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The problem of gender disparity in computer science and engineering has persisted despite longstanding efforts made in higher education. This study drew data from ten female computer science and engineering faculty in China and Italy to demonstrate the gender-specific challenges they face. The researcher applied positioning theory to analyse female faculty's motherhood experiences. Findings suggest that the dual mother-and-educator role conflict in the intersectional space of work and family can be both a burden and an asset to female faculty. As a burden, the sociocultural expectations of the mother's role drive females to take on primary childcare responsibilities. It leads to significant barriers to female faculty's career advancements. As an asset, the study finds that the female faculty's motherhood experience allowed them to generate new insights into their identities as educators and teaching practices. Their learning from motherhood prompts them to reflect and gain an appreciation of their educator identity. Such realisation enables them to improve their teaching practices and interactions with students. The study offers policy recommendations for higher education institutions to support female faculty navigating the dual roles of mother and educator. Finally, the researcher discusses the limitations and directions for future study.

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

ESP textbook plays an important role in facilitating students to develop their profession-related language skills. However, ESP textbooks published in China are less developed and often criticized as ignoring the training of language skills. This research aims to reveal the specific problems of China's ESP textbooks by conducting a multiple-case study. Three ESP textbooks used by ESP courses participants from G University in China were selected: "Computer Professional English Course" "Advertising English" and "Logistics English". The research investigated their performance focusing on six aspects: coverage of language skills, text features, coverage of discourse functions, recycling, organization and difficulty. The content was analyzed by three different corpus tools. It is found that the three textbooks place too much emphasis on reading and vocabulary, lacking the training of listening skill, speaking skill, as well as the delivery of certain learning strategies. All three textbooks involve a wide range of discourse functions. The texts are informative academic texts, but organized by subject matter only, rather than a synthesis of subject matter, language points and language skills. There is scarce recycling of language points in two of the books and texts through all of them do not indicate a rising difficulty. It is concluded that the drawbacks of the three ESP textbooks far outweigh their merits. By uncovering problems of three ESP textbooks in China the research provides useful reference for future ESP textbook compilation.

Computational thinking (CT) plays a vital role in the fields of science, technology, engineering and mathematics (STEM). However, whether students' learning attitude towards STEM is related to their CT skills remains unknown. Two studies were conducted to address this knowledge gap. In Study 1, we validated a newly developed STEM learning attitude scale among a sample of Chinese primary school students (N = 489). Exploratory and confirmatory factor analysis results revealed that the scale which consisted of three factors (mathematics, science and information technology) could sufficiently measure primary school students' STEM learning attitude in the current sample. In Study 2, we explored the association between students' STEM learning attitude and their CT skills. Evidence revealed that learning attitude towards STEM significantly predicted CT skills. We also found that girls had a more positive learning attitude towards STEM than boys, and the fourth grade might be the key period for the development of CT skills. Implications for promoting primary school students' STEM learning and CT skills were also discussed.

Microteaching techniques have been used for teacher training since the mid 1960s. Despite its usefulness, as affirmed by pre-service teachers, in-service teachers, and graduate teaching assistants (GTAs), there are numerous criticisms on the shortcomings of microteaching activities. Specifically, it (a) oversimplifies the classroom learning and teaching nature, (b) encourages skill modelling on one or only few technique(s) demonstrated during training sessions, (c) involves costly human and technical resources for implementation, and most critically, (d) fails to provide instant and reusable feedback to improve classroom teaching skills. Addressing the inadequacies of traditional microteaching practice, this paper proposes an Extreme-Teaching-2 (XT[superscript 2]) framework based on the computer science literature. Originating from Extreme Programming (XP) methodology, XT[superscript 2] preserves the agility on teaching-feedback-teaching cycles with heavy peer and instructor involvement. With strong technological support, XT[superscript 2] allows specific, personalized, incremental, and constructive formative feedback to be given by peers and instructors during and after two classroom observation sessions. Through the XT[superscript 2] framework, teacher candidates are able to reuse feedback instantly (feedforward) and rapidly improve (a) confidence in identifying their weaknesses and strengths, (b) and their facilitating skills, while the administrative workload on instructors is significantly reduced.

Practical abilities are important for students from majors including Computer Science and Engineering, and Electrical Engineering. Along with the popularity of ACM International Collegiate Programming Contest (ACM/ICPC) and other programming contests, online judge (OJ) websites achieve rapid development, thus providing a new kind of programming practice, i.e. online practice. Due to fair and timely feedback results from OJ websites, online practice outperforms traditional programming practice. In order to promote students' practical abilities in programming and algorithm designing, this article presents a novel teaching idea, online judge & practice oriented teaching (OJPOT). OJPOT is applied to "Programming Foundation" course. OJPOT cultivates students' practical abilities through various kinds of programming practice, such as programming contests, online practice and course project. To verify the effectiveness of this novel teaching idea, this study conducts empirical research. The experimental results show that OJPOT works effectively in enhancing students' practical abilities compared with the traditional teaching idea.

Computational thinking (CT) is a fundamental skill for students, and assessment is a critical factor in education. However, there is a lack of effective approaches to CT assessment. Therefore, we designed the Three-Dimensional Integrated Assessment (TDIA) framework in this article. The TDIA has two aims: one was to integrate three dimensions (directionality, openness, and process) into the design of effective assessment tasks; and the other was to assess comprehensively the three dimensions of CT including computational concepts, practices, and perspectives. Guided by the TDIA framework, we designed three pairs of tasks: closed forward tasks and closed reverse tasks, semiopen forward tasks and semiopen reverse tasks, and open tasks with a creative design report and open tasks without a creative design report. To further confirm each task's applicability and its advantages and disadvantages, we conducted a test experiment at the end of the autumn semester in 2014 in a primary school for 3 weeks. The results indicated that (a) the reverse tasks were not more superior than the forward tasks; (b) the semi-open tasks and the open tasks were more effective than the closed tasks, and the semi-open tasks had higher difficulty and discrimination than the others; (c) the self-reports provided a helpful function for learning diagnosis and guidance; (d) the scores had no significant difference between the schoolboys and the schoolgirls in all six tasks; and (e) the six tasks' difficulty and discrimination were all acceptable, and the semi-open tasks had higher difficulty and discrimination than the others. To effectively apply them, the following suggestions for teachers to design computational tasks are proposed: motivating students' interest and enthusiasm, incorporating semi-finished artifacts, involving learning diagnosis and guidance, and including multiple types of tasks in an assessment.

Purpose: The declining participation of young American females in computing fields negatively impacts diversity in the computing-related workforce. While computing in China is also dominated by men, career prospects are positively perceived by Chinese women. The purpose of this paper is to investigate both countries' female gender perceptions toward computing and two influencing psychological factors, computer anxiety and computer self-efficacy (CSE). To better understand American females' perception toward computing, these factors are compared to those of Chinese women. Design/methodology/approach: The paper tried to understand gender perception toward computing, which is believed to be one factor influencing female participation in computing. The paper examined 137 American female students and 145 Chinese female students. All students were non-IT-related business majors. The survey contained the following scales: gender typing scale, CSE scale, and computer anxiety rating scale. The paper used the model comparison technique in structural equation modeling to develop the proposed model demonstrating the relationship among the three psychological factors. Findings: It is found that American females perceive that computing is for females, but Chinese females perceive that computing is for both males and females. Computer anxiety has a direct influence on gender perception toward computing for both groups. CSE, however, does not have a direct influence on gender perception toward computing for both groups. CSE also has a direct relationship with computer anxiety. The extent to which each factor influences other factors is different between both groups and this reveals interesting implications. Social implications: The authors believe that to properly encourage female participation in computing, it is important to emphasize two areas in education. In order to lower computer anxiety and increase CSE, educators need to promote basic computer skills and provide a fundamental knowledge of computers and information technologies. A more positive perception by females could be created through a stronger awareness of female roles in computing. Classroom discussions including more examples of positive female role models in computing is necessary. Female professionals need to support each other and encourage young females to pursue computing-related education fields and careers. Originality/value: There is an important need to understand the decline of American female participation in computing. Few researchers have investigated gender perception toward computing with computer anxiety and CSE. Furthermore, a model explaining the relationship of the three factors previously had not been presented. This paper not only investigated how computer anxiety and CSE influences gender perception toward computing, but also compared these results between American and Chinese females. A suggested guideline to improve gender perception of American females was developed. (Contains 1 note, 6 tables, and 2 figures.)

The computer science classes in China's institutions of higher education often have large numbers of students. In addition, many institutions offer "blended" classes that include both on-campus and online students. These large blended classrooms have long suffered from a lack of interactivity. Many online classes simply provide recorded instructor lectures to which distance students listen after downloading. This format only reinforces the negative effects of passive nonparticipatory learning. At a major university in Shanghai, researchers and developers actively seek technological interventions that can greatly increase interactivity in blended classes. They have developed a cutting-edge mobile learning system that can deliver live broadcasts of real-time classroom teaching to online students with mobile devices. Their system allows students to customize their means of content-reception, based on when and where the students are tuning into the broadcast. The system also supports short text-messaging and instant polls. Through these features, students can ask questions and make suggestions in real time, and the instructor can respond immediately. This paper describes this system in detail and also reports results from a formal implementation of the system with a blended classroom of 562 students (of whom 90% were online). (Contains 5 tables and 2 figures.)

Purpose: The purpose of this paper is to consider the under-representation of women in computing, information technology (IT) and information systems in most Western countries. This under-representation can only be detrimental to society. Design/methodology/approach: The authors discuss recently published studies on the under-representation of women in this field, and the lack of career progression for women, particularly in positions of senior responsibility. The results of a survey conducted in the UK, Greece, Malaysia and China are presented, and similarities as well as differences are identified. Malaysia and China are included as the under-representation is much less in the Asian countries. Findings: The authors identify the main reasons for the under-representation and lack of career progression, and study the effect of national culture on the formation of strategy and of practice in the four countries. Research limitations/implications: The survey respondents are mainly female students studying computing/IT at degree level. In the next phase of the research, the survey will include both male and female students, lecturers and employees within the IT sector. The research will be extended into a longitudinal study, in order to monitor trends. Originality/value: The UK Government is currently undertaking a number of initiatives to improve the representation of women in information and communication technologies. For those initiatives to be successful, the cultural issues identified in the paper need to be considered. (Contains 7 tables.)

Discussion of cultural differences and their effects on how new technology is viewed in developed and developing countries focuses on a study that examined the conceptions of Swedish and Chinese college students concerning computers. The attitudes of psychology students and computer science students are compared, and implications for computer implementation are discussed. (11 references) (LRW)

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and ProQuest for review. The findings reveal a sharp increase in recent years in the body of research into educational knowledge graphs which was mainly conducted from institutions in China. Most of the relevant research work adopted a quantitative method, such as performance evaluation, user surveys, and controlled experiments, to assess the effectiveness of knowledge graph approaches. The findings also suggest that knowledge graph approaches were primarily researched and implemented in higher education institutions, with a focus on computer science, mathematics, and engineering. The most frequently addressed objectives included enhancing knowledge representation and providing personal learning recommendations, and the most common applications were concept instruction and educational recommendations. Diverse data resources, such as course materials, student learning behaviours, and online encyclopaedia, were processed to implement knowledge graph approaches in different scenarios. Relevant technical means employed for the implementation of knowledge graphs dealt with the purposes of building knowledge ontology, achieving recommendations, and creating knowledge graphs. Various pedagogies such as personalised learning and collaborative learning are supported by the knowledge graph approaches. The findings also identified key limitations in the relevant work, including insufficient information for knowledge graph construction, difficulty in extending applications across subject areas, the restricted scale and scope of data resources, and the lack of comprehensive user feedback and evaluation processes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

The rise of the Internet and social media (i.e. reviews, forum discussions, blogs and social networks) constituted an interesting source to detect user opinion trends. This study examines the global publication output on opinion mining and sentiment analysis from documents published in 2000 to 2020. Bibliometric indicators on the trends, most cited papers, authors, institutions, countries, funding agencies and research subject areas were independently screened and analysed using bibliometrix package in R. A total of 7603 eligible documents were identified from 2000 to 2020. The total number of citations for all publications was 129,251, with an average of 17.0 citations per publication. About 14,629 authors wrote those documents with 1.93 authors per document and a collaboration index of 1.98. The most prolific author was Cambria Erik, with 47 publications and h-index of 42. The leading countries for research were China with n = 824, India with n = 576 and the United States with n = 244 publications. Lecture Notes in Computer Science proceedings was the top-ranked venue for publications with n = 434, h-index of 32 and 4598 total citation scores. National Natural Science Foundation of China was the top-ranked funding agency for research, and most of the publications were computer science (n = 6320) documents. The study provides an in-depth assessment of the landmark of the hot research topic and acknowledges the contribution of the most productive and active authors across different countries in the world. In addition, the findings could support the younger scholars in their future research direction and improve the efficiency in potential future research collaborations and projects. [ABSTRACT FROM AUTHOR]

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer‐related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China. [ABSTRACT FROM AUTHOR]

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments. [ABSTRACT FROM AUTHOR]

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme. [ABSTRACT FROM AUTHOR]

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This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

This research paper utilizes a systematic literature review methodology, drawing from the Scopus database and VOSViewer analysis, to investigate the current landscape of 6G telecommunications research and its potential implications. Our findings indicate that 6G technology is poised to revolutionize wireless communication, with significant ramifications for key sectors such as autonomous vehicles, transportation, and manufacturing, ushering in an era of unprecedented connectivity and operational efficiency. A notable trend is the growing interest among researchers in critical technical aspects, including bandwidth, latency, signal processing, spectrum management, spectral efficiency, Intelligent Reflecting Surfaces (IRS), meta-communication, and beamforming. Geographically, China and the United States lead in 6G research, reflecting a global competition to harness its transformative potential. Most researchers driving innovation in this field have engineering and computer science backgrounds. We anticipate the widespread deployment of 6G technology by 2030, with ongoing research efforts focusing on diverse implementation techniques across industries. Expected advancements encompass Machine-to-Machine (M2M) communication, Massive Multiple Input Multiple Output (mMIMO) systems, IRS utilization, novel waveform technologies, meta-communication, Orthogonal Time Frequency Space (OTFS) modulation, Generalized Frequency Division Multiplexing (GFDM), and the integration of Low-Density Parity-Check (LDPC) codes, all poised to enhance network speed and reduce latency significantly. Furthermore, the Metaverse is expected to experience exponential growth, driven by the capabilities of the 6G network. This paper contributes original insights to the burgeoning field of 6G telecommunications, offering a comprehensive overview of its current status and potential implications across various domains, particularly in management. [ABSTRACT FROM AUTHOR]

WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. “Top Stories” is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends. Specifically, when a user reads an article by opening it, the “click” behavior is private. Moreover, if the user clicks the “wow” button, (only) her/his direct connections will be aware of this action/preference. Based on the unique WeChat data, we aim to understand user preferences and “wow” diffusion in Top Stories at different levels. We have made some interesting discoveries. For instance, the “wow” probability of one user is negatively correlated with the number of connected components that are formed by her/his active friends, but the click probability is the opposite. We further study to what extent users’ “wow” and click behavior can be predicted from their social connections. To address this problem, we present a hierarchical graph representation learning based model DiffuseGNN, which is capable of capturing the structure-based social observations discovered above. Our experiments show that the proposed method can significantly improve the prediction performance compared with alternative methods. [ABSTRACT FROM AUTHOR]

This study was carried out to perform bibliometric analysis on e-logistics, which could not be found to be examined before in the literature, and create bibliometric networks through visualizations. The VOSviewer (ver. 1.6.17) program was used in the creation and visualization of bibliometric networks. According to the analysis results of this study, most publications in the field of e-logistics were published in 2004, the languages of the publications were mostly English, the country with the most publications was China, the most cited authors were Feng-Cheng Tung, Su-Chao Chang, and Chi-Min Chou, 2008 and according to Web of Science (WoS) categories, the most publications were understood to be in the computer science information systems category. According to the results of the bibliometric analysis using the creation and visualization of bibliometric networks, the most cited author was Ulieru, M. based on the co-citation author analysis, the most cited source was the International Journal of Physical Distribution & Logistics Management based on the co-citation source analysis, the country with the most documents was China based on the bibliographic coupling country analysis, and the most repeated keywords were e-logistics, e-commerce, logistics, supply chain management and reverse logistics based on the keyword co-occurrence analysis. [ABSTRACT FROM AUTHOR]

Summary: Art painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi‐source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved. [ABSTRACT FROM AUTHOR]

The financial-technology industry has recently attracted the attention of many sectors. The financial-technology industry designs new and unusual technological financial services in many areas. It combines technology with finance and provides an alternative to the traditional financial system. In the scope of this study, 636 publications were obtained from Scopus. Various tools, such as Microsoft Excel for frequency analysis, and VOSviewer for data visualization, were used. The open-source codes used for bibliometric analysis through the R Studio program were developed by the authors and used for citation-metrics analysis. The main aim of this study was to find out the most influential studies and authors and to reveal the distributions and impacts of publications in the FinTech area between 2015 and 2021 from the Scopus database. The results indicate that the most influential journal is Sustainability Switzerland, and the most cited author is Gomber et al. Additionally, Rabbani has the most publications, while China has emerged as the most productive country. On the other hand, this study found that FinTech research clustered in four areas. These areas are computer science, business management, economics, and social sciences. This FinTech study examines financial services, financial access, and financial technology, where FinTech is at the center. It also focuses on cryptocurrency, bitcoin, and smart contracts where the blockchain is at the center. The results reveal a systematic map of existing studies. Further, the study plays a guiding role in future research. [ABSTRACT FROM AUTHOR]

Computational thinking (CT), which is a cognitive skill used to solve problems with computational solutions, has drawn increasing attention among researchers and practitioners due to the growing recognition of CT competence as a 21st century skill. Collaboration is commonly integrated into CT education to facilitate novice learning, but there is inadequate knowledge regarding the influences of collaboration in CT education. This meta‐analysis examined the overall effects on the cognitive, social and affective competencies of collaborative versus individual problem solving in CT through programming. We identified 33 publications involving 4717 learners, which allowed for 220 effect size comparisons. We found a medium effect size (Hedges' g = 0.562; p < 0.001) in favour of collaborative problem solving on cognitive learning outcomes and a small effect size (Hedges' g = 0.316; p < 0.01) on affective learning outcomes using a random‐effects model. Categorical moderator analysis revealed the moderating effects of educational level, programming environment, study duration, grouping method and group size. The competency model that was generated from the synthesized literature on collaborative computational problem solving yielded insights into the learning design and assessment of relevant activities. Practitioner notesWhat is already known about the topic?Although professional programming usually requires collaboration to accomplish common goals in the work environment, programming is predominantly taught on an individual basis in educational settings.Some intervention studies have reported inconsistent learning outcomes (positive and negative) from integrating collaborative problem solving (CPS) into programming education.There is a need for further scrutiny and exploration of the role of collaboration in the process of learning to program.What does this paper add?Our meta‐analysis showed that CPS is an effective pedagogical tool for developing students' cognitive, affective and social competencies.The moderator analysis revealed that CPS in programming has cognitive benefits irrespective of grouping method, group size and educational levels; however, there is a need for caution in regard to designing collaborative problem‐solving activities for different programming environments and durations.The model generated from the literature synthesis can yield insights into better assessing and designing collaborative problem‐solving activities.Implications for practice and/or policy?Integrating CPS into programming education can prepare students with 21st century skills.Social and affective competencies are crucial to students' learning capacity, which is an issue that needs to be better assessed.Future studies could look more rigorously into the effects of CPS in visual programming and robotics programming among younger age groups, such as primary or middle school students. [ABSTRACT FROM AUTHOR]

This study's major objective is to examine the impact of human-computer interaction on innovation and sports psychology. This research study utilized questions linked to independent and dependent variables based on primary data analysis to assess the research. This research study on human-computer interaction conducted in China also discusses innovation and sports psychology. Human-computer interaction is the primary independent variable; it encompasses computer science, cognitive science, and human factor engineering, all regarded as independent variables. Innovation and sports psychology are viewed as dependent factors. This research is based on original data from assessing the outcome with intelligent PLS software and generating useful results. The correlation, R-square, F-square values, and weighted average analysis illustrate the intelligent PLS Algorithm model. The overall findings indicate that human-computer interaction has a favorable and significant effect on innovation and sports psychology. Computer science has direct effects on sports psychology as well. Cognitive science has a close relationship with innovation and sports psychology. [ABSTRACT FROM AUTHOR]

The aim of this study was to reveal hotspots and frontiers of computer-assisted English learning (CAEL) studies indexed by EI Compendex database from 2001 to 2020 via bibliometric analysis. The publication output has exponentially grown in the past two decades and is likely to progress in the next several years. China occupied the leading position while Lecture Notes in Computer Science was the most prolific journal, and Deyi Xiong was the most productive author. Keyword analysis was assisted by VOSviewer software. Our results show that "computer aided instruction", "computer aided language translation" and "learning systems" were the most frequently used keywords in documents. CAEL studies were mainly conducted from five dimensions (technology, learners, teaching, English acquisition and testing). The findings of this study have implications for English language instructors. Teaching methods and modes should be adjusted according to technological development. [ABSTRACT FROM AUTHOR]

This study examines the script crisis that emerged in China during the late 1970s and early 1980s, when the old ideology of state phonocentrism and the new wave of computer science demanded the abandoning of Chinese characters altogether. Under the project of establishing new human-machine interfaces, national debates over such technical issues as the statistical properties of Chinese according to Claude Shannon's information theory took place in various venues, challenging the conventional language ideologies. As linguists and engineers rallied to collaborate on the scriptal modernization project on the basis of the discursive space opened up by critiques of the 1977 Simplification Scheme, efforts to integrate Chinese script into computer data-storage formats ended up destabilizing the understanding of Chinese writing that conventionally takes "zi" as opposed to "ci" as its basic unit. The shifting foci of script reform discourses during the period demonstrate that the rise of information technology and resumed debates over script reform jointly instigated a retheorization of written Chinese. [ABSTRACT FROM AUTHOR]

The study provides a quantitative and qualitative description of global research in the domain of quantum machine learning (QML) as a way to understand the status of global research in the subject at the global, national, institutional, and individual author level. The data for the study was sourced from the Scopus database for the period 1999-2020. The study analyzed global research output (1374 publications) and global citations (22434 citations) to measure research productivity and performance on metrics. In addition, the study carried out bibliometric mapping of the literature to visually represent network relationship between key countries, institutions, authors, and significant keyword in QML research. The study finds that the USA and China lead the world ranking in QML research, accounting for 32.46% and 22.56% share respectively in the global output. The top 25 global organizations and authors lead with 35.52% and 16.59% global share respectively. The study also tracks key research areas, key global players, most significant keywords, and most productive source journals. The study observes that QML research is gradually emerging as an interdisciplinary area of research in computer science, but the body of its literature that has appeared so far is very small and insignificant even though 22 years have passed since the appearance of its first publication. Certainly, QML as a research subject at present is at a nascent stage of its development. [ABSTRACT FROM AUTHOR]

The frequent detection of persistent per- and polyfluoroalkyl substances (PFAS) in organisms and environment coupled with surging evidence for potential detrimental impacts, have attracted widespread attention throughout the world. In order to reveal research hotspots and trends of technologies for PFAS removal, herein, we performed a data-driven analysis of 3975 papers and 436 patents from Web of Science Core Collection and Derwent Innovation Index databases up to 2023. The results showed that China and the USA led the way in the research of PFAS removal with outstanding contributions to publications. The progression generally transitioned from accidental discovery of decomposition, to experimentation with removal effects and mechanisms of existing methods, and finally to enhanced defluorination and mechanism-driven design approaches. The keywords co-occurrence network and technology classification together revealed the main knowledge framework, which was constructed and correlated through contaminants, substrates, materials, processes and properties. Moreover, adsorption was demonstrated to be the dominant removal process among the current studies. Subsequently, we concluded the principles, advances and drawbacks of enrichment and separation, biological methods, advanced oxidation and reduction processes. Further exploration indicated the hotspots such as alternatives and precursors for PFAS ("genx": 1.258, "f-53b": 0.337), degradable mineralization technologies ("photocatalytic degrad": 0.529, "hydrated electron": 0.374), environment-friendly remediation technologies ("phytoremedi": 0.939, "constructed wetland": 0.462) and combination with novel materials ("metal-organic framework": 1.115, "layered double hydroxid": 0.559) as well as computer science ("molecular dynamics simul": 0.559, "machine learn"). Furthermore, the future direction of technological innovation might lie in high-performance processes that minimize secondary pollution, the development of recyclable and renewable treatment agents, and collaborative control strategies for multiple pollutants. Overall, this study offers comprehensive and objective review for researchers and industry professionals in this field, enabling rapid access to knowledge guidance and insights into research frontiers. [Display omitted] [ABSTRACT FROM AUTHOR]

This study aims to analyse the work done in the field of Night Vision Robots using IR and Object Detection from 2011 to 2021, using the bibliometric methods. This paper presents a Scopus database review on "Night Vision Bot using Dynamic IR and Object Detection". The necessity for doing this bibliometric survey is that to know how the technology in the field of mobile robotics and night vision, as well as to object detection, has evolved over the years. This paper shows the importance of Night Vision Robot from the year 2011 and continued up to 2021 April. The database analysis for the robot is done through Scopus and VOSviewer Version 1.6.16. Through this database survey, it is revealed that the maximum number of publications are Conference Paper and most of them are from the field of Engineering and Computer Science, India has the greatest number of publications followed by China. Methods: The Scopus database was used to obtain the articles for the above topic. The research papers were considered from the year 2011 to 2021. The Scopus analyzer can be used for the analysis of the database with different categories like Source, Subject Area, Country, etc. The analysis such as co-authorship, co-occurrences, citation analysis etc. is done by using VOSviewer Version 1.6.16. Results: In the study, a total of 69 articles on Night Vision Robot were obtained between the years 2011 and 2021. The statistical analysis and network analysis shows that the maximum number of papers were published in the year 2020. India is the highest contributor followed by China and Australia. Conclusion: The outcome of the Scopus database is 69 articles with the English Language having the largest number of articles. The Statistic Analysis helps to understand the potential of topic. It is done for Authors, Documents, Country, affiliations, funding sponsors. The Network Analysis indicates the interconnections between different parameters such as Coauthor, sources. It indicates that this is a new concept, and the research has been done mostly in the last year, so there is lot of future potential and scope for development. [ABSTRACT FROM AUTHOR]

Since the last century, computer science has not stopped growing, innovating and producing changes in society. Facing these changes, the legislator is called to create certain rules that can accompany computer science and, especially, artificial intelligence (AI) in its constant development, providing legal security to users, to the scientific and business community and, in general, to the whole society. The problem posed by artificial intelligence focuses on the fact that law will have to create institutions in order to regulate the “behavior” of these computer systems, which today have wide autonomy. Law, conceived to regulate the behavior of the human being, needs to make substantial changes in its interior. This study aims to analyze this situation in the light of the theories that consider natural law as the foundation of law. For this purpose, in a first phase, artificial intelligence, its definition and characteristics were analyzed, as well as the legal areas where there is concern about the changes originated from the introduction of the AI. Then, general considerations about natural law, its definition, functions and importance are presented. Finally, observations were made about the advisability of considering natural law theories, in the search for solutions to questions presented in the legal field by the evolution of AI in society. This study is documentary-type, based on the consultation and consideration of different specialized texts on the subject. The special technique of direct observation was used to describe and analyze homogeneous characteristics of the phenomena studied, so it is classified as a descriptive study. The study of AI allows us to observe the benefits it has given to society areas such as medicine, entertainment, public administration and even in the practice of law through systems that allow helping lawyers. Some of the problems analyzed are those related to civil liability, personal data protection, algorithm contracts and transhumanism. Some emblematic cases regarding the use of AI are presented, as the case of the citizenship granting to a robot endowed with AI, the consideration of the copyright of a work created through AI in China and the discovery of powerful drugs. It was also possible to determine that the legal problem of AI is in the degree of autonomy that these systems possess. The analysis of natural law allowed us to observe its influence on the creation of the current conception of law and its formative and critical function. The doctrine, in the search for a solution to this problem, has proposed to use a dogmatic consistent with the theories that support contemporary law, without resorting to unsustainable fictions. The proposal of a new legal status for autonomous artificial intelligence systems seems to be a viable solution. Theories of natural law could be useful to guide the formulation of legal precepts applicable to the subject of artificial intelligence; in addition, to provide deep and effective criticisms of the proposed solutions. These new rules must present a solution within the values and principles of the legal system, which respects its unity and maintains the main value of the person. [ABSTRACT FROM AUTHOR]

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods. [ABSTRACT FROM AUTHOR]

Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

Recently, wide applications of fuzzy set theory have attracted the attention of both researchers and practitioners. Fuzzy portfolio develops as a new area in the research field of investment portfolio. This paper investigates the major research hotspots, development trend, and evolution of fuzzy portfolio, which provides a systematic review of the current fuzzy portfolio literature. CiteSpace, the most commonly used bibliometrics software, is used in this article. According to the 602 articles with 15132 references, several conclusions can be summarized as follows: (1) Fuzzy portfolio becomes increasingly interdisciplinary with the connections among "Computer Science" and so on. (2) Most contributive authors are Markowitz and Zadeh. (3) South China University of Technology makes excellent performance in this research area and China is the most influential country. (4) European Journal of Operations Research is the cradle of plenty of crucial fuzzy portfolio investigations. (5) We find some research hotspots helpful to make scientific predictions of future trends by analyzing the keywords. By utilizing the effective bibliometric methods, we provide a comprehensive analysis and deep insights into the fuzzy portfolio research, enabling the individuals, especially the new beginners who are interested in this area to learn fuzzy portfolio, which will be of great help for their future explorations. [ABSTRACT FROM AUTHOR]

Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future. [ABSTRACT FROM AUTHOR]

The purpose is to use medical image processing technology to avoid the influence of subjective factors through the mutual penetration and development of clinical medicine and computer science. Can diagnose the degree of malignancy of ischemic optic neuropathy as quickly as possible, and can take an effective treatment plan for the patient early.Therefore, image segmentation of ischemic optic neuropathy based on fuzzy clustering theory is particularly important for the diagnosis of disease in patients. This paper analyzes the research status of medical image segmentation at home and abroad and the development trend in this aspect in China. Discussed the fuzzy C-means clustering (FCM) image segmentation algorithm in depth, studied the effects of iterative cutoff error, initial clustering center, number of clustering categories and fuzzy weighted index on the practical application of the algorithm. At the same time, the traditional algorithm is not sensitive to the spatial information of the image, making the algorithm sensitive to noise. Firstly, introduced the spatial information of the image, and introduced the algorithm based on spatial information constraint, Based on the above description and based on the neighborhood properties described by the two-dimensional histogram, studied and proposed a relatively easy to understand multidimensional distance measurement method. That is, the two-dimensional pixel value and the neighborhood pixel value viewpoint that can be updated in the two-dimensional direction, by setting a clustering objective function, a clustering measurement method includes neighborhood information. Through the above two-dimensional image segmentation algorithm based on neighborhood spatial information, proposed an image segmentation algorithm for ischemic optic neuropathy of fuzzy kernel clustering theory combined with spatial information. The experimental results show that the proposed algorithm can show excellent results in ischemic neuropathy image segmentation, and the algorithm has faster convergence speed and higher classification accuracy. Experimental results of artificial images and actual images show that the algorithm has strong noise immunity and practicability. [ABSTRACT FROM AUTHOR]

BACKGROUND: Orthopedic surgery robot is a new medical device which integrates many disciplines and fields, such as medicine, electronic informatics and mechanical physics. It is an important development direction of modem medical informationization, programmable control and intellectualization, and has a wide application prospect in orthopedic surgery. OBJECTIVE: To explore the application reliability and room for promotion of robots in orthopedic surgery. METHODS: CNKI, VIP, Wan Fang and PubMed databases were retrieved by using the keywords of "robot, orthopedics, surgery, accuracy" in Chinese and English, respectively for the articles published before June 2019. Forty-nine eligible articles were enrolled based on inclusion criteria. The key problems of surgery robot in orthopedics were reviewed. RESULTS AND CONCLUSION: (1) There are many researches on orthopedic surgical robots, and the methods are mature, but the domestic research on surgical robots Is still In the primary stage compared with developed countries. (2) The technology of orthopedic surgical robots Is moving towards the directions of human-machine intelligence, image refinement, morphological refinement, non-invasive surgery, and remote controllable fluidization. (3) Promoting independent research and development of orthopedic surgical robots, fonnulating industry standards and clinical standards in an all-round way will be an important direction for the devalopment of intelligent medical devices and equipment in China. [ABSTRACT FROM AUTHOR]

Professor John Hopcroft at Cornell University is a Turing Prize winner (1986) and an educator with more than 55 years of teaching experience. For the past 10 years, Hopcroft has been coming to China to give courses to undergraduate students at Shanghai Jiaotong University (SJTU) and has helped SJTU to improve the quality of computer-science education. He also chairs the Center on Frontiers of Computing Studies at Peking University (PKU), the Turing Class at PKU and the Hopcroft Center at Huazhong University of Science and Technology (HUST) in Wuhan, and is engaged in many other projects aiming to upgrade China's computer-science undergraduate education. Recently, NSR talked with Professor Hopcroft to learn his views on education in China. [ABSTRACT FROM AUTHOR]

Situational cognition can help students to construct their knowledge to a great extent. In order to solve the problem of lack of situational cognition in College English teaching, this paper studies the validity of students' situational cognition in College English teaching based on the method of image block gain optimization. First, this paper analyses the general situation of situational cognition capacity in College English teaching in China at present, and puts forward the function of device image in constructing situational cognitive competence in teaching. Then, it divides device image into blocks under pseudo-haze conditions, and proposes the optimization method of block gain. Finally, on the basis of block gain, it makes an empirical test of situational cognitive competence in College English teaching. The empirical results show that image block gain optimization can effectively improve the construction of situational cognition capacity in College English teaching. With the help of this study, some new and useful ideas can be traced for the development of computer science and college English teaching, and also stimulate the further improvement of College English education in China. [ABSTRACT FROM AUTHOR]

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. [ABSTRACT FROM AUTHOR]