Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains

BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023.By delving into these publications,the aim is to deter-mine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry.METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023,forming the dataset for the analysis.Employing advanced visualization techniques,this study mapped the knowledge domains represented in these papers.RESULTS The findings revealed a prevalent focus on key topics such as depression,mental health,anxiety,schizophrenia,and the impact of coronavirus disease 2019.Additionally,through keyword clustering,it became evident that these papers were predominantly focused on exploring mental health disorders,depression,anxiety,schizophrenia,and related factors.Noteworthy contributions hailed authors in regions such as China,the United Kingdom,United States,and Turkey.Particularly,the paper garnered the highest number of citations,while the American Psychiatric Association was the most cited reference.CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry.Additionally,there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.

Global trends and hotspots of type 2 diabetes in children and adolescents:A bibliometric study and visualization analysis

BACKGROUND Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus(T2DM)among children and adolescents worldwide.Due to rapid disease progression,severe long-term cardiorenal complications,a lack of effective treatment strategies,and substantial socioeconomic burdens,it has become an urgent public health issue that requires management and resolution.Adolescent T2DM differs from adult T2DM.Despite a significant increase in our understanding of youth-onset T2DM over the past two decades,the related review and evidence-based content remain limited.AIM To visualize the hotspots and trends in pediatric and adolescent T2DM research and to forecast their future research themes.METHODS This study utilized the terms“children”,“adolescents”,and“type 2 diabetes”,retrieving relevant articles published between 1983 and 2023 from three citation databases within the Web of Science Core Collection(SCI,SSCI,ESCI).Utilizing CiteSpace and VoSviewer software,we analyze and visually represent the annual output of literature,countries involved,and participating institutions.This allows us to predict trends in this research field.Our analysis encompasses co-cited authors,journal overlays,citation overlays,time-zone views,keyword analysis,and reference analysis,etc.RESULTS A total of 9210 articles were included,and the annual publication volume in this field showed a steady growth trend.The United States had the highest number of publications and the highest H-index.The United States also had the most research institutions and the strongest research capacity.The global hot journals were primarily diabetes professional journals but also included journals related to nutrition,endocrinology,and metabolism.Keyword analysis showed that research related to endothelial dysfunction,exposure risk,cardiac metabolic risk,changes in gut microbiota,the impact on comorbidities and outcomes,etc.,were emerging keywords.They have maintained their popularity in this field,suggesting that these areas have garnered significant research interest in recent years.CONCLUSION Pediatric and adolescent T2DM is increasingly drawing global attention,with genes,behaviors,environmental factors,and multisystemic interventions potentially emerging as future research hot spots.

Analysis of Visualization Mapping in the Field of Brand Community Based on Cite Space Ⅲ

Taking 887 relevant papers in the Web of Science database as the analysis object,and with the help of Citespace III software developed by Chen Chaomei s team,this study makes a visual mapping analysis of the brand community field from the perspective of research hotspots,cooperation between countries(regions)and institutions,and co-citation networks.The study has found that research hotspots in this field focus on brand community,community,consumer,internet,model,loyalty,word-of-mouth,impact and trust.The United States has absolute strength in leading the research frontier of this field,and the degree of cooperation within the country is higher than that of international cooperation.The core papers in this field were published in from authoritative journals in the field of management.Through the analysis of the core papers,readers can further understand the development context of the brand community field.

Visualization Analysis of Researches on Digital Storytelling Based on Mapping Knowledge Domain

The development of the information age and globalization has challenged the training of technical talents in the 21st century, and the information media and technical skills are becoming increasingly important. As a creative sharing form of multimedia, the digital storytelling is being concerned by more and more educators because of its discipline applicability and media technology enhancing ability. In this study, the information visualization software, i.e. CiteSpace was applied to visualize and analyze the researches on digital storytelling from the aspects of key articles and citation hotspots, and make a review on the research status of the digital storytelling in the education fields, such as promoting language learning, and helping students develop the 21 st century skills.

Visualization Analysis for China’s Business Schools Accreditation Studies Based on Knowledge Mapping

The aim of the paper is to identify and explore leading thematic areas within the research field related to Business Schools Accreditation in China.Based on data from the China National Knowledge Infrastructure(CNKI)database,the keywords frequency was analyzed,and the theory of mapping knowledge domain was used to visualize the keywords co-occurrence network to make further research of the heated issues.The findings indicate that the research scope involved in business school accreditation research is broad,and research content focus on teaching,management,talent cultivation.According to the results of keywords co-occurrence analysis of different stages,AACSB,AACSB Accreditation,international accreditation,business school,AOL,internationalization,accreditation are the most important issues to business school research in China,given their position and role in the research network.The paper generates the added value mainly from the point of view of theory development.

Visualization Analysis of Research on Student Classroom Engagement by Knowledge Mapping

The aim of the paper is to identify and explore leading development situation within the research field related to student classroom engagement.Using knowledge mapping tools,this paper conducted the visualization analysis on internationalliteraturein relation to student classroom engagementfrom the Web of Science databases.By combination of the statistical data and visualizationmapping,this paper studied on the research relationship networks and status for the co-authors'countries/institutions,co-citation documents/journalsand co-occurring keywordsbased on the sample datafromliterature.The results indicate that the number ofpublished papershasincreased obviously and expanded graduallysince 2000.Main countries and institutions of researches were the UnitedStates,Australia,United Kingdom,Canada and China in order,and these countries had intense collaboration.The theoretical basis of the research come from education and teaching,psychology.The hotspots of researches fromthe global literature coveredvarious fields including achievement,motivation,behavior,education,performance.As a result of the multidisciplinary integration,many relevant internationalresearch constantly expanded the research scopes,which promoted the combination and development of theories.

Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

APPLICATION OF MAP AND FILE INFORMATIONVISUALIZATION SYSTEM TO COMPREHENSIVEDIVISION OF NATURAL DISASTERS- Takingthe ChangjiangValleg as an Example

ABSTRACT: This paper generalizes the makeup and forming dynamic mechanism of natural disaster systems, principles and methods of comprehensive division of natural disasters, as well as structure, function and up-build routes of map and file information visualization system (MFIVS). Taking the Changjiang(Yangtze) Valley as an example, on the basis of revealing up the integrated mechanism on the formations of its natural disasters and its distributing law, thereafter, the paper relies on the MFIVS technique, adopts two top-down and bottom-up approaches to study a comprehensive division of natural disasters. It is relatively objective and precise that the required division results include three natural disaster sections and nine natural disaster sub-sections, which can not only provide a scientific basis for utilizing natural resources and controlling natural disaster and environmental degradation, but also be illuminated to a concise, practical and effective technique on comprehensive division.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

Semi-Direct Visual Odometry and Mapping System with RGB-D Camera

In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.

Visualization Analysis of Multi-Domain Access Control Policy Integration Based on Tree-Maps and Semantic Substrates

The complexity of multi-domain access control policy integration makes it difficult to understand and manage the policy conflict information. The policy information visualization technology can express the logical relation of the complex information intuitively which can effectively improve the management ability of the multi-domain policy integration. Based on the role-based access control model, this paper proposed two policy analyzing methods on the separated domain statistical information of multi-domain policy integration conflicts and the policy element levels of inter-domain and element mapping of cross-domain respectively. In addition, the corresponding visualization tool is developed. We use the tree-maps algorithm to statistically analyze quantity and type of the policy integration conflicts. On that basis, the semantic substrates algorithm is applied to concretely analyze the policy element levels of inter-domain and role and permission mapping of cross-domain. Experimental result shows tree-maps and semantic substrates can effectively analyze the conflicts of multi-domain policy integration and have a good application value.

Research on Ubiquitous Map Visualization for Ternary Space

In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” with the extension of its object space, expression space and information source, which challenges the theory of cartographic visualization. This paper discusses the ubiquitous map visualization from the object content and expression form. Oriented to the ternary space, it divides the object dimension of ubiquitous map visualization and analyzes the expression characteristics of ubiquitous map visualization. Based on that, it constructs the variable system, symbol system and method system of ubiquitous map visualization. With three cases of the metro roadmap, the tag map, and the three-dimensional (3D) city map, the application of the proposed content is explained to illustrate its effectiveness. The research in this paper is expected to further enrich the theoretical basis of cartographic visualization and provide theoretical support for the expression and application of ubiquitous map visualization.

Visual Information Processing Based on Qualitative Mapping

Visual information processing is not only an important research direction in fields of psychology,neuroscience and artificial intelligence etc,but also the research base on biological recognition theory and technology realization.Visual information processing in existence,e.g.visual information processing facing to nerve calculation,visual information processing using substance shape distilling and wavelet under high yawp,ANN visual information processing and etc,are very complex in comparison.Using qualitative Mapping,this text describes the specific attributes in the course of visual information processing and the results are more brief and straightforward.So the software program of vision recognition is probably easier to realize.

基于光流法算法的Visual Map快速建立方法

Visual Map是一个含有丰富位置信息的图像数据库,数据库中每一幅图片或图片的特征在存储时会加入相应的位置信息.室内定位的性能与Visual Map图片的数量有关.建立庞大的图片数据库能够使得定位结果更加准确,但是花费时间成本会更大.针对这个问题,本文提出了使用光流法算法来建立图片数据库Visual Map.针对光流法用于室内图像的计算会受到光线明暗不同的影响以及相机转向会产生横向偏移的问题,本文对光流法进行了改进,并使用改进后的光流法算法对摄像机采集的图像序列进行计算,得到摄像机的自身位移,从而得到每一幅图片的对应的地理位置信息.实验结果表明,利用使用光流法快速建立的Visual Map进行室内定位,误差小于1米的概率是26%,误差小于2米的概率是70%.与传统的视觉室内定位法相比,定位精度虽然略有降低,但建立图像数据库所需时间消耗大大减少.相比于视频流快速建立Visual Map方法,定位效果相当,建立Visual Map所需的设备更少,要求更加宽松.利用光流法算法快速建立Visual Map能够很好的应用于室内视觉定位系统,特别是应用于大型场所以及室内场景多变化的场所.

基于Visual C++环境的MapObjects组件开发

分析了MapObjects组件的特点和功能,介绍了在VisualC++环境下基于MapObjects组件开发地理信息系统的方法,并结合一个车辆定位监控系统探讨了图层处理、实时跟踪、地理要素拾取、图层标注、交互式提示等功能的具体实现,给出了该系统的设计原理、组成、功能、主要技术特点及其应用。实践表明,系统编程体现了地图组件处理地理信息的优势和面向对象编程的灵活性。

基于图像关键帧的Visual-Depth Map建立方法

对于室内视觉定位系统,需要在离线阶段建立Visual Map数据库用来存储图像信息,在线阶段用户通过与Visual Map数据库进行比对来完成用户位置的估计.离线阶段建立的数据库可以采用逐点采样或视频流采样的方式.但是无论何种方式,考虑到数据库中图像信息的相似性,传统方式建立的数据库中存储图像有较多冗余,导致增加了在线阶段的定位时间开销.因此,本文根据Visual Map中的相邻图像间的相似性,提出了一种基于图像关键帧的Visual-Depth Map建立方法,有效地减少了离线数据库的规模.在离线阶段,本文使用Kinect传感器同时获得图像信息和深度信息;然后,通过基于图像相似度的图像关键帧算法对原始图像序列进行筛选,得到关键帧序列,从而实现Visual-Depth Map的建立.在线阶段,用户可以直接输入查询图像与Visual-Depth Map中的图像序列进行检索匹配,找到相似度较高的匹配图像,再通过EPnP算法进行2D-3D的位姿估计,完成用户位置的计算.实验证明,本文所提方法可以在保证较高定位精度的前提下,有效减少离线数据库规模,降低在线阶段的定位时间开销.