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.

Bibliometric analysis and mapping knowledge domain of pterygium:2000-2019

AIM:To track the knowledge structure,topics in focus,and trends in emerging research in pterygium in the past 20 y.METHODS:Base on the Web of Science Core Collection(Wo SCC),studies related to pterygium in the past 20 y from 2000-2019 have been included.With the help of VOSviewer software,a knowledge map was constructed and the distribution of countries,institutions,journals,and authors in the field of pterygium noted.Meanwhile,using cocitation analysis of references and co-occurrence analysis of keywords,we identified basis and hotspots,thereby obtaining an overview of this field.RESULTS:The search retrieved 1516 publications from Wo SCC on pterygium published between 2000 and 2019.In the past two decades,the annual number of publications is on the rise and fluctuated a little.Most productive institutions are from Singapore but the most prolific and active country is the United States.Journal Cornea published the most articles and Coroneo MT contributed the most publications on pterygium.From cooccurrence analysis,the keywords formed 3 clusters:1)surgical therapeutic techniques and adjuvant of pterygium,2)occurrence process and pathogenesis of pterygium,and 3)epidemiology,and etiology of pterygium formation.These three clusters were consistent with the clustering in co-citation analysis,in which Cluster 1 contained the most references(74 publications,47.74%),Cluster 2 contained 53 publications,accounting for 34.19%,and Cluster 3 focused on epidemiology with 18.06%of total 155 cocitation publications.CONCLUSION:This study demonstrates that the research of pterygium is gradually attracting the attention of scholars and researchers.The interaction between authors,institutions,and countries is lack of.Even though,the research hotspot,distribution,and research status in pterygium in this study could provide valuable information for scholars and researchers.

Large-Scale Ontology Development and Agricultural Application Based on Knowledge Domain Framework

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management- the knowledge domain framework （KDF）, and introduces an integrated development environment （IDE） named large-scale ontology development environment （LODE）, which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the described briefly. principle of knowledge domain framework and LODE was

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.

RepDNet:A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.

A Citespace Mapping-knowledge-domain Analysis of SLA from the Lin⁃guistic Perspective in China

Research papers in the field of SLA published between 2009 and 2019 are analyzed in terms of research status of domes⁃tic SLA researchers,research institutions,research frontiers and hotspots in the paper,and maps the knowledge domains of SLA re⁃searches.The data are retrieved from 10 core journals of linguistics via the CNKI journal database.By means of CiteSpace 5.3,an analysis of the overall trend of studies on SLA in China is made.

Incorporating Domain Knowledge into Data Mining Process:An Ontology Based Framework

With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.

Extracting mining subsidence land from remote sensing images based on domain knowledge

Extracting mining subsidence land from RS images is one of important research contents for environment monitoring in mining area. The accuracy of traditional extracting models based on spectral features is low. In order to extract subsidence land from RS images with high accuracy, some domain knowledge should be imported and new models should be proposed. This paper, in terms of the disadvantage of traditional extracting models, imports domain knowledge from practice and experience, converts semantic knowledge into digital information, and proposes a new model for the specific task. By selecting Luan mining area as study area, this new model is tested based on GIS and related knowledge. The result shows that the proposed method is more pre- cise than traditional methods and can satisfy the demands of land subsidence monitoring in mining area.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph

With the rise of open-source software,the social development paradigm occupies an indispensable position in the current software development process.This paper puts forward a variant of the PageRank algorithm to build the importance assessment model,which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components.The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph.According to the three dimensions of the Java open-source project’s project influence,project activity and project popularity,the project is measured.A modified PageRank algorithm is proposed to construct the importance evaluation model.Thereby providing quantifiable importance evaluation indicators for new Java projects based on or components of Java open-source projects.This article evaluates the importance of 4512 Java open-source projects obtained on GitHub and has a good effect.

A Domain Question Answering Algorithm Based on the Contrastive Language-Image Pretraining Mechanism

Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.

Intelligent Multilevel Knowledge Acquisition System for Product Design and Its Implementation

The characteristics of design process, design object and domain knowledge of complex product are analyzed. A kind of knowledge representation schema based on integrated generalized rule is stated. An AND-OR tree based model of concept for domain knowledge is set up. The strategy of multilevel domain knowledge acquisition based on the model is presented. The intelligent multilevel knowledge acquisition system (IMKAS) for product design is developed, and it is applied in the intelligent decision support system of concept design of complex product.

Knowledge Graph Extension Based on Crowdsourcing in Textile and Clothing Field

Generally,knowledge extraction technology is used to obtain nodes and relationships of unstructured data and structured data,and then the data fuse with the original knowledge graph to achieve the extension of the knowledge graph.Because the concepts and knowledge structures expressed on the Internet have problems of multi-source heterogeneity and low accuracy,it is usually difficult to achieve a good effect simply by using knowledge extraction technology.Considering that domain knowledge is highly dependent on the relevant expert knowledge,the method of this paper try to expand the domain knowledge through the crowdsourcing method.The method split the domain knowledge system into subgraph of knowledge according to corresponding concept,form subtasks with moderate granularity,and use the crowdsourcing technology for the acquisition and integration of knowledge subgraph to improve the knowledge system.

Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity relationships.Here,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal superalloys.Firstly,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic standing.Next,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility analysis.Following this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in superalloys.The extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning

A mathematical formula of high physical interpretation,and accurate prediction and large generaliza-tion power is highly desirable for science,technology and engineering.In this study,we performed a domain knowledge-guided machine learning to discover high interpretive formula describing the high-temperature oxidation behavior of FeCrAlCoNi-based high entropy alloys(HEAs).The domain knowledge suggests that the exposure time dependent and thermally activated oxidation behavior can be described by the synergy formula of power law multiplying Arrhenius equation.The pre-factor,time exponent(m),and activation energy(Q)are dependent on the chemical compositions of eight elements in the FeCrAlCoNi-based HEAs.The Tree-Classifier for Linear Regression(TCLR)algorithm utilizes the two exper-imental features of exposure time(t)and temperature(T)to extract the spectrums of activation energy(Q)and time exponent(m)from the complex and high dimensional feature space,which automatically gives the spectrum of pre-factor.The three spectrums are assembled by using the element features,which leads to a general and interpretive formula with high prediction accuracy of the determination coefficient R^(2)=0.971.The role of each chemical element in the high-temperature oxidation behavior is analytically illustrated in the three spectrums,thereby the discovered interpretative formula provides a guidance to the inverse design of HEAs against high-temperature oxidation.The present work demonstrates the sig-nificance of domain knowledge in the development of materials informatics.

Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

The meta-heuristic algorithm with local search is an excellent choice for the job-shop scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification

Deep learning provides an effective way for automatic classification of cardiac arrhythmias,but in clinical decisionmaking,pure data-driven methods working as black-boxes may lead to unsatisfactory results.A promising solution is combining domain knowledge with deep learning.This paper develops a flexible and extensible framework for integrating domain knowledge with a deep neural network.The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label,and a knowledge module to guarantee consistency with the domain knowledge.These two components are trained interactively to bring the best of both worlds.The experiments show that the domain knowledge is valuable in refining the neural network prediction and thus improves accuracy.

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.

An Ontology-Based Question Answering System for University Admissions Advising

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in manyfields.However,these systems depend on learning methods and training data.Therefore,it is necessary to prepare such a good dataset,but it is not an easy job.An ontol-ogy-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efficient responses automatically.A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generat-ing answers to users’questions.Two experiments are carried out to test how the system reacts to different questions.Thefirst experiment examines questions made from some templates,and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.