonstrate duration-dependent and frequency-specific stimulation of juvenile daphnid growth with weak LFMFs. It suggests that exposure time critically influences the manifestation and breadth of resonance-like magnetobiological effects. Full article

In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability. Full article

Internal reconnection events (IREs) are rapid magnetohydrodynamic phenomena that play an important role in the confinement and stability of spherical tokamak plasmas. Reliable identification of IREs in experimental data is challenging due to short discharge durations, ambiguous event boundaries, and the limited availability of labeled data. In this study, we propose an unsupervised, event-level IRE detection framework based on anomaly detection techniques and apply it to experimental data from the VEST spherical tokamak. The proposed framework combines a two-stage detection strategy using plasma current and $H_{\alpha }$ emission signals with sliding-window segmentation and event-level evaluation, enabling physically meaningful IRE identification without labeled training data. Three unsupervised models—K-Nearest Neighbors (KNN), One-Class Support Vector Machine (OCSVM), and an autoencoder (AE)—are evaluated within a unified framework. All models achieve stable detection performance, with precision exceeding 80% and recall above 70% under a precision-oriented operating point. To enhance detection robustness, a KNN-based cleaning procedure is introduced during training to remove noise-driven, locally isolated windows, significantly reducing spurious detections while preserving physically meaningful IRE signatures. Event-level analysis indicates that missed detections under this operating regime predominantly correspond to weak events with limited impact on global plasma behavior. The proposed framework is fully unsupervised, computationally efficient, and readily extensible to other spherical tokamak devices, providing a flexible foundation for incorporating additional diagnostics, such as Mirnov coil signals, toward precursor-aware detection and future predictive modeling of IRE activity. Full article

Optically pumped magnetometers (OPMs) provide a non-cryogenic alternative to superconducting quantum interference devices (SQUIDs) for detecting weak biomagnetic fields. We report the design, construction, and characterization of a single-cell intrinsic OPM gradiometer. The gradiometer employs a rubidium-87 vapor cell in an orthogonal pump and probe beam configuration. The pump beam was split to illuminate two parallel sensing regions of the cell, separated by a baseline of 3 cm, with opposing circular polarization. A linearly polarized probe beam propagated through both regions and was captured by a balanced polarimeter whose output directly measured the spatial magnetic gradient. This prototype achieved a common-mode rejection ratio exceeding 50 dB and a sensitivity of 267 pT/cm/√Hz without passive magnetic shielding, using active ambient-field coils. As a proof of concept, we recorded preliminary cardiac-synchronous magnetic measurements using an optical pulse sensor for beat segmentation. After bandpass filtering and ensemble averaging, a cardiac-synchronous waveform was observed, consistent with cardiac timing. Unlike many multi-cell gradiometers that require complex calibration, modulation, and passive shielding, this single-cell design reduces cost and complexity. Full article

Tilting pad journal bearings are critical components in high-speed turbomachinery. The use of sensors within the bearing is crucial to ensure operational safety and to validate computational models. The objective of this study is to improve the experimental investigation of the performance of a tilting pad journal bearing by enhancing the selection and placement of conventional and non-conventional sensors based on the results of a thermohydrodynamic model. The multi-sensor system measures film pressure and pad temperature at multiple locations, as well as pad tilt and film thickness. Redundant measurements are also performed to evaluate the performance of new induction coils capable of detecting magnetic flux variations due to vibrations. This work contributes to the discussion of bearing instrumentation by proposing a synergic sensor system comprising a suitable number of appropriately located conventional sensors together with non-conventional, non-invasive sensors. The experimental results obtained with the refined conventional sensor system agree with the predicted results, with differences that can be attributed to manufacturing and assembly tolerances of the bearing and simplified assumptions in the model. The results of the non-conventional sensor device, although promising, need further investigation. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

Currently, marine and land oil resources have entered the high-water extraction stage. The remaining oil is dispersed, and the oil–water relationship is complex, making it increasingly difficult to extract. However, traditional electrical logging techniques are limited by the shielding effect of highly conductive steel casing, rendering them unsuitable for formation resistivity measurement in casing wells. Time-domain electromagnetic method overcomes the constraints of downhole push-off systems and casing conditions, enabling continuous measurement and acquisition of formation resistivity parameters. To overcome these limitations, this paper proposes an active compensation method based on differential measurements between specially configured coils, enabling the early response of the formation to be identified, the method enhances weak signal detection capabilities in casing formations. The coils offset part of the casing influence, while the casing background serves as baseline information. A time-domain electromagnetic instrument for metal casing resistivity measurement was developed, along with a ground water tank resistivity calibration device. The experimental results show that the instrument can effectively suppress casing response, obtain formation resistivity signals, and provide effective guidance methods for measuring formation resistivity of casing wells in the ocean and land. Full article

Variable reluctance sensors are widely adopted for robust and contactless detection of motion in harsh and space-constrained environments. This paper presents a MEMS-based variable reluctance induction sensor for the noncontact characterization of rotating ferromagnetic targets, based on a micromachined planar micro-coil coupled with an external permanent magnet. The rotation of a ferromagnetic object modulates the magnetic circuit reluctance, generating a voltage signal across the micro-coil that encodes information on the target rotational speed, proximity, and cross-sectional shape. Sensor operation is investigated through a lumped-element magnetic–electrical circuit model and finite-element magnetostatic simulations, quantifying the effects of target diameter, distance, and angular position on the linked magnetic flux. Experimental validation is performed using rotating drill bits as representative targets and a dedicated high-gain, high-input-impedance front-end circuit to amplify the induced voltage. Measured results at fixed rotation frequency show periodic voltage waveforms whose amplitude and shape vary consistently with target geometry, proximity and speed. Reliable detection is achieved for rotational speeds up to 1500 rpm, for drill bit diameters as small as 5 mm, and at sensor-to-target distances up to 8 mm. These results demonstrate the potential of MEMS variable reluctance induction sensors for compact speed sensing and target shape detection. Full article

Urban environments face heightened seismic risks due to dense infrastructure and population concentration. Traditional seismic methods often face significant practical limitations in cities due to space constraints, traffic disruption, and acoustic noise, necessitating reliable alternative geophysical approaches for fault screening. This study evaluates the efficacy and practical utility of the opposing-coils transient electromagnetic method (OCTEM) as an effective alternative to conventional seismic techniques for detecting shallow-fault-like resistivity signatures under complex urban electromagnetic noise. By employing dual coaxial coils with opposing currents, the OCTEM suppresses primary-field interference, enabling high-resolution imaging of subsurface structures at depths of 0–200 m. A case study in Tiancheng Chengyuan, Cangzhou City, China, demonstrates the OCTEM’s capability to reliably delineate stratigraphic interfaces and resistivity anomalies under challenging electromagnetic background conditions. Field data exhibited a mean square relative error of 4.01%, validating its data quality and measurement stability. The survey successfully identified stratigraphic continuity and localized heterogeneity features within the investigation zone. These results establish the OCTEM as a robust and efficient tool for urban fault screening, particularly in environments where traditional high-resolution seismic methods are impractical or economically unfeasible. Full article

The development of autonomous mobile robots or automated guided vehicles is consistently challenged by energy-storage constraints, and while batteries are the standard solution for mobile robots, dynamic wireless power transfer is an alternative way to supply power without reliance on chemical energy storage. For efficient dynamic wireless power transfer, transmitting coils should be energized as required, necessitating real-time position tracking of the receiving coil. Current prevalent techniques require complex modifications to existing systems and additional position sensors, which increase total costs. This article proposes a novel receiving coil position detection method for wireless power transfer systems without using external receiving coil position detection sensors and describes the application of the sensorless coil position detection method and its advantages compared to other methods. The proposed method was implemented on an existing low-power, miniaturized test bench. The described method was successfully validated and correctly switched transmitting coils, ensuring continuous movement of an electric vehicle, therefore proving its viability as a potential new approach for sensorless receiving-coil detection. Experimental results demonstrate that the prototype achieved a maximum power transfer efficiency of 53.8% while maintaining continuous transmitting coil switching operation at vehicle speeds up to 77 cm/s. Full article