摘要：
Background: Migraine is a common neurovascular disorder with typical throbbing and unilateral headaches, causing a considerable healthcare burden on the global economy. This research aims to prepare chitosan-alginate (CS-AL) nanoparticles (NPs) containing Foshousan oil (FSSO) and investigate its potential therapeutic effects on the treatment of migraine.Methods: FSSO-loaded CS-AL NPs were prepared by using the single emulsion solvent evaporation method. Lipopolysaccharide (LPS)-stimulated BV-2 cells and nitroglycerin (NTG)-induced migraine mice were further used to explore anti-migraine activities and potential mechanisms of this botanical drug.Results: FSSO-loaded CS-AL NPs (212.1 & PLUSMN; 5.2 nm, 45.1 & PLUSMN; 6.2 mV) had a well-defined spherical shape with prolonged drug release and good storage within 4 weeks. FSSO and FSSO-loaded CS-AL NPs (5, 10, and 15 & mu;g/mL) showed anti-inflammatory activities in LPS-treated BV-2 cells via reducing the levels of pro-inflammatory cytokines such as tumor necrosis factor-& alpha; (TNF-& alpha;), interleukin-1 & beta; (IL-1 & beta;), interleukin-6 (IL-6), and nitric oxide (NO), but elevating interleukin-10 (IL-10) expressions. Moreover, FSSO-loaded CS-AL NPs (52 and 104 mg/kg) raised pain thresholds against the hot stimulus and decreased acetic acid-induced writhing frequency and foot-licking duration in NTG-induced migraine mice. Compared with the model group, calcitonin gene-related peptide (CGRP) and NO levels were downregulated, but 5-hydroxytryptamine (5-HT) and endothelin (ET) levels were upregulated along with rebalanced ET/NO ratio, and vasomotor dysfunction was alleviated by promoting cerebral blood flow (CBF) in the FSSO-loaded CS-AL NPs (104 mg/kg) group.Conclusion: FSSO-loaded CS-AL NPs could attenuate migraine via inhibiting neuroinflammation in LPS-stimulated BV-2 cells and regulating vasoactive substances in NTG-induced migraine mice. These findings suggest that the FSS formula may be exploited as new phytotherapy for treating migraine.
作者机构：
[Zhou, Jinbo; Li, Hao; Kang, Zhen; Zeng, Shan; Sheng, Zhongyin] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Chen, Yulong] Wuhan Polytech Univ, Coll Med & Hlth Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Shan Zeng] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
polished rice;RoI;YOLOv5;YOLACT
摘要：
The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily.
摘要：
This paper is concerned with the large time behavior of the solutions for 1D radiation hydrodynamic limit model without viscosity and its asymptotic stability of the viscous contact discontinuity wave under the smallness assumption of the strength of the contact wave and initial perturbations. The present pressure includes a fourth-order term about the absolute temperature from radiation effect which brings the main difficulty. Furthermore, the dissipative of the system is weaker for the lack of viscosity. All these make the problem more challenging. The prove is mainly based on the energy method, including normal and radial directions energy estimates.
摘要：
While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets.
作者：
Liu, Chunyan;Werner, Elisabeth M.;Ye, Deping;Zhang, Ning
期刊：
JOURNAL OF GEOMETRIC ANALYSIS,2023年33(8):1-25 ISSN：1050-6926
通讯作者：
Zhang, N
作者机构：
[Liu, Chunyan] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.;[Werner, Elisabeth M.] Case Western Reserve Univ, Dept Math, Cleveland, OH 44106 USA.;[Ye, Deping] Mem Univ Newfoundland, Dept Math & Stat, St John, NF A1C 5S7, Canada.;[Zhang, Ning] Huazhong Univ Sci & Technol, Sch Math & Stat, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Zhang, N ] H;Huazhong Univ Sci & Technol, Sch Math & Stat, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China.
关键词：
Affine surface area;Floating body;Log-concave functions;Ulam floating function;Ulam floating set
摘要：
We extend the notion of Ulam floating sets from convex bodies to Ulam floating functions. We use the Ulam floating functions to derive a new variational formula for the affine surface area of log-concave functions.
摘要：
This paper is concerned with an ideal polytropic model of non-viscous and heat-conductive gas in a one-dimensional half space. We focus our attention on the outflow problem when the flow velocity on the boundary is negative and we prove the stability of the viscous shock wave and its superposition with the boundary layer under some smallness conditions. Our waves occur in the subsonic area. The intrinsic properties of our system are more challenging in mathematical analysis, however, in the subsonic area, the lack of a boundary condition on the density provides us with a special manner for defining the shift for the viscous shock wave, and helps us to construct the asymptotic profiles successfully. New weighted energy estimates are introduced and the perturbations on the boundary are handled by some subtle estimates.
摘要：
This paper focuses on the exponential stabilisation problem of inertial memristive neural networks (IMNNs) with unbounded discrete time-varying delays. The considered IMNNs are modelled by second-order derivatives equations by introducing the inertial terms. By using nonsmooth analysis, Lyapunov stability theory, inequality techniques and integral-differential of Lyapunov functional method, a feedback controller is designed to guarantee pth moment exponential stabilisation of the addressed IMNNs under the framework of Filippov solutions. It is worth noticing that the considered time delays of IMNNs can be unbounded. Finally, a numerical example is presented to illustrate the effectiveness of the main theoretical result.
期刊：
Structural and Multidisciplinary Optimization,2023年66(4):1-22 ISSN：1615-147X
通讯作者：
Liang Gao
作者机构：
[Zhang, Haobo; Xia, Zhaohui; Zhuang, Ziao; Gao, Liang] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipmesssnt & Technol, Wuhan 430074, Peoples R China.;[Yu, Chen] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Yu, Jingui] Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan 430070, Peoples R China.
通讯机构：
[Liang Gao] T;The State Key Lab of Digital Manufacturing Equipmesssnt and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
摘要：
Isogeometric analysis has been widely applied in topology optimization in recent years, and various methods have been derived. However, most methods are accompanied by significant computational costs, which make it difficult to deal with complex models and large-scale design problems. In this paper, an isogeometric topology optimization method based on deep neural networks is proposed. The computational time of optimization can be effectively reduced while ensuring high accuracy. With the IGA-FEA two-resolution SIMP method, the machine-learning dataset can be obtained during early iterations. Unlike existing data-driven methods, online dataset generation both significantly reduces data collection time and enhances relevance to the design problem. As the iterations process, the machine learning model can be updated online by continuously collecting new data to ensure that the optimized topology structures approach the standard results. Through a series of 2D and 3D design examples, the generality and reliability of the proposed model have been verified and its time-saving advantage becomes more pronounced as the design scale increases. Furthermore, the impacts of neural network parameters on the results are studied through several controlled experiments.
通讯机构：
[Xu, SY ] H;Huazhong Agr Univ, Coll Engn, Wuhan, Peoples R China.;Huazhong Agr Univ, Minist Agr, Key Lab Agr Equipment Middle & Lower Reaches Yangt, Wuhan, Peoples R China.
关键词：
3D Reconstruction;point clouds segmentation;rapeseed siliques;silique recognition;sparse-dense point clouds mapping
摘要：
In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.
通讯机构：
[Zhang, C ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Contamination;Farming;Geographic information systems;Machine learning algorithms;Risk management;farmland protection;geographic information systems;heavy metals;machine learning algorithms;risk assessment;soil
摘要：
As a nationally protected land resource, farmland plays a crucial role in agriculture production and food safety, making the quality of soil and environmental health critically important. Therefore, studying the extent of soil heavy metal pollution in farmland is of great significance for understanding the growth environment of food crops and protecting agricultural land resources. This study addresses the challenge of accurately, quickly, and conveniently assessing the extent of soil heavy metal pollution across an entire research area using a limited number of soil samples. To tackle this issue, a novel soil heavy metal pollution risk hybrid intelligent evaluation model (HIEM) is proposed. The HIEM utilizes the Semi-Supervised Bayesian Regression (Semi-BR) model, trained through Bayesian Co-training, to predict the soil heavy metal content at unsampled points. It employs an improved Multiple Kernel Support Vector Machine (MKSVM) model to evaluate the pollution status of the soil. Additionally, Geographic Information System (GIS) techniques are employed for spatial analysis of the pollution situation in the research area. The study focuses on eight soil heavy metals: As, Cd, Cr, Hg, Pb, Zn, Cu, and Ni. The experimental verification of the model was conducted using field sampling data from the major agricultural areas of Huangpi and Xinzhou in Wuhan, Hubei Province, China. The experimental results show that the eastern region of Huangpi District is more severely contaminated, particularly the central area in the northeast, with moderate to high pollution levels. The hybrid intelligent evaluation model achieves an average accuracy of 96.66% in assessing single-factor pollution of the eight soil heavy metals and an overall evaluation accuracy of 97.42%. The hybrid intelligent evaluation model is able to accurately fit traditional single-factor index methods and Nemerow comprehensive pollution index method. The Geographic Information System representation reveals a consistent distribution trend of soil heavy metal pollution reflected by the hybrid intelligent evaluation model with the results obtained from single-factor index and Nemerow comprehensive pollution index evaluation, indicating the feasibility of using this evaluation method for assessing the risk of soil heavy metal pollution. The conclusion shows that the hybrid intelligent evaluation model needs at least 639 sets of sample data to achieve the highest accuracy when assessing the risk of soil heavy metal contamination in an area of about $3.7\times 10^{4}\,\,hm^{2}$ , and this paper provides a reference to solve the problem of realizing high-precision risk assessment of heavy metal contamination of agricultural soils in the case of small samples. This study is of great practical significance for soil pollution investigation, soil quality assessment and other practical work.
