This work proposes a simple method for detecting incipient short circuits in a seven-phase electrical machine based on an analytical model of the phase currents operated in speed-control conditions. The method assumes that an electrical fault will affect the amplitude, phase shift, and mean value of the phase currents (fundamental components and harmonics). The proposal is validated with experimental data collected from a seven-phase permanent magnet synchronous machine (PMSM) in speed control under different fault scenarios: interturn or interphase short-circuit (ITSC, IPSC) faults at low speeds (50 and 100 r/min) for a fault severity varying from 3% to 19%. The results show that the analytical model of the phase currents using the change in parameters (amplitude and phase shift) is relevant and accurate, with an average normalized root-mean-square error (NRMSE) lower than 0.08 and 0.14 for ITSC and IPSC detection, respectively.

Accurate assessment of fish feeding intensity is significant for the timely understanding of feeding demands, dynamically adjusting feeding strategies, and reducing aquaculture costs. However, existing methods often rely on superficial visual features that fail to capture subtle satiety dynamics, resulting in limited reliability. To address the issue, a method for fish feeding intensity assessment based on spatial features and TabNet model with Dynamic Feature Weighting Layer (TabNet-DFWL) is proposed in this study. Fish body contours are extracted from lateral-view images through a pipeline of segmentation, enhancement, and binarization. Subsequently, spatial features highly correlated with fish feeding mechanisms are proposed to characterize behavioral changes. Based on these, an interpretable model integrating spatial features and TabNet-DFWL is constructed to achieve precise fish feeding intensity assessment. This method explores spatial features related to feeding behavior from the underlying mechanism of fish behavioral changes and establishes a feeding intensity assessment model based on TabNet-DFWL. By doing so, it avoids the black-box risk commonly associated with traditional deep learning models and significantly improves model interpretability and reliability, thereby providing a trustworthy basis for precision feeding in aquaculture. Experiments conducted on a real-world fish feeding dataset demonstrate that the proposed method achieves an accuracy of 95.96%, an average precision of 93.44%, an average recall of 93.33%, an average specificity of 98.15%, and an average F1-score of 93.38%. Compared with comparative algorithms, all evaluation metrics exhibit improvements. These results indicate that the proposed method enables accurate assessment of fish feeding intensity and can effectively support the dynamic adjustment of feeding strategies in aquaculture systems.

Recently, the randomized sparse Kaczmarz method has been accelerated by designing heavy ball momentum adaptively via a minimal-error principle. In this paper, we develop a new adaptive momentum method based on the minimal dual function principle to go beyond the exact measurement restriction of the minimal-error principle. Moreover, by integrating the new adaptive momentum method with the quantile-based sampling, we introduce a general algorithmic framework, called quantile-based randomized sparse Kaczmarz with minimal dual function momentum, which provides a unified approach to exact, noisy, or corrupted linear systems. In addition, we utilize the discrepancy principle and monotone error as stopping rules for the proposed algorithm. Theoretically, we establish linear convergence in expectation of Bregman distance up to a finite horizon related to the contaminated level. At last, we provide numerical illustrations on simulated and real-world data to demonstrate the effectiveness of our proposed method.

Large-scale linear systems of the form Ax=b are often doubly-noisy, in the sense that both its measurement matrix A and measurement vector b are noisy. In this paper, we extend the relaxed greedy randomized Kaczmarz (RGRK) method to the doubly-noisy systems to accelerate convergence. However, RGRK fails to converge to the least-squares solution for doubly-noisy systems. To address this limitation, we propose a simple modification: averaging multiple measurements instead of using a single measurement. The proposed RGRK with signal averaging (RGRK-SA) converges to the solution of doubly-noisy systems at a polynomial rate. Numerical experiments demonstrate that both RGRK and RGRK-SA outperform the classical randomized Kaczmarz method, and RGRK-SA has a higher accuracy.

Stochastic projection algorithms for solving convex feasibility problems (CFPs) have attracted considerable attention due to their broad applicability. In this paper, we propose a unified stochastic bilevel reformulation for possibly inconsistent CFPs that combines proximity function minimization and structural regularization, leading to a feasible bilevel model with a unique and stable regularized solution. From the algorithmic perspective, we develop the stochastic block Bregman projection method with Polyak-like and projective stepsizes, which not only subsumes several recent stochastic projection algorithms but also induces new schemes tailored to specific problems. Moreover, we establish ergodic sublinear convergence rates for the expected inner function, as well as linear convergence in expectation to the inner minimizer set under a Bregman distance growth condition. In particular, the proposed Polyak-like stepsize ensures exact convergence in expectation for possibly inconsistent CFPs. Finally, numerical experiments demonstrate the effectiveness of the proposed method and its robustness to noise.

Due to their structural resilience, electrical machines with more than three phases are increasingly used in high-power applications (transportation and energy production). However, monitoring them to increase reliability and improve system availability and safety is essential. This work exploits the properties in the frequency domain of electrical currents flowing in 7-phase electrical machines for fault type diagnosis and faulty phase isolation, especially in the case of incipient faults in the latter. These properties are derived from projecting the phase currents in the stationary frames. The fault features are the amplitudes of the fundamental component of the transformed currents (1 F). Several distances are used for fault clustering in the t-distributed Stochastic Neighbour Embedding (t-SNE) framework. The clustering results are provided through graphical visualization and two metrics, the Silhouette Score (SS) and Davies-Bouldin Index (DBI), measuring intraclass and interclass distances. The results demonstrate that the proposed features effectively identify the fault type and faulty phase, even when the same incipient fault type affects different phases. Additionally, the Mahalanobis distance performs well in fault type isolation, and the various distance metrics exhibit consistently high performance in faulty phase isolation.

Recently, preconditioned primal-dual methods with projection (PPP) have gained popularity for solving inclusion problems. However, existing algorithms often lack concrete guidelines for selecting general nonlinear preconditioners and provide limited numerical validation. To address these issues, we propose a nonlinear preconditioned primal-dual with projection (NL-PPP) method. This framework offers explicit strategies for designing strongly monotone, nonlinear, and potentially nonsymmetric preconditioners; it is applicable to a broader class of nonmonotone inclusion problems characterized by the weak MVI (Minty Variational Inequality) condition. Moreover, NL-PPP not only encompasses existing preconditioners but also integrates previous PPP algorithms with nonlinear preconditioners into a unified framework. The convergence analysis of NL-PPP is established by leveraging the projective correction underlying the separate and project principle. Numerical experiments on several minimax problems demonstrate the effectiveness and flexibility of the proposed NL-PPP.

This study introduces an optimized variational mode decomposition-Transformer framework (OVMD-Transformer) for accurate remaining useful life (RUL) prediction of lithium-ion batteries (LiBs). First, key variational mode decomposition (VMD) hyperparameters, notably the number of intrinsic mode functions (IMFs), are automatically tuned via the particle swarm optimization (PSO) objective function, removing the need for manual parameter selection. The resulting IMFs, which isolate high-frequency capacity fluctuations and low-frequency aging trends, are independently fed into a dedicated Transformer network model to capture multiscale degradation dynamics. The proposed model is rigorously validated on National Aeronautics and Space Administration (NASA) (B0005, B0006, B007, B0018) and Center for Advanced Life Cycle Engineering (CALCE) (C35-C38) datasets under multiple training-testing splits (50%/50% and 30%/70% for NASA; 50%/50% and 20%/80% for CALCE). On the NASA data, the proposed model achieves perfect RUL alignment (the absolute error rate (AER) is 0%) while maintaining $\mathrm {R}<^>{2} \gt 0.95$ . The CALCE dataset similarly sustains $\mathrm {R}<^>{2} \gt 0.99$ with all prediction relative errors lower than 3%. Moreover, the framework excels in early stage forecasting, delivering reliable RUL estimates from just the first 30% (NASA) or 20% (CALCE) of the capacity data. These results demonstrate that OVMD-Transformer significantly outperforms other approaches' accuracy and robustness.

Accurate fish counting in pond aquaculture provides crucial scientific guidance for dynamic water quality regulation, disease early warning, precise feeding, and ecological benefit assessment. To address the challenges posed by multi-scale variation and dense occlusion in complex pond environments, a Multi-scale Adaptive Fusion Attention YOLO v11n (MAFA-YOLO v11n) model for robust fish detection and counting was proposed in this paper. First, a comprehensive dataset was constructed by collecting and preprocessing fish images from multiple ponds at various angles and time intervals. Secondly, the Convolutional Block Attention Module (CBAM) was introduced into the multi-scale feature fusion path of the YOLOv11 neck to enhance the perception capability for key target regions of fish bodies. Finally, the Fish Adaptive Spatial Feature Fusion Head (FASFFHead) was proposed to perform dynamic multi-level feature fusion expansion. This architecture effectively alleviates the problem of insufficient cross-scale integration by adaptively weighting features from different network layers. In the specific context of pond fish counting, this design ensures the accurate capture of both small-scale distant fish and large-scale near-field individuals while maintaining robust detection performance during high-density overlaps characteristic of feeding periods. In practical pond management, real-time surface fish counting at feeding zones serves as a critical proxy indicator for local aggregation density, as complete surface emergence of all fish during feeding cannot be guaranteed and a total population census of the entire pond is physically constrained. This numerical feedback provides a quantitative basis for density-responsive feeding management, where feed distribution can be adjusted according to the observed clustering of fish at the water surface. Evaluated on a self-built fish school dataset, the proposed MAFA-YOLO v11n model achieves a precision of 87.9

Accurately detecting the number of feeding fish in ponds is crucial for grasping the overall feeding demand of the fish school in time, dynamically adjusting the feeding strategy, and reducing the aquaculture cost. As fish tend to tilt their bodies when feeding, with their mouths open and floating towards the water surface while the rest of their bodies remain in the water, coupled with the pond aquaculture water quality being relatively turbid, the images captured from a single perspective cannot fully represent the entire fish. Therefore, it is difficult to determine the number of feeding fish in ponds by detecting the entire fish body. To address this problem, a method for detecting feeding fish in ponds based on the improved YOLOv8, called FFishNet-YOLOv8 is proposed, in which the open mouth of the fish was detected to identify the fish that are feeding. The pond-feeding fish images are obtained by the acquisition device and processed. Subsequently, the feeding fish image features are extracted based on the backbone feature extraction network of YOLOv8 and further enhanced by the constructed neck network based on skip connection and adaptively spatial feature fusion (Skip-ASFF). Finally, the feeding fish are predicted through the head structure, and the proposed joint intersection over union (Joint-IoU) is adopted to further identify the feeding fish. The proposed method addresses the issue of accurately identifying incomplete and unclear fish targets under turbid water conditions in ponds by detecting their open mouth. This enables the identification of the feeding fish target, which is crucial for the accurate acquisition of the overall feeding intensity of the fish school. The FFishNet-YOLOv8 method was tested on the real pond-feeding fish images, with a mean average precision (mAP) of 95.59%, precision of 74.53%, recall of 99.10%, and F1-Score of 85.08%. These evaluation metric values are better than the comparison methods, indicating that the proposed method can accurately detect feeding fish in ponds and provide support for dynamic adjustment of feeding strategies.

Depression is a prevalent affective psychiatric disorder projected to be the leading contributor to the world’s disease burden by 2030. Due to its high prevalence and low recognition rate, an objective and effective detection method is urgently needed. Deep learning methods based on electroencephalography (EEG) have shown significant potential in depression detection. However, excessive channels can increase redundancy and computational complexity in EEG, while irrelevant channels may reduce accuracy. Additionally, existing models often overlook the complementarity between the temporal-, spatial-, and frequency-domain features of EEG, limiting their detection capabilities. To address these issues, we propose a method that fuses the temporal, spatial, and frequency domain features of EEG to enhance the detection accuracy while eliminating redundant channels. We introduce an EEG channel selection method based on frequency domain weighting that automatically adjusts the channel weights to select the EEG channels that best capture spatial information across the delta, theta, alpha, beta, and gamma bands, thereby optimizing the extraction of spatial-frequency features. In addition, we designed a multiscale spatiotemporal convolutional attention network to extract the spatiotemporal features of EEG. In this network, the multiscale convolutional attention module enhanced the model’s ability to capture spatial features, whereas the temporal trend-aware self-attention module extracted long-term temporal features by analyzing global correlations across different time points. Experimental results on the MODMA dataset show that our method achieved a 97.24% detection accuracy, surpassing current state-of-the-art models. This study offers a novel approach for constructing depression detection models, providing a foundation for future research and application.

Accurately estimating fish mass is crucial for evaluating fish growth status, enabling precise feeding, and improving aquaculture efficiency. In current aquaculture, invasive measurements may cause certain damage to fish and affect their healthy growth, non-invasive measurements mostly use stereo cameras to capture fish images and extract two-dimensional and three-dimensional features for mass estimation. The calculation of three-dimensional fish features relies on the acquisition of accurate disparity maps. Most current disparity maps are obtained through manual tuning of algorithmic parameters, which not only increases labor and time costs but also introduces a degree of randomness. To address the above problems, a fish mass estimation method based on Adaptive Parameter Tuning-Disparity Map Restoration (APT-DMR) and Multiple Linear Regression (MLR) under binocular vision technology is proposed. Firstly, fish images are obtained using a binocular camera, followed by camera calibration and image correction. Secondly, image processing technologies are used to segment the corrected image to obtain the fish target, and the two-dimensional features of the fish target are extracted. On this basis, the method based on APT-DMR is adopted to obtain the fish disparity map, extract the corresponding key matching points of the left and right images of the fish, and calculate the coordinates of the three-dimensional spatial feature points using the triangular transformation principle, achieving the extraction of the three-dimensional features of the fish target. Finally, fish mass is predicted using the MLR method. Based on the binocular vision technology, the APT-DMR method is employed to obtain the fish disparity map, which realizes the extraction and calculation of the three-dimensional features of the fish. The proposed method effectively addresses the problem that the traditional algorithm needs to constantly tune the parameters to obtain an accurate disparity map. Additionally, a new feature, the fish depth ratio, is introduced to enrich the model representation, and finally, the fish mass is successfully predicted. In addition to saving time and labor costs, the proposed method also effectively eliminates the stress and potential damage to the fish caused by invasive mass measurements. The crucian carp were taken as the experimental object and the proposed method was tested on the real dataset. The results show that the mean absolute error (MAE) is 0.0061, the root mean square error (RMSE) is 0.0084, and the coefficient of determination (R2) is 0.9338. Compared with Polynomial Regression (PR)-Weight, Decision Tree Regression (DTR)-Weight, Random Forest Regression (RFR)-Weight, Back-Propagation Neural Network (BPNN)-Weight, and Support Vector Regression (SVR)-Weight mass estimation methods, the performance of each evaluation metric of the proposed method has been greatly improved, predicting the fish mass more accurately.

The effects of particulate matter (PMs) from different cities on the nervous system remain unclear. In this study, aqueous solutions of 0.45 μm membrane-filtered PM from 31 major Chinese cities were intravenously administered to rats. Neurotoxicity and mechanisms were investigated by quantifying rat behavior, analyzing in vivo biomarkers, and examining the PM physicochemical properties. PMs from different cities had variable impacts on rat responses, as manifested by the movement speed of the right ear, particularly at 1 h and 4-6 days postexposure. Physiological mechanisms were linked to the regulation of glucocorticoids via the hypothalamic-pituitary-adrenal axis and miR-107/miR-124 expression in the blood. Additionally, PM toxicity was strongly influenced by particle morphology, size, and zeta potential, which varied greatly across cities. Using random forest and multiple linear regression, we revealed that PM particle sizes (458.67 and 531.17 nm) and PM zeta potentials (-3.78, -17.01, and -20.31 mV) had the most important impacts on rat responsiveness, which was in line with blood biomarkers levels in rats such as Glucocorticoid, IL-1β, and IFN-α. These findings indicate that PMs from 31 cities contribute to varying neurotoxicity, thus presenting a possible differential burden on Alzheimer's disease in the aging population across many different regions.

This paper proposes a method to estimate incipient Inter-Turn Short-Circuit (ITSC) faults in seven-phase Permanent Magnet Synchronous Machines (PMSM). The method is based on the phase currents modeled in the natural and the (alpha-beta) stationary reference frames. This model establishes the relationships between the effects of the faults on the fundamental and harmonics of the Clarke-transformed currents and the three key parameters (amplitude, phase shift, and mean value changes). These parameters will be estimated by inverting the model. Distance measures (Jensen-Shannon divergence and Wasserstein distance) are used to assess the estimation’s performance. The method is evaluated with experimental data obtained from a testbed of a seven-phase PMSM in which ITSC faults are generated. The experiments are conducted at low speed (imposed by the load machine) despite challenging conditions due to the low back electromotive force. The accuracy of the model and the estimates are evaluated with the raw measurements and when they are corrupted with an Additional White Gaussian Noise (AWGN). The experimental results validate the analytical model when the severity of the fault varies from 3% to 19%. They also show that the Wasserstein distance is superior to the Jensen-Shannon divergence, even when the measurements are noisy (SNR = 20, 10, 5 dB).

In this paper, we derive a general and exact closed-form expression of scintillation index (SI) for a Gaussian beam propagating through weak oceanic turbulence, based on the general oceanic turbulence optical power spectrum (OTOPS) and the Rytov theory. Our universal expression not only includes existing Rytov variances but also accounts for actual cases where the Kolmogorov microscale is non-zero. The correctness and accuracy of our derivation are verified through comparison with the published work under identical conditions. By utilizing our derived expressions, we analyze the impact of various beam, propagation and oceanic turbulence parameters on both SI and bit error rate (BER) performance of underwater wireless optical communication (UWOC) systems. Numerical results demonstrate that the relationship between the Kolmogorov microscale and SI is nonlinear. Additionally, considering that certain oceanic turbulence parameters are related to depth, we use temperature and salinity data from Argo buoy deployed in real oceans to investigate the dependence of SI on depth. Our findings will contribute to the design and optimization of UWOC systems.

Efforts to reduce particulate matter (PM) mass concentrations often overlook the variability in PM components and associated health effects across cities. Here, we conducted PM toxicity experiments by injecting 228 Wistar rats with water-soluble PM suspensions (filtered through 0.45 mu m membrane) (PMSF) of equal mass (6 mg/kg body weight), collected from automobile air filter in 31 major Chinese cities. Results revealed that PMs from thirty-one sources resulted in statistically significant differences in organ damages (heart, lung and liver), protein biomarkers and four microRNA expressions (miR-21, miR-125b, miR-146a, and miR-155). Nonetheless, these same measurements exhibited a statistical similarity for neighboring cities. Dimensionality reduction and machine learning algorithm revealed a strong link between specific metal components in PMSF and PM-related health risks. For example, reducing 6.20% of metal elements in PMSF was estimated to result in a 23.74% reduction in health risk. Additionally, Polycyclic Aromatic Hydrocarbons (PAHs) levels per unit PM mass was also observed to vary substantially across the 31 cities, thus further explaining the health disparity. Until this work, most studies involve limited number of city PM sources, thus developing a biased understanding of PM toxicity and health impacts for a country or the world. The results here provide a state-of-the-art mechanistic understanding of the health effects of PMs of diverse city sources, while formulating the theoretical basis and reference for city- and component-specific PM control.

Evaluating fish school feeding intensity is essential for optimizing feed efficiency, lowering aquaculture production costs, and ensuring healthy fish development in pond culture. Given that fish schools exhibit diverse feeding morphologies with dynamically shifting aggregation patterns in the pond environment, current models struggle to capture the critical spatial relationships between localized feeding hotspots and global distribution characteristics, leading to compromised accuracy. To tackle this issue, a fish school feeding intensity assessment method based on EfficientNetB0-FPSANet is proposed in this paper. At the data preparation phase, input images first undergo a specialized water surface glare removal processing to eliminate overexposed artifacts caused by water surface reflections, followed by random crops and statistical normalization to optimize visual quality and enhance sample diversity. Secondly, the MBConv layer of EfficientNetB0 is redesigned by removing the Squeeze-Excitation (SE), achieving a lighter architecture without compromising efficacy. Meanwhile, the Fusion Pyramid Squeeze-Excitation Attention (FPSA) is appended at the end of EfficientNetB0, which extracts features at varying scales and effectively preserves the local details and global information. Finally, through rigorous testing on actual fish school image datasets, the developed EfficientNetB0-FPSANet model delivers 98.73

This article proposes a method to estimate incipient interturn short-circuit (ITSC) faults in seven-phase permanent magnet synchronous machines (PMSMs). The method is based on the phase currents modeled in the natural and the (alpha-beta) stationary reference frames. This model establishes the relationships between the effects of the faults on the fundamental and harmonics of the Clarke-transformed currents and the three key parameters (amplitude, phase shift, and mean value changes). These parameters will be estimated by inverting the model. Distance measures [Jensen-Shannon divergence (JSD) and Wasserstein distance (WD)] are used to assess the estimation's performance. The method is evaluated with experimental data obtained from a testbed of a seven-phase PMSM in which ITSC faults are generated. The experiments are conducted at low speed (imposed by the load machine) despite challenging conditions due to the low back-electromotive force. The accuracy of the model and the estimates are evaluated with the raw measurements and when they are corrupted with an additive white Gaussian noise (AWGN). The experimental results validate the analytical model when the severity of the fault varies from 3% to 19%. They also show that the WD is superior to the JSD, even when the measurements are noisy (SNR = 20, 10, 5 dB).

Speech signals are often distorted by reverberation and noise, with a widely distributed signal-to-noise ratio (SNR). To address this, our study develops robust, deep neural network (DNN)-based speech enhancement methods. We reproduce several DNN-based monaural speech enhancement methods and outline a strategy for constructing datasets. This strategy, validated through experimental reproductions, has effectively enhanced the denoising efficiency and robustness of the models. Then, we propose a causal speech enhancement system named Supervised Attention Multi-Scale Temporal Convolutional Network (SA-MSTCN). SA-MSTCN extracts the complex compressed spectrum (CCS) for input encoding and employs complex ratio masking (CRM) for output decoding. The supervised attention module, a lightweight addition to SA-MSTCN, guides feature extraction. Experiment results show that the supervised attention module effectively improves noise reduction performance with a minor increase in computational cost. The multi-scale temporal convolutional network refines the perceptual field and better reconstructs the speech signal. Overall, SA-MSTCN not only achieves state-of-the-art speech quality and intelligibility compared to other methods but also maintains stable denoising performance across various environments.