Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

Reanalysis on the Spectral Characteristics and Materials of Jade Jue Unearthed from Lijiashan Ancient Tomb in Yunnan Province, China

Jade ware Jue is one of the most fashionable ornament in ancient China from the Neolithic Age to the Han Dynasty.In this study,the remnants of jade ware Jue,from Lijiashan ancient tomb group have been tested and analyzed respectively.The samples through a light etch surface showing glass luster and those with a severe etch surface showing earthen luster.With the aid of X-ray fluorescence Spectrometer(X-ray Fluorescence)test and X-ray diffraction(XRD)detection,the results show that tremolite is not the main mineral of the sample.The main components of the tested samples with light erosion are antigorite and talc,although the main components of the samples with severe erosion are calcite containing calcium carbonate barium.Furthermore,the research on jade ware Jue in Yunnan culture has not only contributed to our further understanding of the jade objects from Yunnan culture and the variety of jade material located in the Yunnan Province.It also provides a basis for the trace of the history and culture through the distribution and evolution of burial hierarchy laws.At the same time,the microscopic observation of the tested samples manifested a residual trace of“string”at the perforation,which has played a critical role in restoring the wearing of jade ware Jue from the tombs of the ancient Yunnan State and the handicraft skills at that time.

The Analysis of Wolf Imagery in The Company of Wolves

Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts traditional consciousness,and is the research object of many scholars.Starting from the analysis of the wolf image in The Company of Wolves,this paper uses Deleuze’s Becoming-Animal Theory to explore the construction of harmony between nature,humans and gender relations in The Company of Wolves.

Color Expression and Mood Creation in Imagery Oil Painting

Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new development direction of oil painting,and thus imagery oil painting came into being.Color,as the most important element in imagery oil painting,mainly plays the role of mood creation and emotional expression.Many creators are good at injecting their thoughts and emotions into the paintings through color matching,so as to enhance the artistic expression of the paintings.This paper analyzes the color expression characteristics of imagery oil painting and explores the color expression techniques in imagery oil painting and mood creation of imagery oil painting from several aspects.

Application of a hospital–community–family trinity rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction

BACKGROUND Rehabilitation nursing is considered an indispensable part of the cerebral infarction treatment system.The hospital–community–family trinity rehabilitation nursing model can provide continuous nursing services across hospitals,communities,and families for patients.AIM To explore the application of a hospital–community–family rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction.METHODS From January 2021 to December 2021,88 patients with cerebral infarction were divided into a study(n=44)and a control(n=44)group using a simple random number table.The control group received routine nursing and motor imagery therapy.The study group was given hospital–community–family trinity rehabilitation nursing based on the control group.Motor function(FMA),balance ability(BBS),activities of daily living(BI),quality of life(SS-QOL),activation status of the contralateral primary sensorimotor cortical area to the affected side,and nursing satisfaction were evaluated before and after intervention in both groups.RESULTS Before intervention,FMA and BBS were similar(P>0.05).After 6 months’intervention,FMA and BBS were significantly higher in the study than in the control group(both P<0.05).Before intervention,BI and SS-QOL scores were not different between the study and control group(P>0.05).However,after 6months’intervention,BI and SS-QOL were higher in the study than in the control group(P<0.05).Before intervention,activation frequency and volume were similar between the study and the control group(P>0.05).After 6 months’intervention,the activation frequency and volume were higher in the study than in the control group(P<0.05).The reliability,empathy,reactivity,assurance,and tangibles scores for quality of nursing service were higher in the study than in the control group(P<0.05).CONCLUSION Combining a hospital–community–family trinity rehabilitation nursing model and motor imagery therapy enhances the motor function and balance ability of patients with cerebral infarction,improving their quality of life.

YOLOv5-Based Seabed Sediment Recognition Method for Side-Scan Sonar Imagery

Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Jade Collection of Emperor Qianlong in the Qing Dynasty

During the reign of emperor Qianlong of the Qing Dynasty,the political rule was relatively stable,the economy continued to develop,and both civil and martial situations flourished.The emperor’s love for jade articles made this period a peak in the development of Chinese jade culture.Based on jade articles of emperor Qianlong collected in the Palace Museum and related documents and archives,this paper tries to explore characteristics of this collection and emperor Qianlong’s collecting methods,then explains that this collection not only has the functions of aesthetics,but also has the meaning of morality,religion and politics.

JADE OF HONGSHAN:A SPIRITUAL EXPRESSION

Jade has been valued and worshiped in China for millennia.As early as the prehistoric period,it was deemed a medium for connecting earth and heaven and communicating with ancestors and immortals.It was therefore sculpted into ritual objects.The Hongshan Culture custom of using only jade pieces as funerary objects signifies,in some sense,the advent of a civilization governed by wellestablished etiquette rules.

Analysis of the Application of Ink Art in Graphic Design Imagery

The graphic design industry has been developing rapidly in recent years.People have begun to focus on steering the development of graphic design in the direction of localization,integrating more traditional Chinese elements,raising the level of acceptance toward graphic design content,and disseminating traditional culture on this basis.Ink art plays an important role in the historical and cultural development process.It uses simple color contrast to construct different situations and possesses unique artistic charm and cultural heritage.Incorporating ink elements into graphic design may enhance the graphic design style and provide inspiration.This article focuses on the reasons,advantages,and strategies of using ink art in graphic design imagery,hoping to provide references for graphic design activities.

心理意象视域下元宇宙教学赋能高校学生的学习效果研究

数字化转型背景下,元宇宙的应用促使教育方式发生了深刻变革。然而,元宇宙教学中仍存在一些共识性问题尚未得到解决,如学生的学习持久性不强、专注度不足、学习效率低下等。对此,文章将元宇宙教学的沉浸性、交互性、构想性作为刺激变量,将表象能力作为调节变量、心理意象作为中介变量、学习效果作为反应变量,构建了元宇宙教学赋能学习效果的假设模型。在此基础上,文章构建了假设模型的结构方程模型,并进行了中介效应检验和调节作用检验,研究结果显示:元宇宙教学的沉浸性、交互性、构想性均正向影响高校学生的学习效果,心理意象在元宇宙教学影响高校学生学习效果的过程中起中介作用,表象能力在元宇宙教学影响心理意象和高校学生学习效果的过程中发挥调节作用。据此,文章提出高校应将心理意象融入数字素养、开发数字化元宇宙教学平台、培养学生的表象能力等启示。文章的研究有助于高校有针对性地提升元宇宙教学中学生的学习效果,并推动元宇宙技术与元宇宙教学的深度融合。

ORB JADE Quartet Dark Navy 解码合并式功放

这是一台纯粹的“数码功放”,因为它大胆地槟弃了传统的模拟输入,但在数字音源满天飞的今天,也许不是缺点。如果阁下只用于连接电视/机顶盒、CD转盘/蓝光机、电脑等等具备数字输出的设备,组建桌面或客厅系统的话,ORB的JADEQuartetDarkNavy已经能够满足您的需求。

全景影像在城市研究中的应用进展综述

全景成像技术的进步,街景图像工具的普及,以及人工智能领域的计算机视觉、机器学习和深度学习技术的快速发展,推动了在城市研究中利用全景影像进行大规模、自动化的判别与解析。上述领域的快速发展促使近20年来全景影像、人工智能和城市研究领域之间涌现了大量交叉成果。借助文献计量工具中常用的CiteSpace和VOSviewer作为分析平台,梳理了全景影像在城市研究中的应用进展。首先利用文献共被引聚类网络与术语时区图,划分了全景影像在城市研究中的三个发展阶段。然后借助合著网络和关键词聚类分析,梳理了各阶段全景影像在城市研究中的合著关系、全景影像的获取方式、图像信息的提取技术,归纳了全景影像在城市研究中的四个主要应用领域:城市建成环境、城市景观环境、城市物理环境和智慧城市。最后在历史分期视域下,剖析了促成全景影像应用领域发展的主要驱动因素,并总结了应用全景影像的城市研究目前存在的挑战和未来的发展趋势。

媒介变革中网络文学现实观照的幻像强化——从Internet到Sora的技术迁跃

网络文学的媒介提供了一种虚拟空间的文化交流与精神对话方式。伴随着互联网媒介生产技术从早期诞生到Sora AI问世的持续发展,文艺同社会生活边界的日渐模糊,进而打破了以往同社会存在遥远距离的束缚,以一种超现实的虚拟空间状态进入社会大众的生活。网络文艺创作将个体“无意识之思”通过超现实的叙事表达在集体“无意识之思”的公共语境空间内,并通过一种“在场”式体验的创作同创作者与阅读者构成共同的语意空间。而由于公共语境空间的平等交流性,读者与创作者都在他者介入的影响下参与到网络文艺在“自我理想”中找寻“理想自我”的过程中。

基于Gaofen-2影像和面向对象的椰子林分类研究

椰子是重要的热带经济作物,海南椰子种植面积占全国的90%以上。快速获取椰子种植面积及其空间分布信息对热带作物产业规划具有十分重要的作用。本研究基于国产Gaofen-2高分辨率卫星影像,以文昌市东郊镇为试验区,开展椰子林遥感分类研究。基于最优分割尺度的面向对象分类方法,选取4个光谱特征、5个植被指数和32个纹理特征为辅助参量,构建了4种不同的面向对象分类组合(光谱特征、光谱特征+纹理特征组合、光谱特征+植被指数组合、光谱特征+纹理特征+植被指数特征组合)进行椰子林分类提取,并与基于像元的椰子林分类结果进行对比分析。结果表明:(1)仅采用基于像元分类方法,椰子林的总体分类精度(overall accuracy,OA)和用户精度(user’s accuracy,UA)分别达到87.05%和85.21%。(2)相比基于像元分类,4种面向对象分类组合的OA值提高了5.51%~8.72%。(3)光谱特征和纹理特征组合提取椰子林分类结果最优,OA值和UA值分别达到95.77%和97.15%;光谱特征和植被指数的组合也得到了较好的分类结果,OA值和UA值分别为94.88%和94.42%;所有的光谱特征、植被指数和纹理特征全部参与分类得到的OA值和UA值分别为94.67%和94.17%,低于仅使用光谱特征或者植被指数的组合。综上,国产高分辨率Gaofen-2影像在椰子林遥感精准识别中具有很大的潜力,结合纹理特征的面向对象分类方法可以更准确地提取椰子林分类信息,研究结果可为多云多雨地区大尺度椰子林遥感识别提供技术参考。

“象”与“意”:云冈石窟武术造像整理与研究

云冈石窟是我国古代佛教石窟造像的杰出代表,是中华民族的文化宝库,不仅在历史、宗教、艺术等方面具有重要的意义,在体育方面也有独特的内涵,具有极高的研究价值。文章从过往研究较少涉及的武术文史视角出发,运用文献资料法、实地调查法、图像分析法、三重证据法等研究方法对云冈石窟中的武术造像进行研究,对其数量、题材、布局、美学形态、文化意象进行全方位地梳理、归纳和探索。研究表明:云冈石窟共有163幅武术造像,涉及射箭、拳技、力士托举、持械四种项目,其宏观布局与微观动作等外在之“象”,以直观的和不可替代的方式印证了北魏时期中华武艺的历史演进,呈现出佛武合一下的东方硕武之美以及南北朝时代特有的胡汉民族融合之“意”。

基于卫星图像的城区屋面分布式光伏潜力评估

结合市辖区层面光伏发展情况以及城市中不同用地的特点,利用卫星图片识别长沙市各地区适合装设分布式光伏的建筑区域。基于太阳高度角和方位角,通过山体阴影分析屋顶上的建筑阴影,计算地区屋面光伏可承载容量。对市场侧、制造商及电网部门的分布式光伏规划进行研究。通过识别结果可为光伏建设方及电网提供明确地域列表及可用面积,以及可实现基于实际建设项目的可新增光分布式光伏装机容量的预测,以期为分布式光伏电站用地不足问题提供新的解决思路。

紫墨湘玉成分及放射性核素限值研究

紫墨湘玉是近几年新发现的玉种,是一种很好的玉石材料。公众对紫墨湘玉的了解不足,限制了紫墨湘玉的推广与销售。提升公众对紫墨湘玉的科学认知,有助于紫墨湘玉的开发利用,促进当地石雕产业的发展。在野外地质调查的基础上,对其矿物成分及放射性特征开展了采样分析。研究结果表明,墨湘玉具有典型的角岩结构,紫墨湘玉的内外照射指数远低于A类装饰材料限值标准。