56 research outputs found
Popular Content Distribution in Vehicular Networks using Coalition Formation Games
No full textIn this paper, we address the popular content distribution (PCD) problem in a highway scenario, in which popular files are distributed to a group of on-board units (OBUs) driving through a single roadside unit (RSU). Due to the high speeds, the OBUs may not finish downloading a large file within the limited time for vehicle-to-roadside (V2R) communication and a peer-to-peer (P2P) network consisting of OBUs out of the RSU coverage can be constructed for completing the file delivery process. However, due to fast and unpredictable topological changes of the vehicular ad hoc network (VANET), the static methods in traditional P2P networks can be inefficient. We model this problem as a coalition formation game with transferable utilities, and propose a coalition formation algorithm that converges into a Nash-stable partition adapting to environmental changes. Based on this algorithm, we further propose a distributed scheme for the overall PCD problem. Simulation results show that our scheme presents a considerable performance improvement relative to the non-cooperative case using the carrier sense multiple access with collision avoidance (CSMA/CA).TelecommunicationsEICPCI-S(ISTP)
Moderate Hypothermia Alleviates Sepsis-Associated Acute Lung Injury by Suppressing Ferroptosis Induced by Excessive Inflammation and Oxidative Stress via the Keap1/GSK3β/Nrf2/GPX4 Signaling Pathway
No full textJie Xu,1,2 Liujun Tao,1 Liangyan Jiang,1 Jie Lai,1 Juntao Hu,1 Zhanhong Tang1 1Department of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China; 2Department of Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, 629000, People’s Republic of ChinaCorrespondence: Zhanhong Tang; Juntao Hu, Department of Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi, 530021, Tel +86 13978816316 ; +86 18978866565, People’s Republic of China, Email [email protected]; [email protected]: Sepsis-associated acute lung injury (SA-ALI) is a common complication in patients with sepsis, contributing to high morbidity and mortality. Excessive inflammation and oxidative stress are crucial contributors to lung injury in sepsis. This study aims to examine the protective effects of moderate hypothermia on SA-ALI and explore the underlying mechanisms.Methods: Sepsis was induced in rats through cecal ligation and puncture followed by intervention with moderate hypothermia (32– 33.9°C). Blood, bronchoalveolar lavage fluid, and lung tissues were collected 12 hours post-surgery. Inflammatory responses, oxidative injury, SA-ALI-related pathophysiological processes, and Keap1/GSK3β/Nrf2/GPX4 signaling in septic rats were observed by ELISA, lung W/D ratio, immunohistochemistry, immunofluorescence, histological staining, Western blotting, RT-qPCR, and TEM assays.Results: Moderate hypothermia treatment alleviated lung injury in septic rats, reflected in amelioration of pathological changes in lung structure and improved pulmonary function. Further, moderate hypothermia reduced arterial blood lactate production and suppressed the expression of inflammatory factors IL-1β, IL-6, and TNF-α; downregulated ROS, MDA, and redox-active iron levels; and restored GSH and SOD content. TEM results demonstrated that moderate hypothermia could mitigate ferroptosis in PMVECs within lung tissue. The underlying mechanism may involve regulation of the Keap1/Nrf2/SLC7A11/GPX4 signaling pathway, with the insulin pathway PI3K/Akt/GSK3β also playing a partial role.Conclusion: Collectively, we illustrated a novel potential therapeutic mechanism in which moderate hypothermia could alleviate ferroptosis induced by excessive inflammation and oxidative stress via the regulation of Keap1/GSK3β/Nrf2/GPX4 expression, hence ameliorating acute lung injury in sepsis.Keywords: moderate hypothermia, sepsis, acute lung injury, ferroptosis, Nrf2, inflammatio
Identification of Tomato Leaf Diseases based on LMBRNet
No full textTomato Disease Image Identification Plays a Very Important Role in the Field of Agricultural Production. Aiming at the Problems of Large Intraclass Differences, Small Inter-Class Differences and Difficult Feature Extraction of Some Tomato Leaf Diseases, This Paper Proposes an Identification of Tomato Leaf Diseases based on LMBRNet. Firstly, a Comprehensive Grouped Differentiated Residual (CGDR) is Built,The Multi-Branch Structure of CGDR is Used to Capture the Diversified Feature Information of Tomato Leaf Diseases in Different Dimensions and Receptive Fields. Then, a Multiple Residual Connection Scheme is Adopted,using Residuals to Connect All Layers, to Ensure the Maximum Information Transmission between Layers in the Network and to Solve the Problems of Network Degradation and Gradient Disappearance in the Network Training Process. Secondly,the Visual Enhancement Effectively Fuses the Results Obtained by Three Different Downsampling Strategies using Average Pooling, Max Pooling, and 1*1 Convolution. Avoid the Loss of Information Caused by Downsampling and Improve the Accuracy of the Network. Moreover, Deep Separable Convolution is Used to Optimize the Network Structure, Reduce the Amount of Model Parameters and Reduce the Computational Resources Occupied by Training and Deploying the Model.we Found that the Depthwise Separable Convolution with a Kernel Size of 1*1 Can Slightly Improve the Efficiency of the Model under the Premise that It Has Little Effect on the Number of Model Parameters. the Application Results of More Than 8000 Images Show that the overall Identification Accuracy is About 99.7%,higher Than ResNet50(97.48%),GoogleNet(98.96%) Etc. Conventional Models. the Parameter Amount of LMBRNet is 4.1M. Less Than ResNet50(23M),GoogleNet(5.7M) Etc. Conventional Models. It is Worth Noting that the Accuracy of LMBRNet(99.7%) is Similar to that of InceptionResNetV2(99.68%), But the Amount of Parameters of LMBRNet(4.1M) is Much Lower Than that of InceptionResNetV2(54M). Moreover, the Parameter Amount of LMBRNet (4.1M) is Slightly Lower Than that of MobileNetV2(2.2M), But the Accuracy Rate of LMBRNet(99.7%) is Higher Than that of MobileNetV2(97.87%). LMBRNet Was Tested on RS, SIW, Plantvillage-Corn Public Datasets, All Obtained High Recognition Accuracy, 82.32% on RS, 88.37% on SIW and 97.25% on Plantvillage-Corn, Indicating that LMBRNet Has Good Generalization. Compare LMBRNet with Advanced Methods. in Four Different Classification Tasks, the Performance of LMBRNet is Similar to ResMLP12 and DCCAM-MRNet, and the Difference of Recognition Accuracy between LMBRNet and ResMLP12 and DCCAM-MRNet is Not More Than 1%. However, the Parameters of LMBRNet (4.1M) Are Lower Than ResMLP12 (14.94M) and DCCAM MRNet (22.8M). LMBRNet is Compared with MobileNetV3, an Advanced Lightweight Classification Model. LMBRNet(88.37% on SIW,82.32% on RS) is Used on Certain Datasets and Performs Better Than MobileNetV3S(83.76% on SIW,75 on RS) and MobileNetV3L(84.34 on SIW,73.39 on RS). the Parameters of LMBRNet(4.1M) Are Lower Than MobileNetV3L(5.4M) and Slightly Higher Than MobileNetV3S(2.9M). This Indicates that LMBRNet Has Good Generality Even Though It Has a Small Number of Parameters
Highly Efficient Waveform Design and Hybrid Duplex for Joint Communication and Sensing
No full textJoint communication and sensing (JCAS) is a very promising 6G technology, which attracts more and more research attention. Compared with communication, radar has many unique features in terms of waveform design criteria, self-interference cancellation (SIC), aperture-dependent resolution, and virtual aperture. This paper proposes a novel waveform design named max-aperture radar slicing (MaRS) to gain a large time-frequency aperture, which is generated by orthogonal frequency division multiplexing (OFDM) and occupies only a tiny fraction of OFDM resources. The proposed MaRS keeps the radar advantages of constant modulus, zero auto-correlation sequence, and simple SIC. As MaRS consumes much less resources, conventional processing methods fail, and novel angle-Doppler map based methods are proposed to obtain the range-velocity-angle information from MaRS echos and strong clutters. To avoid complex full-duplex communication, this paper proposes a hybrid-duplex JCAS scheme composed of half-duplex communication and full-duplex radar. The half-duplex communication antenna array is reused, and a small sensing-dedicated antenna array is added. Using these two arrays, a large space-domain sensing aperture is virtually formed to greatly improve the angle resolution. The numerical results show that the proposed MaRS and hybrid duplex can achieve a high sensing resolution with only 0.4% OFDM resources, which reduces the overheads of conventional methods to less than one tenth.in IEEE Internet of Things Journa
MFaster R-CNN for Maize Leaf Diseases Detection based on Machine Vision
No full textIn order to realize the intelligent diagnosis of maize diseases with complicated backgrounds and similar disease spot characteristics in the real field environment, MFaster R-CNN is proposed by improving the Faster R-CNN algorithm. Firstly, a batch normalization processing layer is added to the convolution layer to speed up the convergence speed of the network and improve the generalization ability of the model; secondly, a central cost function is proposed to construct a mixed loss function to improve the detection accuracy of similar lesions; then, four kinds of pre-trained convolution structures are selected as the basic feature extraction network of Faster R-CNN for training, and the random gradient descent algorithm is used to optimize the training model to test the optimal feature extraction network; finally, the trained model is used to select test sets under different weather conditions for comparison, and MFaster R-CNN is compared with Faster R-CNN and SSD. The experimental results show that in MFaster R-CNN disease detection framework, VGG16 convolution layer structure as feature extraction network has better performance, the average recall rate is 0.9719, F1 is 0.9718, the overall average accuracy rate can reach 97.23%; compared with Faster R-CNN, MFaster R-CNN has an average accuracy of 0.0886 higher and a single image detection time of 0.139 s less; compared with the SSD, the average accuracy is 0.0425 higher, and the single image detection time is reduced by 0.018 s. Our method also provides a basis for timely and accurate prevention and control of maize diseases in the field
Investigation into the influence of mild hypothermia on regulating ferroptosis through the P53-SLC7A11/GPX4 signaling pathway in sepsis-induced acute lung injury
No full textAbstract Background Sepsis-induced acute lung injury (S-ALI) significantly contributes to unfavorable clinical outcomes. Emerging evidence suggests a novel role for ferroptosis in the pathophysiology of ALI, though the precise mechanisms remain unclear. Mild hypothermia (32–34 °C) has been shown to inhibit inflammatory responses, reduce oxidative stress, and regulate metabolic processes. P53 has been reported to downregulate the transcriptional activity of solute carrier family 7 member 11 (SLC7A11), thereby limiting cystine uptake. This reduction in cystine availability compromises the activity of Glutathione peroxidase 4 (GPX4), a cystine-dependent enzyme, ultimately increasing cellular susceptibility to ferroptosis. However, it remains unclear whether mild hypothermia exerts protective effects through the P53-SLC7A11/GPX4 signaling pathway. This study investigates the influence of mild hypothermia on ferroptosis mediated by the P53-SLC7A11/GPX4 pathway in S-ALI. Methods This study utilized both in vivo and in vitro models. In the vivo model, 64 Sprague–Dawley rats were employed, with 40 analyzed for survival outcomes. Sepsis was induced using the cecum ligation and puncture (CLP) method, after which rats were subjected to either normothermic (36–38 °C) or mild hypothermic (32–34 °C) conditions for a duration of 10 h. Twelve hours post-surgery, blood samples, bronchoalveolar lavage fluid, and lung tissue samples were harvested for histological analysis, measurement of inflammatory markers, wet/dry ratios, blood gas analysis, assessment of oxidative stress and ferroptosis, Western blotting, and RT-qPCR analysis. In the in vitro model, RLE-6TN cells were exposed to lipopolysaccharide (LPS) for 24 h under normothermic and mild hypothermic conditions. These cells were then evaluated for cell viability, inflammatory markers, oxidative stress levels, ferroptosis markers, as well as Western blot and RT-qPCR analyses. Results CLP-induced sepsis led to elevated levels of inflammatory markers, increased lung injury scores, and heightened oxidative stress markers. These detrimental effects were significantly ameliorated by mild hypothermia. Furthermore, mild hypothermia reversed the modified expression of P53, SLC7A11, and GPX4 signaling molecules. Notably, mild hypothermia also improved the 5-day survival rate of CLP rats. Conclusion Mild hypothermia attenuates S-ALI and modulates ferroptosis through the P53-SLC7A11/GPX4 signaling pathway
PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images
No full textThe target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods
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