Search Results (21,982)

The human body is often occluded by a variety of obstacles in the monitoring system, so occluded person re-identification is still a long-standing challenge. Recent methods based on pose guidance or external semantic clues have improved the representation and related performance of features; there are still problems, such as weak model representation and unreliable semantic clues. To solve the above problems, we proposed a feature extraction network, named shared feature fusion with pose-guided and unsupervised semantic segmentation (SFPUS). This network will extract more discriminative features and reduce the occlusion noise on pedestrian matching. Firstly, the multibranch joint feature extraction module (MFE) is used to extract feature sets containing pose information and high-order semantic information. This module not only provides robust extraction capabilities but can also precisely segment occlusion and the body. Secondly, in order to obtain multiscale discriminant features, the multiscale correlation feature matching fusion module (MCF) is used to match the two feature sets, and the Pose–Semantic Fusion Loss is designed to calculate the similarity of the feature sets between different modes and fuse them into a feature set. Thirdly, to solve the problem of image occlusion, we use unsupervised cascade clustering to better prevent occlusion interference. Finally, performances of the proposed method and various existing methods are compared on the Occluded-Duke, Occluded-ReID, Market-1501 and Duke-MTMC datasets. The accuracy of Rank-1 reached 65.7%, 80.8%, 94.8% and 89.6%, respectively, and the mAP accuracy reached 58.8%, 72.5%, 91.8% and 80.1%. The experiment results demonstrate that our proposed SFPUS holds promising prospects and performs admirably compared with state-of-the-art methods. Full article

As new kinds of aggregation functions, overlap functions and semi overlap functions are widely applied to information fusion, approximation reasoning, data classification, decision science, etc. This paper extends the semi-overlap function on [0, 1] to the function on complete lattices and investigates the residual implication derived from it; then it explores the construction of a semi-overlap function on complete lattices and some fundamental properties. Especially, this paper introduces a more generalized concept of the ‘semi-Θ-Ξ function’, which innovatively unifies the semi-overlap function and semi-grouping function. Additionally, it provides methods for constructing and characterizing the semi-Θ-Ξ function. Furthermore, this paper characterizes the semi-overlap function on complete lattices and the semi-Θ-Ξ function on [0, 1] from an algebraic point of view and proves that the algebraic structures corresponding to the inflationary semi-overlap function, the inflationary semi-Θ-Ξ function, and residual implications derived by each of them are inflationary MTL algebras. This paper further discusses the properties of inflationary MTL algebra and its relationship with non-associative MTL algebra, and it explores the connections between some related algebraic structures. Full article

During aging, disruptions in various signaling pathways become more common. Some older patients will exhibit irregular bone morphogenetic protein (BMP) signaling, which can lead to osteoporosis (OP)—a debilitating bone disease resulting from an imbalance between osteoblasts and osteoclasts. In 2002, the Food and Drug Administration (FDA) approved recombinant human BMP-2 (rhBMP-2) for use in spinal fusion surgeries as it is required for bone formation. However, complications with rhBMP-2 arose and primary osteoblasts from OP patients often fail to respond to BMP-2. Although patient samples are available for study, previous medical histories can impact results. Consequently, the C57BL/6 mouse line serves as a valuable model for studying OP and aging. We find that BMP receptor type Ia (BMPRIa) is upregulated in the bone marrow stromal cells (BMSCs) of 15-month-old mice, consistent with prior data. Furthermore, conjugating BMP-2 with Quantum Dots (QDot^®s) allows effective binding to BMPRIa, creating a fluorescent tag for BMP-2. Furthermore, after treating BMSCs with methyl-β-cyclodextrin (MβCD), a disruptor of cellular endocytosis, BMP signaling is restored in 15-month-old mice, as shown by von Kossa assays. MβCD has the potential to restore BMPRIa function, and the BMP signaling pathway offers a promising avenue for future OP therapies. Full article

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR. Full article

Accurately extracting underwater images has never been more challenging, as the lack of clarity of detail due to issues such as scattering and light absorption is more noticeable than ever before. This research method addresses these problems while clarifying the limitations of existing methods and proposes a comprehensive approach to underwater image processing. Current methods tend to focus only on the effects of individual factors, such as color shifts, visibility, or contrast enhancement, and do not take into account biological vision applications. In contrast, the method proposed in this paper applies a color correction module that takes into account the effects of biological vision in LAB color space, and an enhanced Type-II Fuzzy set visibility enhancement module. This synchronized approach overcomes the limitations of the previous methods in that the contrast enhancement utilizes a curve transform and a multi-scale fusion strategy that preserves the essential image details. The framework not only adjusts the overall image features, but also finely handles the local details, resulting in a significant enhancement of both the overall quality and the local detail clarity of underwater images. The experimental results demonstrate that the application of the method of this study on two datasets gives results that are better than those of the top 10 existing algorithms. By explicitly addressing the limitations of existing methods, the method becomes an advantageous solution in underwater image processing, providing enhancements in image quality and task-specific applications in a concise and efficient manner. Full article

Spatially referenced and geometrically accurate laser scanning is essential for the safety monitoring of an underground mine. However, the spatial inconsistency of point clouds collected by heterogeneous platforms presents challenges in achieving seamless fusion. In our study, the terrestrial and handheld laser scanning (TLS and HLS) point cloud registration method based on the coarse-to-fine strategy is proposed. Firstly, the point features (e.g., target spheres) are extracted from TLS and HLS point clouds to provide the coarse transform parameters. Then, the fine registration algorithm based on identical area extraction and improved 3D normal distribution transform (3D-NDT) is adopted, which achieves the datum unification of the TLS and HLS point cloud. Finally, the roughness is calculated to downsample the fusion point cloud. The proposed method has been successfully tested on two cases (simulated and real coal mine point cloud). Experimental results showed that the registration accuracy of the TLS and HLS point cloud is 4.3 cm for the simulated mine, which demonstrates the method can capture accurate and complete spatial information about underground mines. Full article

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. Full article

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. Full article

Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. Our network consists of blocks containing a full- and sub-band LSTM module for capturing spectral information, and a global–local attention fusion module for refining this information. The full- and sub-band LSTM module integrates both full-band and sub-band information through two LSTM layers, while the global–local attention fusion module learns global and local attention in a dual-branch architecture. To further enhance the feature integration, we fuse the outputs of these branches using a spatial attention module. The model is trained to predict the complex ratio mask (CRM), thereby improving the quality of the enhanced signal. We conducted an ablation study to assess the contribution of each module, with each showing a significant impact on performance. Additionally, our model was trained on the SPA-DNS dataset using a circular microphone array and the Libri-wham dataset with a linear microphone array, achieving competitive results compared to state-of-the-art models. Full article

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with a median overall survival (OS) of 15–18 months despite standard treatments. Approximately 8% of GBM cases exhibit genomic alterations in fibroblast growth factor receptors (FGFRs), particularly FGFR1 and FGFR3. Next-generation sequencing techniques have identified various FGFR3 fusions in GBM. This report presents a novel FGFR3 fusion with fatty acid synthase (FASN) in a 41-year-old male diagnosed with GBM. The patient presented with a persistent headache, and imaging revealed a right frontal lobe lesion. Surgical resection and subsequent histopathology confirmed GBM. Initial NGS analysis showed no mutations in the IDH1, IDH2 or H3F3 genes, but revealed a TERT promoter mutation and CDKN2A/2B and PTEN deletions. Postoperative treatment included radiotherapy and temozolomide. Despite initial management, recurrence occurred four months post-diagnosis, confirmed by MRI and histology. A second surgery identified a novel FGFR3-FASN fusion, alongside increased Ki67 expression. The recurrence was managed with regorafenib and bevacizumab, though complications like hand–foot syndrome and radiation necrosis arose. Despite initial improvement, the patient died 15 months after diagnosis. This case underscores the importance of understanding GBM’s molecular landscape for effective treatment strategies. The novel FGFR3-FASN fusion suggests potential implications for GBM recurrence and lipid metabolism. Further studies are warranted to explore FGFR3-FASN’s role in GBM and its therapeutic targeting. Full article

Protein–RNA interactions are essential to many cellular functions, and missense mutations in RNA-binding proteins can disrupt these interactions, often leading to disease. To address this, we developed PRITrans, a specialized computational method aimed at predicting the effects of missense mutations on protein–RNA interactions, which is vital for understanding disease mechanisms and advancing molecular biology research. PRITrans is a novel deep learning model designed to predict the effects of missense mutations on protein–RNA interactions, which employs a Transformer architecture enhanced with multiscale convolution modules for comprehensive feature extraction. Its primary innovation lies in integrating protein language model embeddings with a deep feature fusion strategy, effectively handling high-dimensional feature representations. By utilizing multi-layer self-attention mechanisms, PRITrans captures nuanced, high-level sequence information, while multiscale convolutions extract features across various depths, thereby enhancing predictive accuracy. Consequently, this architecture enables significant improvements in ΔΔG prediction compared to traditional approaches. We validated PRITrans using three different cross-validation strategies on two newly reconstructed mutation datasets, S315 and S630 (containing 315 forward and 315 reverse mutations). The results consistently demonstrated PRITrans’s strong performance on both datasets. PRITrans demonstrated strong predictive capability, achieving a Pearson correlation coefficient of 0.741 and a root mean square error (RMSE) of 1.168 kcal/mol on the S630 dataset. Moreover, its robust performance extended to independent test sets, achieving a Pearson correlation of 0.699 and an RMSE of 1.592 kcal/mol. These results underscore PRITrans’s potential as a powerful tool for protein-RNA interaction studies. Moreover, when tested against existing prediction methods on an independent dataset, PRITrans showed improved predictive accuracy and robustness. Full article

This paper proposes a new approach to pixel-level fusion using the opposite frequency from the discrete wavelet transform with Gaussian or Difference of Gaussian. The low-frequency from discrete wavelet transform sub-band was fused with the Difference of Gaussian, while the high-frequency sub-bands were fused with Gaussian. The final fusion was reconstructed using an inverse discrete wavelet transform into one enhanced reconstructed image. These enhanced images were utilized to improve recognition performance in the face recognition system. The proposed method was tested against benchmark face datasets such as The Database of Faces (AT&T), the Extended Yale B Face Dataset, the BeautyREC Face Dataset, and the FEI Face Dataset. The results showed that our proposed method was robust and accurate against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with time gap, and conditions where the person uses attributes such as glasses. The proposed method is comparable to state-of-the-art methods and generates high recognition performance (more than 99% accuracy). Full article

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes. Full article

This study explores the formation of δ-ferrite and its self-tempering behavior in the microstructure of Type 410 martensitic stainless steel produced via laser powder bed fusion (L-PBF). The study investigates the correlation between varying energy densities applied during the L-PBF process and the resultant mechanical properties of the as-built specimens. A microstructural analysis shows that with an increase in energy density, the δ-ferrite fraction decreases, while the martensite content increases, leading to changes in tensile strength and elongation. Higher energy densities reduce tensile strength but significantly enhance ductility. The self-tempering effect of martensite in reheated zones, caused by the complex thermal cycling during the L-PBF process, plays a critical role in determining mechanical behavior. These findings provide valuable insights for optimizing the additive manufacturing of martensitic stainless steels to achieve the desired mechanical properties. Full article