Branching Characteristics Research Articles

Mixed Convection Flow Analysis of Carreau Fluid Over a Vertical Stretching/Shrinking Sheet

The current investigation aspires to unravel the steady mixed convection flow of Carreau fluid over a permeable vertical stretching/shrinking sheet near a stagnation point. The system of governing equations is reduced into ODEs utilizing appropriate similarity transformations. The similarity transformations are obtained via the Lie scaling group of transformations. Dual similarity solutions are detected depending on the opposing flow parameter for stretching and shrinking cases. The effects of pertinent parameters on the skin friction coefficient, Nusselt number, velocity, and temperature fields are examined in detail. The influence of the suction parameter on the variations of skin friction coefficient for the stretching case shows various behavior than in the shrinking case. However, on the variations of the Nusselt number, a similar trend in both the stretching and shrinking cases is observed. The fluid velocity decreases, and the temperature rises with the increment of non-Newtonian parameter in the upper branch, whereas the lower branch depicts opposite trends. Due to the different characteristics of the lower branch than the upper branch, it is necessary to find a physically reliable solution branch. Thus, a linear temporal stability analysis is conducted based on the sign of the smallest eigenvalue. The smallest eigenvalues are determined numerically using the shooting technique, revealing that the upper branch is the only stable solution branch.

Experimental Study and 3-D Modeling of Positive Streamer Discharges Under the Effect of Humidity

The research on streamer discharges not only provides support for insulation design of high-voltage power transmission systems, but also lays a theoretical foundation for lightning discharges. The macroscopic parameters of streamer discharges have been investigated, but there is a lack of analysis on the development and branching characteristics of streamers under the effect of humidity. Besides, different types of models have been built to simulate streamer propagation. However, there are few 3D models which simulate branching process of multiple streamers at different humidity. In this work, a discharge experimental platform with controllable environmental parameters has been built. The effect of the humidity of 4.5~19.1 g/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> on streamer propagation and branching has been experimentally studied. Then the 3D “discharge tree” model of collective streamers is improved, in which the streamer branching incorporates the photoionization in humid air. Furthermore, we compare the results of the 3D simulation model with the experimental data. Through the simulation model, the streamer parameters such as line charge density and electric field distribution are obtained. The results show that the electric field of streamer channels are larger when the humidity is higher, but the propagation velocities are reduced. The humidity has limited effect on the streamer branching ratio.

DETisSeg: A dual-encoder network for tissue semantic segmentation of histopathology image

Accurate segmentation of tissue regions in hematoxylin and eosin (H&E)-stained histopathology images is an essential step in computational pathology. It is still a challenging task due to the large-scale distribution, irregular morphological variations, and fuzzy boundaries between different tissues. Semantic segmentation methods with strong feature representative capability for extracting local fine-grained and global coarse-grained features are desired to tackle this challenge. In this paper, we propose a dual encoder network for tissue semantic segmentation of histopathology image, called DETisSeg, which aims to fully fuse the features of global context information by the Swin Transformer branch and local features by the convolutional neural network(CNN) branch. Particularly, we design an improved residual connection to recover the lost spatial information during the patch merging phase in the Swin Transformer branch. In addition, we employ a pyramid architecture decoder to generate a composite feature map with multiple scales. We perform experiments on two publicly available tissue semantic segmentation datasets, including the BCSS and LAUD-HistoSeg datasets, to evaluate the effectiveness of the proposed DETisSeg. Compared with eight state-of-the-art semantic segmentation methods, the results show that the proposed DETisSeg achieves improved performance. Particularly, it outperforms Swin transformer on the mean Dice and IoU with improvements of 1.36% and 1.79% on BCSS dataset, 0.96% and 4.48% on LUAD-HistoSeg dataset, respectively.

SOME OPTIONS OF FETAL TOPOGRAPHY OF THE LONG BRANCHES OF THE CROSS PLEXUS

In the conditions of wartime, the frequency of damage to the nerves of the lower extremity increased sharply. Open injuries make up the vast majority of peripheral nerve injuries. Understanding variations in the topography and the condition of the surrounding soft tissue structures allows for a detailed assessment of the condition of the peripheral nerves of the lower limb, to diagnose the exact site of damage, the type of neuropathy, and to choose treatment and rehabilitation tactics. The purpose of the study will be to establish variants of the fetal topography of the long branches of the sacral plexus. The material for the study of topographical and anatomical features of the long branches of the sacral plexus was 26 human fetuses of 5-7 months. The sciatic nerve is the largest nerve in humans, originating from the ventral division of spinal nerves L4 to S3 and containing fi bers from both the posterior and anterior lumbosacral plexuses. The sciatic nerve runs along the entire back surface of the lower limb up to the foot. The sciatic nerve innervates a signifi cant part of the skin and muscles of the thigh, lower leg, and foot. It was established that the long branches of the sacral plexus can be subjected to compression or damage in diff erent parts of its anatomical course. Among the variants of the anatomical variability of the long branches of the sacral plexus, the topography of the sciatic nerve is the most common. In the three studied fetuses of 180.0 mm, 215.0 mm, 255 mm TKD, a high branching variant of the sciatic nerve was detected, and the features of the long branches of the sacral plexus in the specifi ed fetuses were unilateral. In this case, when assessing clinical symptoms, practicing doctors should take into account options for bilateral asymmetry. Prospects for further research. Deepening the study of the variant anatomy of the long branches of the sacral plexus in human fetuses of diff erent ages.

Evaluation of facial nerve perineural spread from cutaneous squamous cell carcinoma using 3T MR neurography.

Perineural spread (PNS) is a rare but potentially fatal consequence of cutaneous squamous cell carcinoma (cSCC) of the head and neck. We aimed to evaluate the accuracy of 3T MR neurography in detecting and defining the extent of facial nerve (VII) PNS from cSCC, and highlight characteristic radiological features in peripheral branches to improve early diagnosis. Single-institution retrospective review of 38 patients with clinical, radiological, and/or histopathological findings consistent with VII PNS from cSCC who underwent pre-operative 3T MR neurography. Compared to histopathology (gold standard), 3T MR neurography had a sensitivity of 89% and positive predictive value of 97%. In true-positive cases (n = 33), zonal extent was correctly identified in 100%. Seventy-nine% had simultaneous trigeminal nerve (V) PNS, mostly involving the auriculotemporal branch of the mandibular nerve (64%). When the causative lesion was absent (n = 23), the extra-temporal VII demonstrated asymmetrical enhancement alone (n = 6), bulky expansion (n = 8), or extra-neural spread (n = 9). Peripheral VII branch involvement, particularly the buccal and zygomatic, was readily identified using known anatomical landmarks. 3T MR neurography is highly accurate in evaluating VII PNS from cSCC, and thus should be specifically requested by physicians if suspicious for disease. Coexistent V PNS was common, highlighting the need to examine V branches to allow complete treatment planning. The unique radiological patterns identified showcases disease progression. As early detection improves patient outcomes, the radiologist must look for peripheral VII involvement inspecific anatomical areas, which is within the capabilities of 3T MR neurography.

Theoretical Insights into Midchain Radicals and Branching Characteristics in Solution Copolymerization of Ethylene and Vinyl Acetate.

The kinetics of intermolecular chain transfer to polymer (CTP) and chain transfer to monomer (CTM), as well as backbiting in solution copolymerization of ethylene and vinyl acetate (VAc), are calculated by density functional theory. An intrinsic rate coefficient for each reaction type, whose Arrhenius parameters are expressed as functions of unreacted monomer composition, is extracted from calculated elementary reactions, and hence it applies to a wide range of fragment compositions of EVA. Backbiting controls the generation of midchain radicals (MCRs) on the backbone, and its rate maximizes at a medium fraction of VAc in the unreacted monomers. CTP plays an insignificant role in MCR generation even at high conversion rates due to its low pre-exponential factor. The concentration of MCRs is quantified and is close to that of ECRs at high conversions and high fractions of VAc in monomers. Additionally, branching characteristics are explored; concentrations of short-chain branching (SCB) and long-chain branching (LCB) are about 0.7-2.5 and 0.1-0.4%, respectively, and drop with VAc fraction rapidly. The role of the migration of MCRs is highlighted, which transforms about 10% of SCB points into LCB points. Disagreeing with insights from laboratory experiments, it is the migration, rather than CTP, which increases the LCB concentration at high conversions.

Segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black blood magnetic resonance imaging with multi-task learning.

Atherosclerotic cardiovascular disease is the leading cause of death worldwide. Early detection of carotid atherosclerosis can prevent the progression of cardiovascular disease. Many (semi-) automatic methods have been designed for the segmentation of carotid vessel wall and the diagnosis of carotid atherosclerosis (i.e., the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis) on black blood magnetic resonance imaging (BB-MRI). However, most of these methods ignore the intrinsic correlation among different tasks on BB-MRI, leading to limited performance. Thus, we model the intrinsic correlation among the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis tasks on BB-MRI by using the multi-task learning technique and propose a gated multi-task network (GMT-Net) to perform three related tasks in a neural network (i.e., carotid artery lumen segmentation, outer wall segmentation, and carotid atherosclerosis diagnosis). In the proposed method, the GMT-Net is composed of three modules, including the sharing module, the segmentation module, and the diagnosis module, which interact with each other to achieve better learning performance. At the same time, two new adaptive layers, namely, the gated exchange layer and the gated fusion layer, are presented to exchange and merge branch features. The proposed method is applied to the CAREII dataset (i.e., 1057 scans) for the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis. The proposed method can achieve promising segmentation performances (0.9677 Dice for the lumen and 0.9669 Dice for the outer wall) and better diagnosis accuracy of carotid atherosclerosis (0.9516 AUC and 0.9024 Accuracy) in the "CAREII test" dataset (i.e., 106 scans). The results show that the proposed method has statistically significant accuracy and efficiency. Even without the intervention of reviewers required for the previous works, the proposed method automatically segments the lumen and outer wall together and diagnoses carotid atherosclerosis with high performance. The proposed method can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate normal carotid arteries from atherosclerotic arteries and to outline vessel wall contours.

Military Sexual Trauma and its Association with Mental Health Among Sexual Minority and Heterosexual Veterans in the United States.

Purpose: The purpose of this study was to examine the prevalence of military sexual trauma (MST) by sexual orientation, characteristics of sexual minority (SM) and heterosexual veterans who have experienced MST, and associations between sexual orientation and mental health symptoms among veterans who have experienced MST. Methods: Data were analyzed from a nationally representative web-based survey of 4069 U.S. veterans (4.9% SM), which assessed sociodemographic (e.g., age, sexual orientation) and military (e.g., branch) characteristics, and lifetime and current mental health symptoms. Bivariate analyses compared sociodemographic and military characteristics and mental health symptoms among veterans who have experienced MST by sexual orientation. Multivariable logistic regressions examined associations between sexual orientation and mental health symptoms among veterans who have experienced MST, while controlling for differences in sociodemographic characteristics and non-MST traumatic events. Results: The prevalence of MST (7.5% of weighted sample) was higher among SM veterans compared to heterosexual veterans, with bisexual/pansexual/queer veterans endorsing the highest prevalence (22.7%), followed by gay/lesbian (17.0%) and heterosexual (6.5%) veterans. SM veterans who experienced MST were more likely than heterosexual veterans to screen positive for lifetime and current posttraumatic stress disorder (odds ratio [OR] = 3.06 and 3.38, respectively), and current drug use disorder (OR = 3.53). Conclusions: This study adds to growing evidence that mental health symptoms associated with MST disproportionately impact SM veterans relative to heterosexual veterans. Approaches to reducing barriers and tailoring MST-related care to SM veterans, including through addressing cumulative effects of minority stress, are discussed.

Supplement and Suppression: Both Boundary and Nonboundary are Helpful for Salient Object Detection.

Current methods aggregate multilevel features from the backbone and introduce edge information to get more refined saliency maps. However, little attention is paid to how to suppress the regions with similar saliency appearances in the background. These regions usually exist in the vicinity of salient objects and have high contrast with the background, which is easy to be misclassified as foreground. To solve this problem, we propose a gated feature interaction network (GFINet) to integrate multiple saliency features, which can utilize nonboundary features with background information to suppress pseudosalient objects and simultaneously apply boundary features to supplement edge details. Different from previous methods that only consider the complementarity between saliency and boundary, the proposed network introduces nonboundary features into the decoder to filter the pseudosalient objects. Specifically, GFINet consists of global features aggregation branch (GFAB), boundary and nonboundary features' perception branch (B&NFPB), and gated feature interaction module (GFIM). According to the global features generated by GFAB, boundary and nonboundary features produced by B&NFPB and GFIM employ a gate structure to adaptively optimize the saliency information interchange between abovementioned features and, thus, predict the final saliency maps. Besides, due to the imbalanced distribution between the boundary pixels and nonboundary ones, the binary cross-entropy (BCE) loss is difficult to predict the pixels near the boundary. Therefore, we design a border region aware (BRA) loss to further boost the quality of boundary and nonboundary, which can guide the network to focus more on the hard pixels near the boundary by assigning different weights to different positions. Compared with 12 counterparts, experimental results on five benchmark datasets show that our method has better generalization and improves the state-of-the-art approach by 4.85% averagely in terms of the regional and boundary evaluation measures. In addition, our model is more efficient with an inference speed of 50.3 FPS when processing a 320 x 320 image. Code has been made available at https://github.com/lesonly/GFINet.

Multilevel Feature Fusion for End-to-End Blind Image Quality Assessment

In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.

Visual camera relocalization using both hand-crafted and learned features

The localization of the camera is essential in AR, MR, and robotics. Diverse pipelines employ a hand-crafted or learning based way to predict the camera pose as per the task. In the localization process, both weaknesses and strengths are maintained. However, few current frameworks consider these two features simultaneously. In this study, a novel relocalization pipeline for RGB or RGB-D input is proposed, including a coarse stage with learned features, further refinement with hand-crafted features, and a stable process to measure the confidence of both stages for improving localization robustness. Instead of directly regressing the camera pose, the coarse procedure uses registration to the known source and predicted weighted target point cloud to obtain the initial result. Therefore, we design a deep network called PGNet to construct the weighted target point cloud with the image and previous poses as inputs. Moreover, in consideration of dynamic surroundings, we add a segmentation branch distinguishing each point as either fixed or dynamic with the purpose of promoting dynamic perception. Correspondingly, the segmentation-extended Chamfer Distance is added to optimize PGNet. During the pose refinement, the feature space is established via hand-crafted feature extraction and matching on the training set. Based on the coarse pose, we obtain the accurate pose by applying Kabsch or Perspective-n-Point (PnP) algorithm to point-to-point correspondences built through searching the space and matching Oriented Fast and Rotated Brief (ORB) features. Furthermore, an additional process is presented by defining coarse and refinement metrics to gain a more stable performance. Finally, experiments on both static and dynamic scenes are conducted. On the one side, the results demonstrate the state-of-the-art performance over other existing methods on 7 Scenes, INDOOR-6, Cambridge Landmarks and TUM RGB-D. On the other side, the positive effects of the pose learning part, dynamic branch, confidence regression and hand-crafted feature based refinement are also provided.

Feature-Decision Level Collaborative Fusion Network for Hyperspectral and LiDAR Classification

The fusion-based classification of hyperspectral (HS) and light detection and ranging (LiDAR) images has become a prominent research topic, as their complementary information can effectively improve classification performance. The current methods encompass pixel-, feature- and decision-level fusion. Among them, feature- and decision-level fusion have emerged as the mainstream approaches. Collaborative fusion of these two levels can enhance classification accuracy. Although various methods have been proposed, some shortcomings still exist. On one hand, current methods ignore the shared advanced features between HS and LiDAR images, impeding the integration of multimodal features and thereby limiting the classification performance. On the other hand, the existing methods face difficulties in achieving a balance between feature- and decision-level contributions, or they simply overlook the significance of one level and fail to utilize it effectively. In this paper, we propose a novel feature-decision level collaborative fusion network (FDCFNet) for hyperspectral and LiDAR classification to alleviate these problems. Specifically, a multilevel interactive fusion module is proposed to indirectly connect hyperspectral and LiDAR flows to refine the spectral-elevation information. Moreover, the fusion features of the intermediate branch can further enhance the shared-complementary information of hyperspectral and LiDAR to reduce the modality differences. In addition, a dynamic weight selection strategy is meticulously designed to adaptively assign weight to the output of three branches at the decision level. Experiments on three public benchmark datasets demonstrate the effectiveness of the proposed methods.

CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation

Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. One such technique is the U-Net framework based on the U-shaped convolutional neural networks (CNN) and its variants. However, these methods have limitations in simultaneously capturing both the global and the remote semantic information due to the restricted receptive domain caused by the convolution operation’s intrinsic features. Transformers are attention-based models with excellent global modeling capabilities, but their ability to acquire local information is limited. To address this, we propose a network that combines the strengths of both CNN and Transformer, called CoTrFuse. The proposed CoTrFuse network uses EfficientNet and Swin Transformer as dual encoders. The Swin Transformer and CNN Fusion module are combined to fuse the features of both branches before the skip connection structure. We evaluated the proposed network on two datasets: the ISIC-2017 challenge dataset and the COVID-QU-Ex dataset. Our experimental results demonstrate that the proposed CoTrFuse outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation. The codes are available at https://github.com/BinYCn/CoTrFuse.

Multi-Scale Semantic and Detail Extraction Network for Lightweight Person Re-Identification

Exploring multi-level information to obtain fine-grained features is the key factor to improve the performance of person re-identification (Re-ID). However, existing models for person Re-ID only focus on learning the high-level semantic information while neglecting the low-level detail information. To alleviate this issue, we propose a lightweight person Re-ID method termed Multi-Scale Semantic and Detail Extraction Network (MSDENet) to obtain robustness and discriminative feature representation for the Re-ID task. Specifically, we design a Series Channel-Spatial Attention (SCSA) and embed it into the lightweight backbone network to focus on the key parts of pedestrian images. Meanwhile, we propose a Multi-Scale Semantic and Detail Extraction (MSDE) method to extract multi-scale features of semantic information and detail information, which can effectively capture the feature diversity of pedestrian images. Furthermore, we design a Feature Enhancement Fusion (FEF), which enhances and fuses the fine-grained features of semantic extraction and detail extraction branches to better obtain the discriminative feature representation. Extensive experiments conducted on popular datasets Market1501, MSMT17, and CUHK03 demonstrate that the proposed MSDENet has competitive performance compared with the state-of-the-art methods.

LBARNet: Lightweight bilateral asymmetric residual network for real-time semantic segmentation

Real-time semantic segmentation, as a key technique for scene understanding, has been an important research topic in the field of computer vision in recent years. However, existing models are unable to achieve good segmentation accuracy on mobile devices due to their huge computational overhead, which makes it difficult to meet actual industrial requirements. To address the problems faced by current semantic segmentation tasks, this paper proposes a lightweight bilateral asymmetric residual network (LBARNet) for real-time semantic segmentation. First, we propose the bilateral asymmetric residual (BAR) module. This module learns multi-scale feature representations with strong semantic information at different stages of the semantic information extraction branch, thus improving pixel classification performance. Secondly, the spatial information extraction (SIE) module is constructed in the spatial detail extraction branch to capture multi-level local features of the shallow network to compensate for the lost geometric information in the downsampling stage. At the same time, we design the attention mechanism perception (AMP) module in the jump connection part to enhance the contextual representation. Finally, we design the dual branch feature fusion (DBF) module to exploit the correspondence between higher-order features and lower-order features to fuse spatial and semantic information appropriately. The experimental results show that LBARNet, without any pre-training and pre-processing and using only 0.6M parameters, achieves 70.8% mloU and 67.2% mloU on the Cityscapes dataset and Camvid dataset, respectively. LBARNet maintain a high segmentation accuracy while using a smaller number of parameters compared to most existing state-of-the-art models.

Image inpainting via progressive decoder and gradient guidance

Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing multi-stage methods fail to effectively inpainting the larger missing areas, their common drawback is that the result of each stage is easily misguided by the wrong content generated in the previous stage. To solve this issue, in this paper, a novel one-stage generative adversarial network based on the progressive decoding architecture and gradient guidance. Firstly, gradient priors are extracted at the encoder stage to be passed to the decoding branch, and multiscale attention fusion group is used to help the network understand the image features. Secondly, multiple parallel decoding branches fill and refine the missing regions by top-down passing the reconstructed priors. This progressively guided repair avoids the detrimental effects of inappropriate priors. The joint guidance of features and gradient priors helps the restoration results contain the correct structure and rich details. And the progressive guidance is achieved by our fusion strategy, combining reimage convolution and design channel coordinate attention to fuse and reweight the features of different branches. Finally, we use the multiscale fusion to merge the feature maps at different scales reconstructed by the last decoding branch and map them to the image space, which further improves the semantic plausibility of the restoration results. Experiments on multiple datasets show that the qualitative and quantitative results of our computationally efficient model are competitive with those of state-of-the-art methods.

Anatomic characteristics of branches related to the vegetative growth of coffee tree (Coffea arabica L., Rubiaceae) under nutritional variation

Although the anatomical tracts of the stem are used to assess the vegetative vigor of different crops, little is known about which characteristics of the vascular system contribute to the growth of coffee plants or how these characteristics are influenced by variations in macronutrients. The study objective was to understand how the growth, anatomical characteristics of the stem, and yield of coffee plants were modified by nutritional variation and which anatomical variables most contribute to growth and/or yield of the coffee plants. Growth, yield, and characteristics of the vascular system of orthotropic and plagiotropic branches of coffee plants, subjected to different percentages of NPK delivered by standard fertilization for coffee plants, had been evaluated. All the evaluated characteristics best fit the quadratic regression model. Growth, morphology, anatomy, and yield were promoted to some extent by NPK fertilization. Orthotropic branches had larger vessel diameters and ray frequencies, whereas in plagiotropic branches, vessels were more numerous. The pith area and the frequency of the rays were correlated with the height of the plant, which in turn was correlated with yield. Plant height, anatomy, and yield were modified by the fertilization. The best NPK range for plant height and yield was 100–130% of the standard. The anatomical variables most correlated with plant height were the pith area of the plagiotropic branch and the ray frequency of the orthotropic branch.

Real-time detection of a camouflaged object in unstructured scenarios based on hierarchical aggregated attention lightweight network

Accurate and real-time grasping of household items is a challenge for home service robots in complex indoor environments. In this paper, using transparent cups as the recognition object, an accurate, reliable, and real-time visual algorithm is proposed for the elderly who needs nursing care to solve the recognition problems of overlapping, blurred, and small cups in complex unstructured indoor scenes. In order to meet the lightweight deployment of home service robots while considering both high-scale and low-scale semantic features, this paper designs a multi-scale fusion lightweight backbone network based on the Split Shuffle Block (SSB) and Group Shuffle Block (GSB) feature encoding units. Among them, depthwise separable convolution (DwConv) uses to reduce the amount of coding unit parameters, and feature shuffle uses to promote information exchange and the ability of information expression. In order to accurately identify the transparent cup camouflaged in complex backgrounds, this paper proposed a lightweight feature enhancement module that combines multi-scale hierarchical aggregation attention and multi-branch parallel convolution structure. The proposed module used an adaptive weighting strategy and a channel normalization weighting strategy to highlight the active regions of boundary features in each branch feature map, enhance the exchange of boundary information, and reduce the loss of detailed information. The experimental results on the IndoorCup and MobileCup datasets show that the detection accuracy of the proposed method is 93.6% and 92.6%, respectively, and the model calculation amount is only 0.91 M, which can lightweight deploy on indoor mobile robots for real-time detection. From the results of qualitative comparisons, the proposed method has strong robustness. It can effectively suppress false and missed detection caused by background interference. Likewise, it also effectively identifies camouflage objects and small objects in complex backgrounds.

Three‐stream RGB‐D salient object detection network based on cross‐level and cross‐modal dual‐attention fusion

AbstractThe effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest. The existing dual‐stream models can be used for high‐level feature fusion or unidirectionally transferring depth features to RGB features; however, they are unable to fully exploit the differences in modality. Furthermore, owing to the influence of image background information, the generated salient object is affected by background swallow. Herein, a three‐stream RGB‐D SOD method based on cross‐layer and cross‐modal dual‐attention (CMDA) fusion is proposed. In the encoding stage, the CMDA fusion module is used to fuse RGB and depth features layer by layer. Through this module, merged interactive features may be used to extract the richer features of salient objects, realize the commonality and complementarity of fusion features, and achieve effective cross‐modal fusion. In addition, for the decoding stage, a cross‐level feature fusion module that introduces global context features into the up‐sampling process, reduces the impact of salient objects being swallowed by the background, and helps to accurately detect salient areas is proposed. Three different branch features are used for simultaneous end‐to‐end training. The experimental results demonstrate that the proposed method outperforms other methods in terms of multiple evaluation metrics on four datasets. Furthermore, the authors visualize the precision–recall curve, F‐measure curve, and saliency map, which indicate that the detection effect of the proposed method is superior to those of other methods. During the testing stage, our model ran at 14 frames per second (FPS).

The Relationship Between the Force Production in the Isometric Squat and Bench Press Exercises and the Lower and Upper Body Anaerobic Power Parameters

The primary aim of this study was to investigate relationships between the muscular force in the isometric bench press and squat movements and the Wingate anaerobic test (WanT) results for the upper and lower body. Secondary purpose was to investigate possible correlations between the WanT and the explosive isometric force. Eighteen healthy male volunteer athletes (age: 23.37 ± 1.65, height: 178.37 ± 5.67, body mass; 76.69 ± 3.73) with at least 2 years of strength training participated in the study. During the two test sessions, one-repetition maximum (1-RM), isometric squat and isometric bench press tests and anaerobic power tests with Wingate anaerobic power and arm ergometer were performed. Results of the study showed a high degree of correlation between isometric squat (Squatiso) and lower body Peak Power and Average Power (r (14) = 0.766, p &amp;lt; 0.001 and r (14) = 0.690, p &amp;lt; 0.003 respectively). However, there was no significant relationship between Squatiso and Fatigue Index (FI) (p &amp;gt; 0.05). In isometric bench press, there was also a high level of correlation between upper body peak power and average power (r (14) = 0.620, p &amp;lt; 0.01 and r (14) = 0.749, p &amp;lt; 0.001 respectively). These data can be used to classify the power capabilities of the athletes and determining the training loads to be used in achieving the training goals. In addition, periodic measurements of the maximum power and strength performances of the athletes with field tests and laboratory test batteries according to the characteristics of the sports branch can also provide significant contributions to the coaches and researchers.