Search Results (412)

The maternal–fetal interface functions as an integrated physiological unit whose homeostatic balance determines pregnancy success. Large-pore channels, composed of connexins (Cxs), pannexins (Panxs), calcium homeostasis modulators (CALHMs) and leucine-rich repeat-containing 8 (LRRC8) proteins, mediate direct intercellular communication, autocrine and paracrine release of ATP and other signaling molecules, and scaffold-based signal integration across this interface. In this review, we synthesize current knowledge on large-pore channel expression and their physiological and pathophysiological roles at the maternal–fetal interface, applying an explicit evidence-classification framework to distinguish established functions from emerging findings. We first map the large-pore channel repertoire of each cellular compartment—syncytiotrophoblast, cytotrophoblast, extravillous trophoblast, villous endothelium and decidual stroma—revealing that each compartment expresses a distinct combination of family members, with varying levels of evidence and gestational stage-dependent dynamics. We then analyze the three principal modes of large-pore channel operation in placental physiology: gap-junctional communication driving syncytialization, channel-mediated ATP release enabling paracrine purinergic signaling, and channel-independent scaffolding functions that integrate mechanical and biochemical signals. Next, we examine how each channel family becomes dysregulated in placental-related diseases. We conclude by outlining a targeted research roadmap with clear priorities: the most urgent need is protein-level validation of CALHM and LRRC8 expression in primary trophoblasts, followed by elucidation of gating mechanisms and testing for crosstalk among channel families. By providing both conceptual synthesis and practical guidance, this review aims to accelerate mechanistic understanding and therapeutic development targeting large-pore channels for pregnancy complications that currently lack mechanism-based treatments. Full article

We study the extremal behavior of real two-term linear combinations of third-order Zernike modes on the closed unit disk $D^2$. Our primary focus is the classification of interior local extrema; absolute extrema on the boundary circle are treated only where they admit a concise description or as a separate boundary-optimization problem. These modes arise naturally in Zernike expansions of optical wavefront aberrations. For each of the six unordered pairwise linear combinations of third-order modes, we classify the interior local extrema in terms of the two real coefficients. The trefoil–trefoil case is treated more generally through linear combinations of primary n-multifoils; harmonicity and the maximum principle show that no interior local extrema occur and that absolute extrema are attained on the boundary circle. For the remaining pairwise combinations, we give analytic conditions for the existence, uniqueness, location, and values of local extrema, including degenerate exceptional cases. We also compare these local extrema with boundary values and describe the associated absolute-extremum problem on the boundary circle. Symbolic computations are included in the appendix to document several algebraic reductions, and numerical illustrations are provided to visualize the resulting classifications. Full article

The mechanical reliability of high-voltage circuit breakers (HVCBs) is crucial for power-grid stability, yet traditional diagnostic methods rely heavily on manually extracted scalar features that can discard transient information. This paper presents a mechanism-aware diagnostic pipeline that combines synchronized coil-current, contact-travel, and spring-pressure measurements; Variational Mode Decomposition (VMD); kinematics-driven Region of Interest (ROI) alignment; Gramian Angular Field (GAF) encoding; RGB channel stacking; and ResNet-18 classification. On a controlled 220 kV experimental platform covering five operating conditions and 1500 operating-cycle samples, the framework achieved an average accuracy of 96.18% (macro-precision 96.10%, recall 96.01%, and F1-score 96.05%) under five repeated stratified 2:1 holdout evaluations. Single-channel controls obtained 88.47% for current, 90.24% for travel, and 85.13% for pressure; removal of VMD and ROI reduced accuracy to 94.72% and 93.46%, respectively. The results should be interpreted as proof-of-concept evidence on controlled simulated faults; validation on temporally separated field data, other breaker types and voltage levels, and naturally imbalanced fault distributions remains necessary before broad condition-based-maintenance deployment. Full article

Accurate discrimination among static eccentricity fault (SEFs), dynamic eccentricity fault (DEFs), and mixed eccentricity fault (MEFs) in permanent-magnet-synchronous motors remains difficult because these conditions produce similar stator-current patterns. This paper proposes an STFT–CNN–LSTM-based Eccentricity Fault Diagnosis System (STFT-CL-EFDS) for classifying Normal, SEF, DEF, and MEF conditions using only three-phase stator currents. Six signal transformations were initially compared, after which the three leading representations—STFT, DWT, and CWT—were evaluated with seven neural-network architectures. Sensitivity and ablation analyses selected a 500-sample observation window and a compact log-magnitude STFT representation over the nominal 0–1 kHz band. Following full-schedule retraining, the proposed model achieved 99.57% accuracy and a 99.57% weighted F1-score, with recalls of 100.00%, 98.66%, 100.00%, and 99.53% for Normal, SEF, DEF, and MEF, respectively. It exceeded the strongest machine-learning benchmark, STFT–MLP, by 8.42 percentage points in accuracy and 8.52 percentage points in weighted F1-score. On an independent unseen test bench, the proposed model provided the most balanced response across the three fault types and ultimately converged to the correct class in every case, although temporary DEF–MEF confusion remained. These results demonstrate the effectiveness of STFT-CL-EFDS for current-only multiclass PMSM eccentricity diagnosis. The main contributions of this study are as follows: (1) a systematic comparison of six signal-transformation methods, namely Fast Fourier Transform (FFT), STFT, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), and Variational Mode Decomposition (VMD), to determine their suitability for eccentricity fault diagnosis; (2) a comparative evaluation of seven neural-network architectures, including CNN, LSTM, CNN–LSTM, DNN, TCN, ModernTCN, and TimesNet, using the three best-performing transformation methods, namely STFT, CWT, and DWT; and (3) the development of a unified current-only STFT-CL-EFDS that combines STFT-based time–frequency representation with convolutional feature extraction and temporal-sequence learning for the classification of Normal, SEF, DEF, and MEF conditions. Full article

Urban expansion, peri-urban growth, and evolving mobility service systems are reshaping the functional links between urban and rural areas. As these relationships become increasingly complex, conventional urban–rural classifications may underestimate the spatial differences in everyday accessibility, environmental conditions, and demographic exposure. To capture these interrelated dimensions, this study develops a GeoAI-enabled Accessibility–Environment–Equity (AEE) framework to analyse the urban–rural continuum as a multidimensional functional space. Using Odense municipality as a case study, the framework incorporates network accessibility indicators, environmental benefit and pressure proxies, and demographic exposure indicators within a hexagonal tessellation. The indicators are combined into a standardised AEE feature matrix, interpreted through a principal component analysis, and classified using Gaussian mixture modelling with posterior-probability uncertainty mapping. The resulting typology shows a distinct but non-uniform core–periphery gradient: the central areas exhibit high accessibility and population density but lower green space availability and higher environmental pressure proxies, while the peripheral areas are greener but less accessible by non-car modes. The benchmarking against single-domain reference classifications reveals that the functional typology is primarily shaped by accessibility and demographic density structures rather than by greenness alone. This study advances urban–rural continuum (URC) research by demonstrating how GeoAI-enabled workflows can identify functional transition zones and accessibility–environment mismatches, supporting more evidence-based planning for medium-sized European cities and their surrounding urban–rural transition zones. Full article

Whether hydrogen should be delivered by pipeline or by truck is a large and largely irreversible investment decision. The cost information needed for this decision varies by a factor of several across the literature, and conventional single-value comparisons can reverse their conclusions depending on which value is selected. This study introduces a risk classification framework from construction management to define cost uncertainty as probability distributions grounded in the literature and in Korean empirical records. Four delivery modes were compared, specifically new pipelines, repurposed pipelines, high-pressure tube trailers, and liquefied hydrogen tanker trucks. Their twenty-year total costs were computed by Monte Carlo simulation with 20,000 draws for each combination of transport distance (10–500 km) and annual demand (1000–1,000,000 t/yr). The results are summarized as robustness maps that display only the mode with the lowest cost in at least 80% of the 20,000 cost scenarios. The synthesized cost distributions are consistent with the Korean cost records available for comparison, one of which was reserved from the model inputs. Where a repurposable pipeline exists, repurposing is effectively the only robust choice. In new corridors, new pipelines become robustly justified from 19,191 t/yr at the reference distance of 150 km, below which lies a gray band where assumptions decide the outcome. The viability of liquefied hydrogen delivery is determined not by technology but by who bears the liquefaction plant cost, and this boundary alone raises the share of the map on which liquefied hydrogen delivery is robust from 1% to 72.4%. The effective levers that advance the transition to pipelines are the operation and maintenance (O&M) rate (45.8%), the planning horizon (38.3%), and demand commitment (23.2%) rather than the carbon price (2.2%). Four announced Korean plans are located on the map in positions consistent with their project stages, which indicates that the framework can serve as a planning-stage diagnostic tool. Full article

With the increasing application of inverter-fed permanent magnet synchronous, motors (PMSMs) in industrial and intelligent energy systems, reliable fault detection and diagnosis (FDD) has become increasingly important for ensuring operational safety and system reliability. However, conventional single-sensor-based approaches usually exhibit limited robustness under varying operating conditions due to measurement noise, load fluctuations, and incomplete fault information. Therefore, multi-sensor information fusion has attracted increasing attention in PMSM fault diagnosis because it can provide complementary information from different sensing sources. This paper proposes a hybrid deep learning framework for multi-class PMSM fault diagnosis, integrating successive variational mode decomposition (SVMD)-based signal denoising, parallel temporal and spatial feature extraction using temporal convolutional network (TCN) and convolutional neural network (CNN), and BiLSTM with attention-based feature enhancement. First, SVMD is employed to adaptively decompose multi-sensor signals, and components with low Pearson correlation coefficients are removed as noise-dominated components. The remaining components are reconstructed to obtain denoised signals with improved quality. Subsequently, parallel TCN and CNN branches are constructed to extract temporal and spatial features, respectively, enabling comprehensive representation of spatiotemporal characteristics from multi-channel signals. Finally, a BiLSTM combined with an attention mechanism is utilized to model long-term dependencies and emphasize discriminative features for accurate fault classification. The proposed method is evaluated on a public PMSM dataset containing eight sensor channels and nine operating states. Experimental results demonstrate that the proposed framework achieves an accuracy of 98.5%, outperforming several existing representative models. Full article

Purpose: To examine the association between diaphragmatic thickness and upper arm muscle area (AMA), as a surrogate of nutritional status, in children and adolescents with cerebral palsy (CP) stratified by gross motor function severity. Methods: In this cross-sectional study, participants were stratified into ambulatory (Gross Motor Function Classification System [GMFCS] I–III) and non-ambulatory (GMFCS IV–V) groups. AMA was estimated from anthropometric measurements, and diaphragmatic thickness was assessed by B- and M-mode ultrasonography; between-group comparisons used the Mann–Whitney U test and associations were examined using Spearman’s rank correlation coefficient (ρ). Results: Fifty children aged 2–12 years with a confirmed CP diagnosis were evaluated (14 ambulatory, 36 non-ambulatory). In the non-ambulatory group, diaphragmatic thickness showed significant positive correlations with AMA during inspiration (ρ = 0.50; p = 0.002) and expiration (ρ = 0.67; p < 0.001). These subgroup-specific associations should be interpreted as exploratory, as the AMA × GMFCS interaction did not reach statistical significance. No significant correlations were observed in ambulatory participants. Moderate muscle mass deficits were prevalent among non-ambulatory children, and approximately 63% of the total sample presented adipose tissue deficit or risk thereof. Conclusions: In children with severe CP, a significant association between peripheral muscle reserve and diaphragmatic thickness suggests a possible functional link between nutritional status and respiratory muscle morphology. These preliminary findings support further investigation of diaphragmatic ultrasonography as a non-invasive tool for combined nutritional and respiratory monitoring in pediatric neurorehabilitation. Full article

To overcome the nonlinear and non-stationary characteristics of rolling bearing vibration signals and the challenge of extracting incipient weak fault features, this paper proposes a joint fault diagnosis method based on Variational Mode Decomposition (VMD), Maximum Correlated Kurtosis Deconvolution (MCKD) and Support Vector Machine (SVM). Different from most existing studies that separately optimize individual stages of the fault diagnosis workflow, the proposed method adopts a multi-strategy enhanced grey wolf algorithm (S-LE-EGWO) to collaboratively tune parameters for multiple key modules within a unified framework. Firstly, taking the minimum envelope entropy as the fitness function, the S-LE-EGWO algorithm is utilized to optimize the mode number $K$ and penalty factor $\alpha$ of VMD to realize adaptive decomposition of vibration signals. Secondly, kurtosis combined with the correlation coefficient is adopted to select effective. Intrinsic Mode Function (IMF), and the signal is reconstructed based on the screened components. Then, the S-LE-EGWO algorithm is employed to optimize the parameters of MCKD to realize effective extraction of periodic fault impulses. Finally, multi-dimensional fault features are extracted, dimension-reduced by Kernel Principal Component Analysis (KPCA), and fed into the optimized SVM classifier to complete fault identification. Feature-oriented mechanism analysis is carried out using simulation signals, and the proposed method is validated on the CWRU rolling-bearing dataset, with comparative investigations against four mainstream optimization-based diagnostic algorithms. The test results show that the proposed method can effectively mine weak fault features of bearings. Compared with other algorithms, the presented method achieves superior identification performance and possesses favorable recognition capability for incipient weak faults, which can realize the classification of bearing faults. Full article

Three-phase induction motors (TIM) are widely used in industrial applications, and in-ter-turn short-circuit faults represent one of the most critical incipient failure modes. This paper proposes a lightweight and interpretable vibration-based fault diagnosis framework that combines frequency-domain feature extraction with a multilayer perceptron (MLP) classifier. Vibration signals acquired by MEMS (Micro-Electro-Mechanical Systems) accelerometers were segmented and processed using the Fast Fourier Transform (FFT). From the resulting spectra, a compact set of statistical features—including energy, spectral centroid, bandwidth, kurtosis, and skewness—was extracted and used as input to the MLP. The method was evaluated under healthy conditions and multiple inter-turn short-circuit fault scenarios, considering different affected phases and severity levels. Experimental results demonstrate competitive classification performance with significantly reduced computational cost compared to deep learning approaches. Full article

Despite the growing use of FLNG in offshore gas development, LNG hose safety during tandem offloading remains a critical challenge. Existing studies often analyze mooring dynamics and hose mechanics separately, lacking a unified framework that integrates multiple failure modes. This fragmented approach leads to unclear safety boundaries and inadequate risk control. Therefore, this study proposes a multi-parameter safe operating zone threshold method based on coupled dynamic analysis. First, a three-dimensional time-domain dynamic analysis model is developed using OrcaFlex, which integrates the floating bodies, hoses, and mooring system into a unified coupling framework based on hydrodynamic theory, simulating the dynamic response of the offloading system under combined wind, wave, and current actions. Second, tension, bending moment, and curvature are selected as safety evaluation parameters. These three parameters correspond to the core criteria of typical failure modes, namely axial overload failure, ultimate bending failure, and local joint failure, respectively. By comparing them with their allowable values, the safety status of the hose under various operating conditions is determined. Finally, a coupled safety threshold analysis method incorporating both “sea state return period” and “operational vessel distance” is proposed. The results indicate that, at a fixed vessel distance, the dynamic response of the hose increases significantly with worsening sea states. Tension satisfies the safety factor requirements under most sea conditions. However, the bending moment first exceeds the limit starting from the 5-year return period, making it the primary failure control indicator. Curvature exceeds the limit notably under the 50-year return period and beyond, becoming the main risk source under extreme sea states. The safe operational vessel distances under different sea states are also calculated, systematically revealing the response patterns and failure sequences of tension, curvature, and bending moment of the LNG hose under combined wind, wave, and current actions. Furthermore, by integrating safety margin calculations, an operational classification standard comprising a safe zone, a warning zone, and a danger zone is proposed, along with the upper limits of safe vessel distance and operational windows for each sea state. The threshold determination method established in this paper can provide effective engineering support for FLNG offloading operation planning, hose selection, and operational risk management. Full article

In recent years, advances in exoskeleton technology have increased the demand for human motion classification in terms of both movement diversity and recognition accuracy, making the effective classification of more complex asymmetric movements increasingly important. This study integrated surface electromyography (sEMG) and inertial measurement unit (IMU) signals and employed mutual information (MI) and muscle symmetry features (MSF) to analyze the characteristic differences between symmetrical muscles in both legs during asymmetric movements, with the aim of improving the accuracy of asymmetric motion classification. sEMG and IMU signals were collected from six movements, including three asymmetric postures: asymmetrical stance, single-knee kneeled position, and crouching advance. The acquired signals were processed through energy envelope analysis, active segment extraction, empirical mode decomposition (EMD), feature extraction, MI extraction, and MSF extraction. The relevance based on weight feature selection (RWFS) combined with conditional mutual information (CMI) method was then applied to reduce feature dimensionality, prioritize features with significant fluctuations, and preserve key characteristics. Finally, the CNN-LSTM-Attention algorithm was used for classification. Experimental results showed that fusing sEMG and IMU signals achieved 96.30% accuracy in lower limb motion recognition. The proposed method improves asymmetric movement classification and may provide a potential basis for exoskeleton motion classification in special environments. Full article

The application of Machine Learning (ML) to bridge Structural Health Monitoring (SHM) is constrained by a lack of diverse and labelled datasets. Obtaining high-quality training data from operational infrastructure is inherently difficult because critical assets are typically repaired immediately upon the detection of defects, preventing the collection of data describing diverse failure modes. To address this scarcity and enable the prototyping of robust algorithms, this study presents a framework for generating synthetic modal frequencies using a calibrated Finite Element (FE) model of the S101 bridge. Aleatory uncertainties and environmental variability are incorporated through the stochastic variation of material properties and thermal loads derived from a 20-year climate record. Analysis of the generated dataset revealed that simulated thermal loads induced frequency shifts that often exceeded those caused by minor structural damage, confirming the necessity of training on environmentally representative data. The primary contribution of this work is an open-access, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) synthetic dataset, intended to serve as a standardised benchmark for the SHM research community under conditions of combined structural and environmental uncertainty. To demonstrate the utility of the generated data, the performance of a supervised multi-layer perceptron and an unsupervised k-means clustering algorithm are evaluated, with the supervised approach achieving a maximum classification accuracy of 1.00. However, the framework also reveals a fundamental modelling limitation: the linear FE approach failed to replicate the physical response under pier settlement, producing frequency shifts an order of magnitude below those observed experimentally. Full article

Background: The risk of violence is elevated in patients with Schizophrenia Spectrum Disorders (SSD). However, current violence risk assessment approaches rely predominantly on clinical and historical factors and remain limited in predictive accuracy, highlighting the need for more objective complementary markers. Methods: The present study examined the neurobiological differences between violent (n = 66) and non-violent (n = 166) SSD patients, and compared the predictive utility of these neurobiological markers and clinical risk factors, including childhood trauma and psychopathology, in distinguishing violent from non-violent individuals. We further aimed to construct a multimodal predictive model using the strongest predictors from both domains, evaluating whether combining neural and clinical variables improves predictive performance beyond either single domain. Structural and resting-state MRI scans were acquired for patients and healthy controls (n = 504), who were used to calibrate the normative model. Neuroimaging data were analyzed within a normative modelling framework, and univariate associations between neurobiological and clinical factors and violence were examined. Results: A multimodal model integrating neurobiological and clinical measures achieved moderate classification performance (74.7% balanced accuracy), outperforming all individual predictors. The strongest predictors included the medial orbitofrontal cortex, caudal middle frontal gyrus, limbic-default mode network functional connectivity, and childhood sexual abuse. Conclusions: Our findings suggest that alterations in fronto-limbic systems involved in goal-directed decision-making and emotion regulation may contribute to violent behaviour in SSD. Furthermore, the improved performance of the multimodal model supports the potential added utility of integrating neuroimaging markers with established clinical risk factors in violence risk assessment. Full article

In-flight wingtip docking can connect multiple UAVs into a high-aspect-ratio chained-wing configuration, offering potential improvements in aerodynamic efficiency, endurance, and cruise performance. Reliable close-range 6-DoF relative pose estimation is essential for precise docking; however, existing studies have focused primarily on aerodynamic characteristics, docking mechanisms, and guidance and control, while robust monocular pose estimation under partial occlusion and image degradation remains insufficiently investigated. To address this gap, a cooperative-target-based monocular vision method is proposed for six-degree-of-freedom relative pose estimation during in-flight wingtip docking in a chained-wing UAV system. An asymmetric seven-ring planar cooperative target is designed to reduce feature-identification ambiguity and retain sufficient geometric constraints under partial occlusion. The front end combines YOLOv8n-seg instance segmentation with local inner–outer ring refinement and topology-based feature classification. According to target visibility, the back end adaptively selects seven-, five-, or four-point pose-estimation modes, refines valid solutions by minimizing reprojection error, and removes isolated suspicious candidates when necessary. The method is evaluated on a controlled indoor hardware-in-the-loop platform as a pre-flight assessment of its incremental measurement performance. In controlled incremental-response experiments, the mean absolute adjacent-increment errors do not exceed $0.08\mathrm{mm}$ in translation and $0.13°$ in rotation. The complete method achieves a pose-solving success rate of 99.07%. Experiments involving progressive wing occlusion and synthetic directional motion blur further demonstrate that the method can provide stable and continuous relative pose output under challenging observation conditions. Full article