Search Results (22,346)

Distributed frameworks for statistical estimation and inference have become a critical toolkit for analyzing massive data efficiently. In this paper, we present distributed estimation for high-dimensional quantile regression with ${\ell }_0$ constraint using iterative hard thresholding (IHT). We propose a communication-efficient distributed estimator which is linearly convergent to the true parameter up to the statistical precision of the model, despite the fact that the check loss minimization problem with an ${\ell }_0$ constraint is neither strongly smooth nor convex. The distributed estimator we develop can achieve the same convergence rate as the estimator based on the whole data set under suitable assumptions. In our simulations, we illustrate the convergence of the estimators under different settings and also demonstrate the accuracy of nonzero parameter identification. Full article

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article

We study investors’ information acquisition strategies under arbitrary and discrete sets of information precision and derive conditions for the existence of equilibria. When investors face information choice from general precision sets, despite their homogeneity, the information market can exhibit asymmetric corner equilibria, where some investors acquire low-precision information and others acquire high-precision information. Conversely, in the case of high-precision sets, there is a symmetric and unique interior equilibrium where all informed agents opt for the same precision level. Furthermore, the impact of information technologies on price informativeness is uncertain: an improvement in information quality tends to reduce price informativeness due to more investors’ free ride on prices, whereas a reduction in information costs enhances price informativeness by encouraging more investors to acquire information. Our analysis has implications on the prevailing trend of robo-advising and the herding behavior of analysts. Full article

In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing hyperspectral imaging as indispensable for remote sensing applications. However, the labor-intensive and time-consuming process of labeling hyperspectral images results in limited labeled samples, while challenges like spectral similarity between different objects and spectral variation within the same object further complicate the development of classification algorithms. Therefore, efficiently exploiting the spatial and spectral information in hyperspectral images is crucial for accomplishing the classification task. To address these challenges, this paper presents a multi-scale feature fusion convolutional neural network (MSFF). The network introduces a dual branch spectral and spatial feature extraction module utilizing 3D depthwise separable convolution for joint spectral and spatial feature extraction, further refined by an attention-based-on-central-pixels (ACP) mechanism. Additionally, a spectral–spatial joint attention module (SSJA) is designed to interactively explore latent dependency between spectral and spatial information through the use of multilayer perceptron and global pooling operations. Finally, a feature fusion module (FF) and an adaptive multi-scale feature extraction module (AMSFE) are incorporated to enable adaptive feature fusion and comprehensive mining of feature information. Experimental results demonstrate that the proposed method performs exceptionally well on the IP, PU, and YRE datasets, delivering superior classification results compared to other methods and underscoring the potential and advantages of MSFF in hyperspectral remote sensing classification. Full article

The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the sustainable use of water resources. Among existing satellite data, the MODIS sensor stands out for its long time series and high temporal resolution, which make it advantageous for large-scale water body monitoring. However, its spatial resolution limitations hinder detailed monitoring. To address this, the present study proposes a dynamic endmember selection method based on phenological features, combined with mixed pixel decomposition techniques, to generate monthly water abundance maps of the Tibetan Plateau from 2000 to 2023. These maps precisely depict the interannual and seasonal variations in surface water, with an average accuracy of 95.3%. Compared to existing data products, the water abundance maps developed in this study provide better detail of surface water, while also benefiting from higher temporal resolution, enabling effective capture of dynamic water information. The dynamic monitoring of surface water on the Tibetan Plateau shows a year-on-year increase in water area, with an increasing fluctuation range. The surface water abundance products presented in this study not only provide more detailed information for the fine characterization of surface water but also offer a new technical approach and scientific basis for timely and accurate monitoring of surface water changes on the Tibetan Plateau. Full article

To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid–continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the Particle Swarm Optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master–slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability. Full article

Underground and submarine power cables are subjected to mechanical stress during installation and operation, which degrades the cable insulation and reduces the reliability of power transmission. Therefore, tests that can evaluate the mechanical properties of power cable insulation are very important. The purpose of this paper is to introduce an improved sampling method for the test sample, the peeling, for mechanical testing of power cable insulation and to evaluate the reliability of the method. The influence of the sampling method of the test sample on the mechanical property values was analyzed. The tensile strength and elongation of XLPE (cross-linked polyethylene) and PP (polypropylene) insulation prepared by the slice method and the peeling method were measured, and the surface of the test samples according to the sampling methods was photographed by SEM. The results show that the mechanical property of the cable insulation increased by more than 10% when the improved peeling method was used, and the precision of the peeling method was relatively better. The SEM analysis showed that the surface of the sliced test sample was rougher than the peeled test sample and was physically damaged. Therefore, the high reliability of the peeling method for mechanical testing of cable insulators was demonstrated. Full article

Cloud detection constitutes a pivotal task in remote sensing preprocessing, yet detecting cloud boundaries and identifying thin clouds under complex scenarios remain formidable challenges. In response to this challenge, we designed a network model, named NFCNet. The network comprises three submodules: the Hybrid Convolutional Attention Module (HCAM), the Spatial Pyramid Fusion Attention (SPFA) module, and the Dual-Stream Convolutional Aggregation (DCA) module. The HCAM extracts multi-scale features to enhance global representation while matching channel importance weights to focus on features that are more critical to the detection task. The SPFA module employs a novel adaptive feature aggregation method that simultaneously compensates for detailed information lost in the downsampling process and reinforces critical information in upsampling to achieve more accurate discrimination between cloud and non-cloud pixels. The DCA module integrates high-level features with low-level features to ensure that the network maintains its sensitivity to detailed information. Experimental results using the HRC_WHU, CHLandsat8, and 95-Cloud datasets demonstrate that the proposed algorithm surpasses existing optimal methods, achieving finer segmentation of cloud boundaries and more precise localization of subtle thin clouds. Full article

This study evaluates the effectiveness of six machine learning models, Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), for predictive maintenance in building systems. Utilizing a high-resolution dataset collected every five minutes from six office rooms at Aalborg University in Denmark over a ten-month period (27 February 2023 to 31 December 2023), we defined rule-based conditions to label historical faults in HVAC, lighting, and occupancy systems, resulting in over 100,000 fault instances. XGBoost outperformed other models, achieving an accuracy of 95%, precision of 93%, recall of 94%, and an F1-score of 0.93, with a computation time of 60 s. The model effectively predicted critical faults such as “Light_On_No_Occupancy” (1149 occurrences) and “Damper_Open_No_Occupancy” (8818 occurrences), demonstrating its potential for real-time fault detection and energy optimization in building management systems. Our findings suggest that implementing XGBoost in predictive maintenance frameworks can significantly enhance fault detection accuracy, reduce energy waste, and improve operational efficiency. Full article

With the rapid advancement of medical imaging technology, medical image segmentation has become increasingly crucial in disease diagnosis, treatment planning, and intraoperative navigation. In this paper, we propose a novel convolutional module, AdaptiveConv2d, that is designed to address the limitations of traditional convolutions and advance convolutional techniques in medical image processing. This approach targets the unique challenges associated with medical images, including complex tissue structures, irregular boundaries, low contrast, and high noise levels. The AdaptiveConv2d module integrates adaptive feature extraction, an optimized receptive field, enhanced sensitivity to details and boundaries, and an innovative feature fusion mechanism to significantly improve segmentation accuracy and robustness. By dynamically adjusting the convolution operations, the module adapts flexibly to medical images with varying shapes and boundaries, leading to more precise feature extraction. Experimental results indicate that the AdaptiveConv2d module outperforms several existing methods across multiple medical image segmentation tasks, highlighting its potential as an effective tool for medical image analysis. Furthermore, the highly modular design of AdaptiveConv2d allows for seamless integration into existing neural network architectures, offering a versatile and adaptable solution for a range of medical image segmentation applications. Full article

Conventional Applicant Tracking Systems (ATSs) encounter considerable constraints in accurately aligning resumes with job descriptions (JD), especially in handling unstructured data and intricate qualifications. We provide Resume2Vec, an innovative method that utilizes transformer-based deep learning models, including encoders (BERT, RoBERTa, and DistilBERT) and decoders (GPT, Gemini, and Llama), to create embeddings for resumes and job descriptions, employing cosine similarity for evaluation. Our methodology integrates quantitative analysis via embedding-based evaluation with qualitative human assessment across several professional fields. Experimental findings indicate that Resume2Vec outperformed conventional ATS systems, achieving enhancements of up to 15.85% in Normalized Discounted Cumulative Gain (nDCG) and 15.94% in Ranked Biased Overlap (RBO) scores, especially within the mechanical engineering and health and fitness domains. Although conventional the ATS exhibited slightly superior nDCG scores in operations management and software testing, Resume2Vec consistently displayed a more robust alignment with human preferences across the majority of domains, as indicated by the RBO metrics. This research demonstrates that Resume2Vec is a powerful and scalable method for matching resumes to job descriptions, effectively overcoming the shortcomings of traditional systems, while preserving a high alignment with human evaluation criteria. The results indicate considerable promise for transformer-based methodologies in enhancing recruiting technology, facilitating more efficient and precise candidate selection procedures. Full article

Unsafe behaviors on construction sites are a major cause of accidents, highlighting the need for effective detection and prevention. Traditional methods like manual inspections and video surveillance often lack real-time performance and comprehensive coverage, making them insufficient for diverse and complex site environments. This paper introduces GeoIoU-SEA-YOLO, an enhanced object detection model integrating the Geometric Intersection over Union (GeoIoU) loss function and Structural-Enhanced Attention (SEA) mechanism to improve accuracy and real-time detection. GeoIoU enhances bounding box regression by considering geometric characteristics, excelling in the detection of small objects, occlusions, and multi-object interactions. SEA combines channel and multi-scale spatial attention, dynamically refining feature map weights to focus on critical features. Experiments show that GeoIoU-SEA-YOLO outperforms YOLOv3, YOLOv5s, YOLOv8s, and SSD, achieving high precision ([email protected] = 0.930), recall, and small object detection in complex scenes, particularly for unsafe behaviors like missing safety helmets, vests, or smoking. Ablation studies confirm the independent and combined contributions of GeoIoU and SEA to performance gains, providing a reliable solution for intelligent safety management on construction sites. Full article

The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future. Full article

(1) Background: Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignancy with high loco-regional recurrence. Advanced imaging biomarkers are critical for stratifying patients at a high risk of recurrence. This study aimed to develop MRI-based radiomic models to predict loco-regional recurrence in OTSCC patients undergoing surgery. (2) Methods: We retrospectively selected 92 patients with OTSCC who underwent MRI, followed by surgery and cervical lymphadenectomy. A total of 31 patients suffered from a loco-regional recurrence. Radiomic features were extracted from preoperative post-contrast high-resolution MRI and integrated with clinical and pathological data to develop predictive models, including radiomic-only and combined radiomic–clinical approaches, trained and validated with stratified data splitting. (3) Results: Textural features, such as those derived from the Gray-Level Size-Zone Matrix, Gray-Level Dependence Matrix, and Gray-Level Run-Length Matrix, showed significant associations with recurrence. The radiomic-only model achieved an accuracy of 0.79 (95% confidence interval: 0.69, 0.87) and 0.74 (95% CI: 0.54, 0.89) in the training and validation set, respectively. Combined radiomic and clinical models, incorporating features like the pathological depth of invasion and lymph node status, provided comparable diagnostic performances. (4) Conclusions: MRI-based radiomic models demonstrated the potential for predicting loco-regional recurrence, highlighting their increasingly important role in advancing precision oncology for OTSCC. Full article

B7 homolog 3 (B7-H3) has emerged as a promising target for cancer therapy due to its high expression in various types of cancer cells. It not only regulates the activity of immune cells but also modulates the signal transduction and metabolism of cancer cells. However, the specific interaction partners of B7-H3 still remain unclear, limiting a comprehensive understanding of the precise role of B7-H3 in cancer progression. In this study, we report that B7-H3 can bind to resting Raji cells, stimulated THP-1 cells, and even PC3 prostate cancer cells through its IgV domain alone. Furthermore, to identify the potential interaction partners of B7-H3 on these cells, we adopted an ascorbate peroxidase 2 (APEX2)-based proximity labeling strategy, which revealed about 10 key potential interaction partners. Interestingly, our results suggest that CD45 could be a putative receptor for B7-H3 on Raji cells, while the epidermal growth factor receptor (EGFR) could closely interact with B7-H3 on PC3 cells. Based on further computational structure modeling studies, we show that B7-H3 can bind to the epidermal growth factor (EGF) binding pocket of EGFR—surprisingly, with a stronger affinity than EGF itself. Overall, our study provides an effective approach to identifying B7-H3 interaction partners in both immune and cancer cell lines. Full article