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We consider a continuous facility location problem in which our objective is to minimize the weighted sum of three costs: (1) fixed costs from installing the facilities, (2) backbone network costs incurred from connecting the facilities to each other, and (3) transportation costs incurred from providing services from the facilities to the service region. We first analyze the limiting behavior of this model and derive the two asymptotically optimal configurations of facilities: one of these configurations is the well studied honeycomb heuristic , and the other is an Archimedean spiral. We then give a fast constant-factor approximation algorithm for finding the placement of a set of facilities in any convex polygon that minimizes the sum of the three aforementioned costs.

The backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives. Interestingly, ordering the other methods from GloSS to NC, PF, LANS, and MLF, we observe that each method’s output encapsulates the backbone of the previous one. Correlation analysis between edge features (weight, degree, betweenness) and the test significance level reveals that the DF and LANS filters favor high-weighted edges while ECM assigns them lower significance to edges with high degrees. Furthermore, the results suggest a limited influence of the edge betweenness on the filtering process. The backbones global properties analysis (edge fraction, node fraction, weight fraction, weight entropy, reachability, number of components, and transitivity) identifies three typical behavior types for each property. Notably, the LANS filter preserves all nodes and weight entropy. In contrast, DF, PF, ECM, and GloSS significantly reduce network size. The MLF, NC, and ECM filters preserve network connectivity and weight entropy. Distribution analysis highlights the PU filter’s ability to capture the original weight distribution. NC filter closely exhibits a similar capability. NC and MLF filters excel for degree distribution. These insights offer valuable guidance for selecting appropriate backbone extraction methods based on specific properties.

Chemical graph theory is the field which deals with the combination of chemistry and graph theory. In this paper, we find the degree based first and second K Bbanhatti, first and second hyper K Banhatti, first and second multiplicative K banhatti, first and second multiplicative hyper K banhatti, Sombor, KG , modified KG , multiplicative KG , multiplicative modified KG , K Harmonic and multiplicative K Harmonic Banhatti, first Banhatti and reduced Banhatti Sombor, delta Banhatti Sombor indices for backbone DNA and subdivided backbone DNA networks. These topological descriptors are computed by direct method.

Weld defect detection poses significant challenges including ambiguous boundaries, diverse defect shapes, and the requirement for precise localization. To address these issues, we propose DSF-YOLO, a novel framework specifically designed for pipeline weld defect detection. DSF-YOLO introduces three core innovations. The Dynamic Staged Fusion Feature Extraction (DSFFE) module dynamically fuses same-scale features from dual-backbone networks, progressively enhancing the representation of defect features and enabling the model to efficiently capture small-sized defects, blurred boundaries, and complex defect characteristics. The Dual Multi-Scale Feature Fusion (DMFF) module builds on the feature extraction capabilities of DSFFE and employs a dual fusion strategy to effectively aggregate global and local features, enhancing the representation of small targets and improving the separation of blurred boundaries. The decoupled head based on SENetv2-ResNeXt incorporates a multi-channel parallel processing strategy to further strengthen feature representation while inter-channel information interaction and global feature representation significantly improve classification and localization precision. Validated on an X-ray weld defect dataset containing 8 defect types, DSF-YOLO achieved an mAP50:95 of 74.7% surpassing YOLOv8-X by 1.1% and an mAP50 of 99.1% exceeding YOLOv8-X by 0.3%. Additionally, DSF-YOLO significantly reduces computational complexity, achieving a 75% reduction in FLOPs and a 47.5% decrease in parameters compared to YOLOv8-X. These results establish DSF-YOLO as an efficient and accurate solution addressing critical challenges in industrial weld defect detection with significant practical value.

In the context of our research project, which involves developing a robotic system capable of eliminating weeds using deep learning technics, the selection of powerful object detection model is essential. Object detectors typically consist of three components: backbone, neck, and prediction head. In this study, we propose an enhancement to the you only look once version 5 (YOLOv5) network by using the most popular convolutional neural networks (CNN) networks (such as DarkNet and MobileNet) as backbones. The objective of this study is to identify the best backbone that can improve YOLOv5 's performance while preserving its other layers (neck and head). In terms of detecting and ultra-localizing pea crops. Additionally, we compared their results with those of the most commonly used object detectors. Our findings indicate that the fastest models among the networks studied were MobileNet, YOLO-tiny, and YOLOv5, with speeds ranging from 5 to 14 milliseconds per image. Among these models, MobileNetv1 demonstrated the highest accuracy, achieving average precision (AP) score of 89.3% for intersection over union (IoU) threshold of 0.5. However, the accuracy of this model decreased when we increased the threshold, suggesting that it does not provide perfect crop delineation. On the other hand, while YOLOv5 had a lower AP score than MobileNetv1 at an IoU threshold of 0.5, it exhibited greater stability when faced with variations in this threshold.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Semantic segmentation is a kind of dense prediction task, which has high requirements on the prediction accuracy and inference speed in mobile terminals. To reduce the computational burden of the segmentation network and supplement the missing spatial information of high-level features, an efficient feature reuse network (EFRNet) is proposed in two steps: a Multi-scale Bottleneck module is designed to extract multi-scale features, and a lightweight backbone is designed based on the MB module; then, features of different depths are integrated through efficient feature reuse model. Experiments on Cityscapes datasets demonstrate that the proposed EFRNet achieves an impressive balance between speed and precision. Specifically, without any pre-trained model and post-processing, it achieves 75.58% Mean IoU on the Cityscapes test dataset with the speed of 118 FPS on a single RTX 2080Ti GPU.

Scientific authors’ collaborations are influenced by various factors, such as their field, geographic region, and institutional role. Here we focus on a group of authors whose patterns of publications greatly deviate from the average, previously referred as hyperprolific authors . Prior studies have investigated the emergence of hyperprolific authors and their productivity. In this article, we focus on the role of coauthorships in the hyperprolific authors’ publication profiles. Based on a network model that represents researchers as nodes and weighted edges as the number of collaborations between a pair of researchers, we argue that not all network edges have the same importance to characterize the existence of hyperprolific authors. As such, we filter out “sporadic” coauthorships, revealing an underlying structure composed only of edges representing consistent and repetitive collaborations, named as the network backbone . Our network-oriented methodology was applied to a dataset of Computer Science publications extracted from DBLP, covering an 11-year period from 2010 to 2020. Our experiments reveal significant topological differences between the full coauthorship networks and backbones, concerning only authors with very off-the-pattern profiles. We also show that hyperprolific authors are consistently more likely to exhibit off-the-pattern coauthorships and that an author’s probability of being present in the backbone substantially increases with her topological proximity to a hyperprolific author. Finally, we investigate how authors’ hyperprolific profiles correlate to their presence in the backbone.

As a key step to endow the neural network with nonlinear factors, the activation function is crucial to the performance of the network. This paper proposes an Efficient Asymmetric Nonlinear Activation Function (EANAF) for deep neural networks. Compared with existing activation functions, the proposed EANAF requires less computational effort, and it is self-regularized, asymmetric and non-monotonic. These desired characteristics facilitate the outstanding performance of the proposed EANAF. To demonstrate the effectiveness of this function in the field of object detection, the proposed activation function is compared with several state-of-the-art activation functions on the typical backbone networks such as ResNet and DSPDarkNet. The experimental results demonstrate the superior performance of the proposed EANAF.

COVID-19 has triggered 6.42 million death tolls, and more than 586 million confirmed positive cases until 10/Aug/2022. CT-based diagnosis methods need special expert knowledge, and the labeling procedure is tedious. We first propose a 12-layer CNN-based backbone network. Then, we utilize the Swish activation function to replace traditional ReLU. The multiple-way data augmentation is utilized to enhance the training set. Our model is named Swish-based CNN (SCNN). A web app is developed based on the proposed SCNN model. The SCNN model performs better than the ReLU-based backbone network and LReLU-based backbone network, indicating the effectiveness of the Swish function. The SCNN model achieves a sensitivity of 94.50 ± 1.06, a specificity of 95.25 ± 0.59, and an accuracy of 94.88 ± 0.65. It performs better than ten state-of-the-art COVID-19 diagnosis methods. Our SCNN model is promising in diagnosing COVID-19. The developed web app can help the users upload their own images and give the prediction results.