作者机构：
[Xicheng Tan; Bocai Liu; Chaopeng Li; Zeenat Khadim Hussain; Kai Wang; Mengyan Ye; Danyang Yang; Zhiyuan Mei] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of China;[Kaiqi Wang] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, People’s Republic of China
通讯机构：
[Xicheng Tan] S;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of China
摘要：
In the event of disaster-related disruptions to public networks, an air-ground communication-navigation-sensing (CNS) ad-hoc network (ANET) can effectively support emergency response operations by ensuring communication, navigation and sensing capabilities. However, during extreme events such as typhoons and torrential rain, flight operations become impossible for aircraft. Therefore, a critical issue that requires immediate investigation is how to effectively provide communication, navigation, and sensing assistance to emergency regions using only ground-based CNS nodes. This paper presents a communication-navigation-sensing spatiotemporal cooperative optimization algorithm based on ResNet-DDPG. It aims to achieve spatiotemporal cooperative optimization of ground CNS nodes in the absence of aerial relay ad hoc network nodes (RANET nodes). This work has three main components: (1) a neural network-based geospatial fitting model for ANET communication, (2) a ResNet based spatiotemporal feature and CNS node fusion encoding method for state space construction for reinforcement learning, and (3) a deep reinforcement learning algorithm called ResNet-DDPG for deploying ground ANET nodes dynamically. Experimental results indicate that, compared to similar approaches, this method significantly improves communication rate optimization and deployment site effectiveness. This study supports emergency response by utilizing ground ANET, which is crucial for disaster emergency response, command scheduling, and life rescue in extreme disaster scenarios.
摘要：
Blended vegetable oil is considered to be a valuable product in the market owing to favourable taste and nutritional composition. The quantification of its contents has notable implications for protecting food safety and consumer interests. Thus, a rapid and non-destructive method is needed to analyse the composition of blended oil. This study established an analytical method combining Raman spectroscopy and prediction models to determine the content of olive oil in a mixture. Competitive adaptive reweighted sampling was employed to select feature bands attributed to β-carotene and unsaturated fatty acids. Various models were used to calculate the mixture proportion, and the importance of characteristic peak intensity affecting the prediction was evaluated via grey relational analysis. The random forest model exhibited superior performance in quantitative analysis, with RMSE and R 2 of 0.0447 and 0.9799, respectively. Overall, this approach was proven to effectively identify blended olive oils, exemplifying its potential in food authentication.
Blended vegetable oil is considered to be a valuable product in the market owing to favourable taste and nutritional composition. The quantification of its contents has notable implications for protecting food safety and consumer interests. Thus, a rapid and non-destructive method is needed to analyse the composition of blended oil. This study established an analytical method combining Raman spectroscopy and prediction models to determine the content of olive oil in a mixture. Competitive adaptive reweighted sampling was employed to select feature bands attributed to β-carotene and unsaturated fatty acids. Various models were used to calculate the mixture proportion, and the importance of characteristic peak intensity affecting the prediction was evaluated via grey relational analysis. The random forest model exhibited superior performance in quantitative analysis, with RMSE and R 2 of 0.0447 and 0.9799, respectively. Overall, this approach was proven to effectively identify blended olive oils, exemplifying its potential in food authentication.
摘要：
The corrosion of steel reinforcement in concrete structures due to chloride exposure poses significant financial and environmental risks. An accurate assessment of chloride diffusion is essential for predicting the service life of steel-reinforced concrete. This article develops six models for estimating the chloride diffusion coefficient (CDC) in concrete, considering various exposure conditions like tidal, splash, atmospheric, and submerged environments. The models utilize a hybrid approach combining the adaptive neuro-fuzzy inference system (ANFIS) and least square support vector regression (LSSVR), enhanced by an improved arithmetic optimization algorithm (IAA). The IAA merges the arithmetic optimization algorithm (AOA) with the Aquila optimizer (AO) to address AOA's limitations. The models undergo sensitivity analysis using the Fourier Amplitude Sensitivity Test (FAST), revealing that the curing mechanism (CM) is the most influential factor, with a sensitivity value of 0.993. The study demonstrates that LSSVR and ANFIS-based methods are highly effective in predicting CDC. Among them, the ANFIS combined with IAA outperforms others regarding reliability and accuracy, making it a superior choice for CDC estimation. This robust model could lead to better management and longevity of concrete structures exposed to chloride, mitigating potential risks. The main application of this research is to enhance the durability management and service life prediction of steel-reinforced concrete structures exposed to chloride-laden environments. Engineers and infrastructure managers can better assess the risk and timing of corrosion onset by accurately estimating the CDC, which is crucial for preventive maintenance and cost-effective design.
摘要：
In this article, we explain how the famous Archimedes' principle of flotation can be used to construct various floating bodies. We survey some of the most important results regarding the floating bodies, including their relations with affine surface area and projection body, their extensions in different settings such as space forms and log-concave functions, and mention some associated open problems.
关键词：
Drone-based monitoring;Construction site safety;IoT integration;GSConv;Real-time safety detection;Linformer
摘要：
Real-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model based on YOLOv10, specifically designed for drone-based safety monitoring. The GSConv module introduces a lightweight feature extraction mechanism, reducing computational complexity without compromising detection accuracy. The Linformer-based attention mechanism efficiently captures global context, addressing challenges in dynamic and complex environments. The model integrates IoT sensor data for real-time feedback, incorporates the GSConv module for lightweight feature extraction, and utilizes a Linformer-based attention mechanism to efficiently capture global context. These innovations reduce computational complexity while significantly improving detection accuracy. Experimental results show that GS-LinYOLOv10 achieves a precision of 91.2% and a mean average precision (mAP) of 89.4%, outperforming existing models. The integration of IoT sensors allows the drone system to dynamically adjust its monitoring focus, improving adaptability to changing environments and enhancing hazard detection. This research provides an advanced, drone-based IoT-enhanced solution for real-time construction site safety monitoring, offering a more effective and efficient approach to safety management.
Real-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model based on YOLOv10, specifically designed for drone-based safety monitoring. The GSConv module introduces a lightweight feature extraction mechanism, reducing computational complexity without compromising detection accuracy. The Linformer-based attention mechanism efficiently captures global context, addressing challenges in dynamic and complex environments. The model integrates IoT sensor data for real-time feedback, incorporates the GSConv module for lightweight feature extraction, and utilizes a Linformer-based attention mechanism to efficiently capture global context. These innovations reduce computational complexity while significantly improving detection accuracy. Experimental results show that GS-LinYOLOv10 achieves a precision of 91.2% and a mean average precision (mAP) of 89.4%, outperforming existing models. The integration of IoT sensors allows the drone system to dynamically adjust its monitoring focus, improving adaptability to changing environments and enhancing hazard detection. This research provides an advanced, drone-based IoT-enhanced solution for real-time construction site safety monitoring, offering a more effective and efficient approach to safety management.
摘要：
The visualization of grain components through non-destructive detection is crucial for crop improvement and quality assessment, making it a research focus. Raman chemical imaging, an effective spectral imaging technology, has been extensively applied to detect various grain components. However, practical applications face challenges such as unclear Raman shift-composition relationships, low-quality images affected by noise, and long imaging times. To address these issues, this paper proposes a novel method, Raman Denoising Diffusion Generative Adversarial Network (RDDGAN), based on the Denoising Diffusion Probabilistic Model (DDPM). DDPM is used as the generator, paired with a discriminator to distinguishes generated and real data. This approach rapidly produces high-quality Raman images, highlighting spatial distribution of prominent components and clarifying Raman shift-composition correspondence. Further, this paper takes maize as the research object to carry out experiments. The main components of maize, such as starch, are continuous in spatial distribution, mainly concentrated in endosperm, and have obvious boundary. There are obvious differences in the distribution of embryo and endosperm, thus Raman imaging can be used to effectively characterize maize components. Experiments with maize show that, compared to other algorithms, RDDGAN generates higher-quality images, accurately characterizing the spatial distribution of maize components, which providing a strong foundation for subsequent quality detection and variety improvement applications.
The visualization of grain components through non-destructive detection is crucial for crop improvement and quality assessment, making it a research focus. Raman chemical imaging, an effective spectral imaging technology, has been extensively applied to detect various grain components. However, practical applications face challenges such as unclear Raman shift-composition relationships, low-quality images affected by noise, and long imaging times. To address these issues, this paper proposes a novel method, Raman Denoising Diffusion Generative Adversarial Network (RDDGAN), based on the Denoising Diffusion Probabilistic Model (DDPM). DDPM is used as the generator, paired with a discriminator to distinguishes generated and real data. This approach rapidly produces high-quality Raman images, highlighting spatial distribution of prominent components and clarifying Raman shift-composition correspondence. Further, this paper takes maize as the research object to carry out experiments. The main components of maize, such as starch, are continuous in spatial distribution, mainly concentrated in endosperm, and have obvious boundary. There are obvious differences in the distribution of embryo and endosperm, thus Raman imaging can be used to effectively characterize maize components. Experiments with maize show that, compared to other algorithms, RDDGAN generates higher-quality images, accurately characterizing the spatial distribution of maize components, which providing a strong foundation for subsequent quality detection and variety improvement applications.
作者机构：
[Li, Mingran; Zhou, Chunhui; Tang, Wuao; Zhang, Fan] Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China.;[Li, Mingran; Huang, Hongxun; Zhou, Chunhui; Tang, Wuao; Zhang, Fan] Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China.;[Huang, Liang; Wen, Yuanqiao] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.;[Huang, Liang; Wen, Yuanqiao] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Peoples R China.;[Huang, HX; Huang, Hongxun] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.
通讯机构：
[Huang, HX ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.
关键词：
Interaction effects;Monitoring point selection;River-crossing bridge;Ship exhaust monitoring;Ship plume dispersion
摘要：
This study addresses the challenge of efficient monitoring of ship exhaust emissions in inland waterways by proposing an optimized approach to selecting bridge-based monitoring points. A micro-scale Computational Fluid Dynamics (CFD) model was developed to simulate interactions between ship exhaust plumes and river-crossing bridges, enabling precise predictions of dispersion patterns and concentrations. A numerical model incorporating pre-defined monitoring points and local environmental data was used to evaluate the influence of wind, water levels, and ship dynamics on plume behavior. The model's feasibility was validated through on-site UAV experiments. Results showed that plume dispersion is significantly affected by wind direction, wind speed, water level, and ship speed. Under extreme low water levels, the proposed three-point monitoring setup achieved a detection probability of 61.47 %, with performance improving as water levels increased. This study enhances monitoring accuracy and efficiency for riverine areas, providing a valuable tool for precise regulation of ship-emitted pollutants and supporting sustainable inland waterway management.
This study addresses the challenge of efficient monitoring of ship exhaust emissions in inland waterways by proposing an optimized approach to selecting bridge-based monitoring points. A micro-scale Computational Fluid Dynamics (CFD) model was developed to simulate interactions between ship exhaust plumes and river-crossing bridges, enabling precise predictions of dispersion patterns and concentrations. A numerical model incorporating pre-defined monitoring points and local environmental data was used to evaluate the influence of wind, water levels, and ship dynamics on plume behavior. The model's feasibility was validated through on-site UAV experiments. Results showed that plume dispersion is significantly affected by wind direction, wind speed, water level, and ship speed. Under extreme low water levels, the proposed three-point monitoring setup achieved a detection probability of 61.47 %, with performance improving as water levels increased. This study enhances monitoring accuracy and efficiency for riverine areas, providing a valuable tool for precise regulation of ship-emitted pollutants and supporting sustainable inland waterway management.
期刊：
Applied Mathematics and Computation,2025年484:128994 ISSN：0096-3003
通讯作者：
Jiemei Zhao
作者机构：
[Shen, Yi; Zhao, Jiemei] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China;[Yu, Liqi] Mathematics Department, East University of Heilongjiang, Harbin 150066, China
通讯机构：
[Jiemei Zhao] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
摘要：
This study is concerned with reachable set bounding of delayed second-order memristive neural networks (SMNNs) with bounded input disturbances. By applying an analytic method, some inequality techniques and an adaptive control strategy, a sufficient condition of reachable set estimation criterion is derived to guarantee that the states of delayed SMNNs are bounded by a compact ellipsoid. A non-reduced order method is employed to investigate the reachable set bounding problem instead of the reduced order method by variable substitution. In addition, the proposed result is presented in algebraic form, which is easy to test. Finally, a simulation is performed to demonstrate the validity of the proposed algorithm.
摘要：
The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring. To address these challenges, we propose an enhanced snake detection model, Snake-DETR, based on RT-DETR, specifically designed for snake detection in complex natural environments. First, we designed the Enhanced Generalized Efficient Layer Aggregation Network Based on Context Anchor Attention, which enhances the feature extraction capability for occluded snakes by aggregating critical layer information and strengthening context-dependent feature extraction. Additionally, we introduced the Enhanced Feature Extraction Backbone Network Based on Context Anchor Attention, which manages input information using multiple Enhanced Generalized Efficient Layer Aggregation Networks to retain essential spatial and semantic information. Subsequently, a lightweight Group-Shuffle Convolution is used to optimize the encoder, which reduces dependency on large-scale training data, thereby making it suitable for deployment on edge devices. Finally, we incorporated the Powerful-IoU loss function to improve regression path accuracy. Experimental results on a custom dataset covering 27 snake species demonstrate that Snake-DETR achieves a good balance between model efficiency and detection performance, meeting the requirements for fine-grained snake object detection. Compared to other state-of-the-art models, Snake-DETR achieved an accuracy of 97.66%, a recall rate of 93.92%, mAP@0.5 of 95.23%, and mAP@0.5:0.95 of 72.15%, all outperforming other algorithms in the comparative tests. Furthermore, the computational load and parameter count of the model are reduced by 47.2 and 52.2%, respectively, compared to the benchmark model. Additionally, the real-time processing capability is 43.5 frames per second, meeting the demand for real-time processing. Snake-DETR demonstrates excellent performance in complex environments and is suitable for wild snake fauna monitoring and edge device deployment, providing key technical support for ecological research.
摘要：
This study aims to provide new evidence linking director tenure to corporate misconduct by analyzing the sample of publicly listed companies in China from 2009 to 2022. The findings reveal a significant positive correlation between director tenure and corporate misconduct, which is negatively moderated by director network position. Further analysis shows that both independent and non-independent directors' tenure increases the likelihood of corporate misconduct, while the centrality of independent and non-independent director networks negatively moderates these corresponding effects. Moreover, external audit quality plays a mediating role in the relationship between director tenure and corporate misconduct. This study elucidates the boundary conditions and mechanisms of corporate misconduct, supporting the management friendliness hypothesis. It offers practical implications for regulators and policymakers to strengthen board governance and audit oversight, thereby contributing to the research on the prevention of corporate misconduct. The limitations of the study include its geographical focus on the Chinese market, suggesting that future research should explore cross-national differences. These findings provide valuable insights for preventing corporate misconduct and promoting corporate sustainability.
期刊：
Journal of Dynamic Systems Measurement and Control-Transactions of the ASME,2025年147(1):011004 ISSN：0022-0434
通讯作者：
Tao, W
作者机构：
[Zhu, Man] Wuhan Univ Technol, Sanya Sci & Educ Innovat Pk Wuhan Univ Technol, Sanya 572000, Hainan, Peoples R China.;[Wen, Yuanqiao; Zhu, Man] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, State Key Lab Maritime Technol & Safety, Wuhan 430063, Hubei, Peoples R China.;[Zhu, Man] Wuhan Univ Technol, Natl Engn Res Ctr Water Transport Safety, Wuhan 430063, Hubei, Peoples R China.;[Zhu, Man] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Hubei, Peoples R China.;[Tao, Wei; Tao, W] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.
通讯机构：
[Tao, W ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.;Wuhan Univ Technol, State Key Lab Maritime Technol & Safety, Wuhan 430063, Hubei, Peoples R China.
摘要：
The effective design of a path-following controller for unmanned surface vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to fulfill this task through taking benefits from an online parameter identification technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multimodel course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to fixing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remained within the acceptable range, especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.
作者机构：
[Lili Fan; Ziwei Wang; Chenfei Liao] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China;[Jingwei Wang] State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
通讯机构：
[Jingwei Wang] S;State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
关键词：
Absorption spectroscopy;Attosecond pulses;Deep learning;High harmonic generation;Laser system design;Neural networks
摘要：
Plasma surface high-order harmonics generation (SHHG) driven by intense laser pulses on plasma targets enables a high-quality extreme ultraviolet (EUV) source with high pulse energy and outstanding spatiotemporal coherence. Optimizing the performance of SHHG is important for its applications in single-shot imaging and EUV absorption spectroscopy. In this work, we demonstrate the optimization of laser-driven SHHG by an improved Bayesian strategy. A traditional Bayesian algorithm is first employed to optimize the SHHG intensity in a two-dimensional parameter space. Then, an improved Bayesian strategy, using the Latin hypercube sampling technique and a dynamic acquisition strategy, is developed to overcome the curse of dimensionality and the risk of local optima in a high-dimensional optimization. The improved Bayesian optimization approach is efficient and robust in convergent to a stable condition in multi-dimensionally optimizing the harmonic ellipticity and intensity. This paves the way for generating a high-quality coherent EUV source with a high repetition rate and promoting further EUV applications.
期刊：
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2025年18:7064-7082 ISSN：1939-1404
作者机构：
[Wei Tao; Haiyang Zhang; Shan Zeng; Long Wang; Chaoxian Liu; Bing Li] College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
摘要：
Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances. The loss of the traditional unmixing algorithm based on deep learning typically depends on reducing the discrepancy between the original and reconstructed hyperspectral image. However, during the training process, the loss feedback method is relatively simple, resulting highly random unmixing results. Moreover, spatial feature extraction can effectively improve the unmixing effect, but existing spatial feature extraction methods in hyperspectral unmixing still have significant room for improvement. To address these challenges, we propose a novel adversarial autoencoder unmixing network considering pixel-level and global similarity measurements based on a Wasserstein generative adversarial network (WGAN) and a U-shaped transformer-enhanced architecture. The WGAN ensures stable gradient updates through a gradient penalty, maintaining Lipschitz continuity, while the U-shaped network with Swin transformer blocks captures both local and global spatial features. Experiments were conducted on synthetic and real-world hyperspectral datasets. Our method outperformed state-of-the-art approaches, achieving improvement in root mean square error and spectral angle distance (SAD). The SAD is a metric that quantifies the angular difference between the true and estimated endmember spectra, our method improves the mean SAD by at least 8.7% compared to competing algorithms, representing an enhancement in unmixing performance. Notably, the method demonstrated superior robustness in low signal-to-noise ratio scenarios, maintaining high unmixing accuracy. These results highlight the potential of our approach to advance unmixing research by addressing both pixel-level and global similarity constraints, providing a new way for hyperspectral unmixing.
摘要：
Conventional segmentation methods based on visible images in intensive pig farming face various challenges. Examples include color differences between pig breeds, background interference and lighting conditions. To overcome these issues, we designed the infrared pig cascade segmentation (INPC) model for the first time on infrared images. The model uses a cascade structure. Each stage utilizes higher resolution feature maps to better preserve fine details. It also solves the problem of poor segmentation of small objects due to low resolution of infrared images. At the same time, the model’s cross-guidance strategy enhances the interaction between bounding box regression and mask prediction. This reduces errors caused by interference like feces and urine. Additionally, a progressive mask branch refines mask prediction, improving segmentation in scenarios like imaging haze or pig adhesion. To facilitate model training and evaluation, we built the first large-scale standardized infrared pig dataset. Experimental results demonstrate that INPC outperforms mainstream segmentation models in terms of average precision (AP), except for AP0.5 . Specifically, INPC achieves AP0.5 , AP0.75 , AP0.5:0.95 , AP0.5:0.95s , and AP0.5:0.95l of 97.9%, 97.1%, 88.2%, 71.5%, and 90.1% respectively. Inference for a single image on a GPU takes only 0.197 s. Some datasets are available at https://github.com/HUBUwg96/INPC .
Conventional segmentation methods based on visible images in intensive pig farming face various challenges. Examples include color differences between pig breeds, background interference and lighting conditions. To overcome these issues, we designed the infrared pig cascade segmentation (INPC) model for the first time on infrared images. The model uses a cascade structure. Each stage utilizes higher resolution feature maps to better preserve fine details. It also solves the problem of poor segmentation of small objects due to low resolution of infrared images. At the same time, the model’s cross-guidance strategy enhances the interaction between bounding box regression and mask prediction. This reduces errors caused by interference like feces and urine. Additionally, a progressive mask branch refines mask prediction, improving segmentation in scenarios like imaging haze or pig adhesion. To facilitate model training and evaluation, we built the first large-scale standardized infrared pig dataset. Experimental results demonstrate that INPC outperforms mainstream segmentation models in terms of average precision (AP), except for AP0.5 . Specifically, INPC achieves AP0.5 , AP0.75 , AP0.5:0.95 , AP0.5:0.95s , and AP0.5:0.95l of 97.9%, 97.1%, 88.2%, 71.5%, and 90.1% respectively. Inference for a single image on a GPU takes only 0.197 s. Some datasets are available at https://github.com/HUBUwg96/INPC .
作者机构：
[Yujian Liu] Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;[Jian Lu] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China;[Guangwu Liu] Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
通讯机构：
[Guangwu Liu] D;Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
关键词：
Shoulder pain;Primary care physicians;Ambulatory care;Differences;NAMCS (National Ambulatory Medical Care Survey)
摘要：
The aim of this study was to identify the differences in the clinical management of shoulder pain by primary care physicians (PCPs) and non-primary care physicians (non-PCPs) from the National Ambulatory Medical Care Survey (NAMCS) dataset. This cross-sectional study included ambulatory care visits for shoulder pain by using NAMCS data from 2007 to 2019. Descriptive statistics were presented to assess patient-level and visit-level characteristics of the sampled visits. By controlling for patient-level and visit-level covariates, we conducted a multivariable logistic regression to evaluate the influence of primary care physician status on the utilization of health services (pain medications, PT referral, health education/counseling, and diagnostic imaging) for shoulder pain. There were 74.43 million ambulatory care visits by adults with shoulder pain during the study period, and nearly one-third of these shoulder visits were made to PCPs. As compared with non-PCPs, PCPs had higher adjusted odds of prescribing narcotic analgesics (adjusted odds ratio [OR] = 1.62, 95% confidence interval [CI]: 1.04–2.51), skeletal muscle relaxants (adjusted OR = 2.71, 95% CI: 1.65–4.45), other pain medications (adjusted OR = 1.87, 95% CI: 1.13–3.07), and lower odds of prescribing PT (adjusted OR = 0.34, 95% CI: 0.21–0.55) and MRI (adjusted OR = 0.46, 95% CI: 0.25–0.84). We observed significant differences in the services ordered or provided by PCPs versus non-PCPs for shoulder pain in ambulatory care settings. These results may reveal the higher reliance of pharmacological approaches, coupled with the potential under-utilization of PT during the ambulatory shoulder care provided by PCPs compared to non-PCPs in the United States.
摘要：
This article proposes a novel soft multiprototype clustering algorithm (SMP) for high-dimensional data clustering with noisy and complex structural patterns. SMP integrates dimensionality reduction, multiprototype clustering, and multiprototype merge clustering under a two-layer seminonnegative matrix factorization (semi-NMF) architecture. Specifically, the first semi-NMF layer performs multiprototype clustering, which solves the problem that a single prototype cannot represent complex data structures. Meanwhile, the multiprototype fuzzy clustering constraints ensure that the multiprototypes better characterize the original data structure. The second semi-NMF layer performs multiprototype merge clustering to mitigate the issues of heavy computation burden and poor antinoise performance of the spectral clustering algorithm. The introduction of the Laplace graph matrix regularization constraint in this layer assists SMP in completing the merging of multiprototypes with complex data structures. Comprehensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms.
摘要：
Hyperspectral imaging (HSI) has been effectively used in the nondestructive assessment of food quality in recent years. However, the identification of moldy objects using HSIs still faces challenges, including slow detection speed and poor identification accuracy. To address these challenges, this study proposes a three-dimensional hyperspectral mold detection (3D-HMD) approach. The model utilizes multiple 3D convolution (3DMC) modules as the backbone network for optimizing spectral-spatial feature extraction and introduces an attention mechanism to promote the feature information of different hyperspectral bands. A feature pyramid network (FPN) is then used to fuse classification features outputted from the backbone network for feature enhancement. To improve the recognition efficiency for moldy targets, a detection head module derived from the field of object detection is introduced to achieve HIS object-level classification. The experimental results indicate that the detection speed of the proposed model is nearly tenfold greater than that of traditional algorithms, such as 1DRNN and 3D-CNN, with a mean average precision (mAP) of 81.63 %. Overall, the 3D-HMD model demonstrates remarkable efficiency and accuracy in recognizing moldy peanuts, leading to suitable applications for food quality detection.
摘要：
Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem.
期刊：
Journal of Differential Equations,2024年409:817-850 ISSN：0022-0396
通讯作者：
Ruan, LZ
作者机构：
[Fan, Lili] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Sch Math & Stat, POB 71010, Wuhan 430079, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Key Lab NAA MOE, POB 71010, Wuhan 430079, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.;[Xiang, Wei] City Univ Hong Kong, Dept Math, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China.
通讯机构：
[Ruan, LZ ] C;Cent China Normal Univ, Sch Math & Stat, POB 71010, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab NAA MOE, POB 71010, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.
关键词：
Radiative full Euler equations;Non-slip boundary condition;Rarefaction wave;Asymptotic stability
摘要：
This paper is devoted to studying the initial-boundary value problem for the radiative full Euler equations, which are a fundamental system in the radiative hydrodynamics with many practical applications in astrophysical and nuclear phenomena, with the non-slip boundary condition on an impermeable wall. Due to the difficulty from the disappearance of the velocity on the impermeable boundary, quite few results for compressible Navier-Stokes equations and no result for the radiative Euler equations are available at this moment. So the asymptotic stability of the rarefaction wave proven in this paper is the first rigorous result on the global stability of solutions of the radiative Euler equations with the non-slip boundary condition. It also contributes to our systematical study on the asymptotic behaviors of the rarefaction wave with the radiative effect and different boundary conditions such as the inflow/outflow problem and the impermeable boundary problem in our series papers including [5], [6].
This paper is devoted to studying the initial-boundary value problem for the radiative full Euler equations, which are a fundamental system in the radiative hydrodynamics with many practical applications in astrophysical and nuclear phenomena, with the non-slip boundary condition on an impermeable wall. Due to the difficulty from the disappearance of the velocity on the impermeable boundary, quite few results for compressible Navier-Stokes equations and no result for the radiative Euler equations are available at this moment. So the asymptotic stability of the rarefaction wave proven in this paper is the first rigorous result on the global stability of solutions of the radiative Euler equations with the non-slip boundary condition. It also contributes to our systematical study on the asymptotic behaviors of the rarefaction wave with the radiative effect and different boundary conditions such as the inflow/outflow problem and the impermeable boundary problem in our series papers including [5], [6].
