《广西植物》被国际知名数据库ICI World of Journals和ICI Journals Master List收录

日前,《广西植物》正式被波兰《哥白尼索引》(IC)旗下的两个知名数据库哥白尼索引期刊数据库(ICI World of Journals)和哥白尼索引精选数据库(ICI Journals Master List)全文收录,这是该期刊继被瑞典知名数据库(DOAJ)成功收录后的又一大喜讯。

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

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.

赢创向ICIG出售超级吸水剂业务

赢创与国际化工投资者集团(ICIG)签署协议,将出售其超级吸水剂业务。收购价格达数亿欧元。此项出售尚待竞争主管机构批准,预计将于2024年年中完成交易。赢创执行董事会主席库乐满表示:“作为一家特种化学品公司,超级吸水剂业务已不再是我们的核心业务。我们已经找到了可靠的投资方来接手这个业务,从而迈出剥离功能材料业务部门的第二步。”ICIG是一家经验丰富的化工投资集团,总部位于德国美因河畔法兰克福。该集团拥有6 200多名员工.

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.

基于EfficientDet网络的超低空无人机大豆幼苗识别

为提高田间大规模高通量情况下大豆幼苗真叶期(V1)、复叶期(V2)形态的识别率,从而为判断出苗率、苗期整齐度及茎叶喷雾除草最佳时期确定提供依据,提出了一种基于超低空无人机可见光图像的大豆幼苗识别方法。该方法以四旋翼无人机采集到的RGB图像为基础,将其转换至HSV颜色空间从而提取出图像中的大豆幼苗植株,并利用K-means方法找到最佳分割阈值,其真叶期阈值点坐标为(83,183,201)、(31,25,74),复叶期阈值点坐标为(79,215,225)、(29,72,61)。基于提取出的大豆幼苗图像训练深度学习网络,得到大豆幼苗识别模型。测试结果表明:模型可有效识别真叶期与复叶期两个幼苗关键时期的大豆幼苗,真叶期幼苗识别准确率达到92.68%,复叶期幼苗识别准确率达到90.09%,可有效完成田间大豆幼苗苗期判断任务,及时指导大豆种植管理决策。

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.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

《基础医学与临床》已被ICI Journals Master List国际数据库收录

2023-02-16,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《基础医学与临床》入选ICI Journals Master List 2021(哥白尼索引精选数据库)。本刊在《哥白尼索引》(ICI)数据库中的主页:https://journals.indexcopernicus.com/search/details?id=123685以下为ICI数据库确认收录本刊的官方邮件内容(部分).

《橡胶工业》被EBSCO,Ulrichsweb和ICI等国际数据库收录

日前,《橡胶工业》陆续收到通知,被美国EBSCO学术数据库和《乌利希国际期刊指南》(Ulrichsweb Global Serials Directory)以及哥白尼索引期刊数据库(ICI World of Journals)和哥白尼精选数据库(ICI Master List)等国际重要数据库收录为来源期刊。2022年12月,《橡胶工业》收到EBSCO学术数据库通知,从2022年第9期开始被EBSCO学术数据库收录。2022年11月,《橡胶工业》被《乌利希国际期刊指南》收录。2022年10月,《橡胶工业》被哥白尼索引期刊数据库和哥白尼精选数据库(后者为前者的子集)收录。

《基础医学与临床》已被ICI Journals Master List国际数据库收录

2023⁃02⁃16,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《基础医学与临床》入选ICI Journals Master List 2021(哥白尼索引精选数据库)。本刊在《哥白尼索引》(ICI)数据库中的主页:https://journals.indexcopernicus.com/search/details?id=123685以下为ICI数据库确认收录本刊的官方邮件内容(部分)。

《大气与环境光学学报》已被ICI Journals Master List国际数据库收录

2023-11-10,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《大气与环境光学学报》入选ICI Journals Master List 2022(哥白尼索引精选数据库)。

《基础医学与临床》已被ICI Journals Master List国际数据库收录

2023-02-16,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《基础医学与临床》入选ICI Journals Master List 2021(哥白尼索引精选数据库)。

《基础医学与临床》已被ICI Journals Master List国际数据库收录

2023-02-16,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《基础医学与临床》入选ICI Journals Master List 2021(哥白尼索引精选数据库)。

《基础医学与临床》已被ICI Journals Master List国际数据库收录

2023⁃02⁃16,波兰《哥白尼索引》(Index Copernicus International)科技期刊评估小组发来官方邮件,确认《基础医学与临床》入选ICI Journals Master List 2021(哥白尼索引精选数据库)。

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。