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Long-term Evolution of Solar Activity and Prediction of the Following Solar Cycles

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Research in Astronomy and Astrophysics 2024年第3期24卷 194-204页

作者： Pei-Xin Luo Bao-lin Tan National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China

Solar activities have a great impact on modern high-tech systems,such as human aerospace activities,satellite communication and navigation,deep space exploration,and related scientific ***,studying the long-term evolution trend of solar activity and accurately predicting the future solar cycles are highly *** on the wavelet transform and empirical function fitting of the longest recorded data of the annual average relative sunspot number(ASN)series of 323 yr to date,this work decisively verifies the existence of the solar century cycles and confirms that its length is about 104.0 yr,and the magnitude has a slightly increasing trend on the timescale of several hundred *** on this long-term evolutionary trend,we predict solar cycles 25 and26 by using phase similar prediction *** for solar cycle 25,its maximum ASN will be about146.7±33.40,obviously stronger than solar cycle *** peak year will occur approximately in 2024,and the period will be about 11±1 *** for solar cycle 26,it will start around 2030,and reach its maximum between2035 and 2036,with maximum ASN of about 133.0±3.200,and period of about 10 yr.

关键词： Sun activity-Sun evolution-(Sun) sunspots-methods data analysis

Low-Brightness Object Recognition Based on Deep Learning

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Computers, Materials & Continua 2024年第5期79卷 1757-1773页

作者： Shu-Yin Chiang Ting-Yu lin Department of Information and Telecommunications Engineering Ming Chuan UniversityTaoyuan City333Taiwan

This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition *** primary goal is to tackle issues related to recognizing objects with low brightness *** this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information *** detection scenarios are categorized into normal brightness and low brightness *** the system determines a normal brightness environment,normal brightness images are recognized using deep learning *** low-brightness situations,three methods are proposed for *** first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented *** second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for *** third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for *** experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition *** research outcome can be applied to nighttime surveillance systems or nighttime road safety systems

关键词： Low-brightness depth image image segmentation image recognition HDV HSD

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

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Computers, Materials & Continua 2024年第2期78卷 2391-2418页

作者： Ruchun Jia Jianwei Zhang Yi lin College of Computer Science Sichuan UniversityChengdu610065China

With the popularization of the Internet and the development of technology,cyber threats are increasing day by *** such as malware,hacking,and data breaches have had a serious impact on *** network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time *** security defense methods and tools have been unable to cope with the complex and changing network security *** paper proposes a machine-learning security defense algorithm based on metadata association *** control over unauthorized users through privacy,integrity,and *** user model is established and the mapping between the user model and the metadata of the data source is *** analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be *** and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense *** experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is *** method can effectively resist data intrusion and has high air traffic control *** can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

关键词： Data-oriented architecture metadata correlation features machine learning security defense data source integration

Cigarette Use and Secondhand Smoke Exposure Among Secondary School Students—China,2023

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China CDC weekly 2024年第22期6卷 509-515页

作者： Xinying Zeng Shiwei Liu lin Xiao Tobacco Control Office Chinese Center for Disease Control and PreventionBeijingChina

What is already known about this topic?In 2021,the prevalence of experimental and current cigarette use among secondary school students in China stood at 16.7%and 4.7%,***,39.9%of these students were exposed to secondhand smoke at *** is added by this report?In comparison to 2021,the prevalence of current cigarette use remained unchanged at 4.2%in 2023,whereas experimental use declined to 13.7%.Notably,rates were significantly higher among vocational senior high school(VSHS)students relative to their peers in senior high school(SHS)and junior high school(JHS).Furthermore,exposure to secondhand smoke in schools decreased to 35.4%in 2023 from previously recorded levels,with more pronounced reductions observed in JHS and SHS populations and no notable change among VSHS *** are the implications for public health practice?Targeted tobacco control policies are imperative for secondary school students,including the establishment of smoke-free school ***,it is crucial to pay close attention to the needs of VSHS students.

关键词： smoke unchanged senior

Psychological intervention based on social cognitive theory: Treating pain, anxiety, and depression in perioperative patients

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World Journal of Psychiatry 2024年第8期14卷 1199-1207页

作者： Hai-Jian Mao lin-Fei Wang Chun lin Department of Surgical Anesthesiology The First People’s Hospital of Lin’an DistrictHangzhouHangzhou 311300Zhejiang ProvinceChina Department of Gastrointestinal Surgery Hangzhou First People’s HospitalHangzhou 310006Zhejiang ProvinceChina Department of Comprehensive Intervention The First People’s Hospital of Lin’an DistrictHangzhouHangzhou 311300Zhejiang ProvinceChina

BACKGROUND Surgery is an effective method for treating certain *** such as disease,preoperative fear and tension,surgical stress,postoperative pain,and related complications directly affect the smooth progression and outcome of *** may experience a series of psychological and physiological changes during the perioperative period,resulting in anxiety and depression,which may reduce the pain threshold and worsen their *** We enrolled 200 patients who underwent surgical care at The First People’s Hospital of lin’an District,Hangzhou between January and December *** were categorized into a routine intervention group(n=103)and a psychological intervention group(n=97),based on the intervention strategies *** assessment tools,including the self-rating anxiety scale(SAS),the self-rating depression scale(SDS),and the Connor–Davidson Resilience scale,were used to measure patients’negative states and *** pre-and post-intervention scores for these metrics in the two groups were then *** In the psychological intervention group,the SAS and SDS scores(31.56±5.18 and 31.46±4.57,respectively)were significantly reduced compared to the routine intervention group(P<0.05).The visual analog scale pain scores at 12 and 24 hours after intervention(6.85±1.21,4.24±0.72)were notably higher than those in the routine intervention group(P<0.05).The psychological intervention group also demonstrated superior scores in perseverance(36.08±3.29),self-reliance(22.63±2.91),optimism(11.42±1.98),and resilience(70.13±5.37),compared to the routine intervention group(P<0.05).Additionally,the psychological intervention group’s confrontation score(23.16±4.29)was higher(P<0.05).This group also reported lower scores in avoidance(9.28±1.94)and yielding(6.19±1.92)(P<0.05).Lastly,the Short Form 36 Health Survey scores were significantly higher in the psychological intervention group,indicating a better quality of life(P<0.05).CONCLUSION

关键词： Perioperative period Social cognitive theory Psychological intervention Pain Anxiety and depression

Neuronomodulation of Excitable Neurons

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Neuroscience Bulletin 2024年第1期40卷 103-112页

作者： Yizhang Chen lin Xiao Jian Qiu Institute of Neuroscience Second Military Medical UniversityShanghai 200433China Institute for Brain Research and Rehabilitation Key Laboratory of BrainCognition and Education Sciences of Ministry of EducationSouth China Normal UniversityGuangzhou 510631China Department of Chemical Physiology and Biochemistry Oregon Health and Science UniversityPortlandOR 97239USA

Neuronomodulation refers to the modulation of neural conduction and synaptic transmission(i.e.,the conduction process involved in synaptic transmission)of excitable neurons via changes in the membrane potential in response to chemical substances,from spillover neuro-transmitters to paracrine or endocrine hormones circulating in the *** can be direct or indirect,depending on the transduction pathways from the ligand binding site to the ion pore,either on the same molecule,*** ion channel,or through an intermediate step on different *** major players in direct neurono-modulation are ligand-gated or voltage-gated ion *** key process of direct neuronomodulation is the binding and chemoactivation of ligand-gated or voltage-gated ion channels,either orthosterically or allosterically,by various *** neuronomodulation involves metabotropic receptor-mediated slow potentials,where steroid hormones,cytokines,and chemokines can implement these *** neuronomodulation is of great significance for understanding the physiological mechanisms of brain function,and the occurrence and treatment of diseases.

关键词： Neuron Membrane potential Excitability Chemoactivation Neuronomodulation

An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan-Vese Model

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 1119-1134页

作者： Shupeng Qiu Chujin lin Wei Zhao School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510520China

In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and *** this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial *** to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational ***,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic ***,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.

关键词： Image segmentation Chan–Vese model local radial basis functionmethod Gaussian kernel Runge–Kuttamethod

Demixing microwave signals using system-on-chip photonic processor

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Light(Science & Applications) 2024年第3期13卷 399-401页

作者： Sheng Gao Chu Wu Xing lin Department of Electronic Engineering Tsinghua University100084 BeijingChina Beijing National Research Center for Information Science and Technology Tsinghua University100084 BeijingChina

The integrated photonic processor,co-packaged with electronic peripherals,is proposed for blind source separation of microwave signals,which separates signal-of-interest from dynamic interference with real-time adapta... 详细信息

关键词： processor microwave mixing

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

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Computers, Materials & Continua 2024年第8期80卷 1907-1925页

作者： Jing Wang Chen Zhang Tianwen lin Geographic Information and Tourism College Chuzhou UniversityChuzhou239000China Anhui Province Key Laboratory of Physical Geographic Environment Chuzhou UniversityChuzhou239000China

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low *** propose the introduction of spatial and channel attention modules to the convolutional neural network ***,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise *** on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample *** results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for *** with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing *** also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

关键词： Deep learning semantic segmentation remote sensing imagery road extraction