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A novel sea-land segmentation based on integral image reconstruction in MWIR images

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Science China(Information Sciences) 2017年第6期60卷 269-271页

作者： yin zhuang Dechun GUO He CHEN Fukun BI Long MA Nouman Q.SOOMRO School of Information and Electronics Beijing Institute of Technology School of Electronic Information Engineering North China University of Technology Department of Software Engineering Mehran University of Engineering and Technology

Dear editor,Middle wave infrared remote(MWIR)technology is the main approach in remote sensing applications,because thermal infrared can work all-time.The MWIR technology can provide valid object information in remote... 详细信息

关键词： In GFM A novel sea-land segmentation based on integral image reconstruction in MWIR images

“物有所值”还是“心有所怡”:乡村民宿主客价值共创平衡机理研究

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经济管理 2024年第1期46卷 169-183页

作者：李燕琴赵壮英姚舟罗湘阳中央民族大学管理学院北京100081 太原工业学院经济与管理系山西太原030008 浙江海洋大学外国语学院浙江舟山316022

民宿是带动乡村经济增长的重要业态,主客价值共创是推进民宿高质量发展的关键。本文以源于价值共创DART模型的价值共创参与度VCCE模型为基础,采用问卷调查与在线实验相结合的方法,探讨物质感知价值与非物质感知价值对游客价值共创意愿... 详细信息

民宿是带动乡村经济增长的重要业态,主客价值共创是推进民宿高质量发展的关键。本文以源于价值共创DART模型的价值共创参与度VCCE模型为基础,采用问卷调查与在线实验相结合的方法,探讨物质感知价值与非物质感知价值对游客价值共创意愿与行为的影响机理。研究发现:(1)物质感知价值(感知可得性、投入产出比)与非物质感知价值(感知互动性、感知公平性)均显著正向影响价值共创意愿,其中感知互动性的影响效应最大;(2)非物质感知价值比物质感知价值更有助于激发游客价值共创意愿,且在“心有所怡”型比“物有所值”型消费情境下,游客价值共创意愿更强;(3)物质型促销与非物质型促销皆有助于将游客价值共创意愿转化为共创行为,且在“心有所怡”型消费情境下,物质型促销作用更显著;而在“物有所值”型消费情境下,非物质型促销作用更显著,呈现出契可尼效应。本文从物质价值感知与非物质价值感知平衡的视角,拓展了对游客“感知价值-价值共创意愿-价值共创行为”间影响机理的理解,研究关注了占比最多的乡村中端民宿高质量发展问题,有助于改善中国乡村民宿主客价值共创中存在的价值失衡现象,为以民宿为重要切入点推进乡村振兴提供了新思路。

关键词：乡村民宿价值共创主客关系物质感知价值非物质感知价值

Precisely Control Relationship between Sulfur Vacancy and H Absorption for Boosting Hydrogen Evolution Reaction

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Nano-Micro Letters 2024年第4期16卷 14-24页

作者： Jing Jin Xinyao Wang Yang Hu zhuang Zhang Hongbo Liu Jie yin Pinxian Xi College of Chemistry and Chemical Engineering Frontiers Science Center for Rare IsotopesState Key Laboratory of Applied Organic ChemistryLanzhou UniversityLanzhou 730000People’s Republic of China

Ef fective and robust catalyst is the core of water splitting to produce hydrogen.Here, we report an anionic etching method to tailor the sulfur vacancy(VS) of NiS_(2) to further enhance the electrocatalytic performance for hydrogen evolution reaction(HER). With the VS concentration change from 2.4% to 8.5%, the H* adsorption strength on S sites changed and NiS_(2)-VS 5.9% shows the most optimized H* adsorption for HER with an ultralow onset potential(68 m V) and has long-term stability for 100 h in 1 M KOH media. In situ attenuated-total-reflection Fourier transform infrared spectroscopy(ATR-FTIRS) measurements are usually used to monitor the adsorption of intermediates. The S-H* peak of the Ni S_(2)-VS 5.9% appears at a very low voltage, which is favorable for the HER in alkaline media. Density functional theory calculations also demonstrate the Ni S_(2)-VS 5.9% has the optimal |ΔG^(H*)| of 0.17 e V. This work offers a simple and promising pathway to enhance catalytic activity via precise vacancies strategy.

关键词： Hydrogen evolution reaction S vacancies Nanosheet H Adsorption

Unveiling protein corona composition:predicting with resampling embedding and machine learning

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Regenerative Biomaterials 2024年第1期3卷 27-33页

作者： Rong Liao Yan zhuang Xiangfeng Li Ke Chen Xingming Wang Cong Feng Guangfu yin Xiangdong Zhu Jiangli Lin Xingdong Zhang College of Biomedical Engineering National Engineering Research Centre for BiomaterialsSichuan UniversityChengdu610065China

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration.When nanoparticles(NPs)enter the living system,they quickly interact with proteins in the body fluid,forming the protein corona(PC).The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs.However,achieving accurate predictions remains a significant challenge.Although several machine learning(ML)models like Random Forest(RF)have been used for PC prediction,they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution.In this study,resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data.Various ML models were evaluated,and RF model was finally used for prediction,and good correlation coefficient(R^(2))and root-mean-square deviation(RMSE)values were obtained.Our ablation experiments demonstrated that the proposed method achieved an R^(2) of 0.68,indicating an improvement of approximately 10%,and an RMSE of 0.90,representing a reduction of approximately 10%.Furthermore,through the verification of label-free quantification of four NPs:hydroxyapatite(HA),titanium dioxide(TiO_(2)),silicon dioxide(SiO_(2))and silver(Ag),and we achieved a prediction performance with an R^(2) value>0.70 using Random Oversampling.Additionally,the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration,PDI and surface modification.

关键词： nanoparticles protein corona machine learming resampling technique feature analysis

Performances of fissured red sandstone after thermal treatment with constant-amplitude and low-cycle impacts

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第2期16卷 561-587页

作者： Yongjun Chen Tubing yin P.G.Ranjith Xibing Li Qiang Li Dengdeng zhuang School of Resources and Safety Engineering Central South UniversityChangsha410083China Department of Civil Engineering Monash UniversityMelbourne3800Australia Oulu Mining School University of OuluOuluFI-90014Finland

In the engineering practices,it is increasingly common to encounter fractured rocks perturbed by temperatures and frequent dynamic loads.In this paper,the dynamic behaviors and fracture characteristics of red sandstone considering temperatures(25℃,200℃,400℃,600℃,and 800℃)and fissure angles(0°,30°,60°,and 90°)were evaluated under constant-amplitude and low-cycle(CALC)impacts actuated by a modified split Hopkinson pressure bar(SHPB)system.Subsequently,fracture morphology and second-order statistics within the grey-level co-occurrence matrix(GLCM)were examined using scanning electron microscopy(SEM).Meanwhile,the deep analysis and discussion of the mechanical response were conducted through the synchronous thermal analyzer(STA)test,numerical simulations,one-dimensional stress wave theory,and material structure.The multiple regression models between response variables and interactive effects of independent variables were established using the response surface method(RSM).The results demonstrate the fatigue strength and life diminish as temperatures rise and increase with increasing fissure angles,while the strain rate exhibits an inverse behavior.Furthermore,the peak stress intensification and strain rate softening observed during CALC impact exhibit greater prominence at increased fissure angles.The failure is dominated by tensile damage with concise evolution paths and intergranular cracks as well as the compressor-crushed zone which may affect the failure mode after 400℃.The second-order statistics of GLCM in SEM images exhibit a considerable dependence on the temperatures.Also,thermal damage dominated by thermal properties controls the material structure and wave impedance and eventually affects the incident wave intensity.The tensile wave reflected from the fissure surface is the inherent mechanism responsible for the angle effect exhibited by the fatigue strength and life.Ultimately,the peak stress intensification and strain rate softening during impact are determined by both the material structure and compacti

关键词： Red sandstone Temperature Fissure Constant-amplitude and low-cycle(CALC) impact Fatigue failure Response surface method(RSM)

An Integrated Control Framework for Torque Vectoring and Active Suspension System

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 62-73页

作者： Jiwei Feng Jinhao Liang Yanbo Lu Weichao zhuang Dawei Pi Guodong yin Liwei Xu Pai Peng Chaobin Zhou School of Mechanical Engineering Southeast UniversityNanjing 211189China School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Mechano-Electronic Engineering Lanzhou University of TechnologyLanzhou 730050China

Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

关键词： Four-wheel independently driven electric vehicles Tire nonlinearity Linear-time-varying(LTV)model Model predictive control Rapid control prototype

Biomaterials-based anti-inflammatory treatment strategies for Alzheimer's disease

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Neural Regeneration Research 2024年第1期19卷 100-115页

作者： Jianjian Chu Weicong Zhang Yan Liu Baofeng Gong Wenbo Ji Tong yin Chao Gao Danqi Liangwen Mengqi Hao Cuimin Chen Jianhua zhuang Jie Gao You yin Department of Neurology Second Affiliated Hospital(Shanghai Changzheng Hospital)of Naval Medical UniversityShanghaiChina School of Pharmacy University College LondonLondonUK Department of Clinical Pharmacy Xinhua HospitalShanghai Jiao Tong University School of MedicineClinical Pharmacy Innovation InstituteShanghai Jiao Tong University School of MedicineShanghaiChina School of Health Science and Engineering University of Shanghai for Science and TechnologyShanghaiChina Changhai Clinical Research Unit Shanghai Changhai HospitalNaval Medical UniversityShanghaiChina

The current therapeutic drugs for Alzheimer's disease only improve symptoms,they do not delay disease progression.Therefo re,there is an urgent need for new effective drugs.The underlying pathogenic factors of Alzheimer's disease are not clear,but neuroinflammation can link various hypotheses of Alzheimer's disease;hence,targeting neuroinflammation may be a new hope for Alzheimer's disease treatment.Inhibiting inflammation can restore neuronal function,promote neuro regeneration,reduce the pathological burden of Alzheimer's disease,and improve or even reverse symptoms of Alzheimer's disease.This review focuses on the relationship between inflammation and various pathological hypotheses of Alzheimer's disease;reports the mechanisms and characteristics of small-molecule drugs(e.g.,nonsteroidal anti-inflammatory drugs,neurosteroids,and plant extracts);macromolecule drugs(e.g.,peptides,proteins,and gene therapeutics);and nanocarriers(e.g.,lipid-based nanoparticles,polymeric nanoparticles,nanoemulsions,and inorganic nanoparticles)in the treatment of Alzheimer's disease.The review also makes recommendations for the prospective development of anti-inflammatory strategies based on nanocarriers for the treatment of Alzheimer's disease.

关键词： Alzheimer's disease anti-inflammation blood-brain barrier drug delivery microglia nanoparticles neuroinflammation plant extracts

Deep learning-based semantic segmentation of human features in bath scrubbing robots

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Biomimetic Intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan yin Hao Sun School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model LiDAR Semantic segmentation

Atropine can induce autophagy independent of the M3 muscarinic acetylcholine receptor

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Traditional Medicine Research 2024年第7期9卷 35-41页

作者： Hai-Chang yin Wen-Zhu zhuang Xin-Jie Jiang Di Liu Postdoctoral Research Workstation Heilongjiang Academy of Agricultural SciencesHarbin 150069China. College of Life Science and Agriculture Forestry Qiqihar UniversityQiqihar 161006China.

Background: No other effects of atropine other than as an antagonist of muscarinic acetylcholine receptor (mAChR) have been found. Methods: In this study, human kidneyepithelial cells were treated with different physiological regulators. Results: Subsequently, it was found that atropine could significantly induce autophagy as demonstrated by the appearance of autophagosome-like double- or single-membrane vesicles in the cytoplasm ofhost cells and the number of GFP-LC3 dots. In addition, increased conversion of the autophagy marker protein LC3-I and LC3-II and increased p62/SQSTM1 indicatedincomplete autophagy. In addition, atropine induced autophagosome levels in a dose-dependent manner within a certain concentration range in human kidney epithelial cells. In atropine-treated mouse skeletal muscle cells containing nicotinic acetylcholinereceptors and rat cardiac muscle cells containing mAchR, atropine induced autophagy in mouse skeletal muscle cells but not in rat cardiac muscle cells. Furthermore, atropine did not induce autophagy in tissue cells containing mAchR in vivo but did in tissue cells not containing mAchR. Conclusion: This study expands the application and understanding of atropine’s action mechanism in the field of medicine.

关键词： autophagy atropine acetylcholine receptor cells