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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]

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.

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]

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]

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 paper examines 12104 global publications on digital library research, as covered in Scopus database during 2007-16. Digital library research registered annual average growth rate of 7.83% and averaged 4.40 citations per paper. Digital library research is dominated by top 15 most productive countries accounting for 81.61% global publications share in 10 years during 2007-16. USA accounted for the highest global publications share (26.89%), followed by China (10.42%), etc. Computer Science accounts for the largest publication share (62.48%) in global digital library research, followed by Social Sciences and Engineering (30.86% and 19.75%), Mathematics (15.74%), etc. The top 20 most productive research organizations and productive authors contributed 13.64% and 6.90% global publication share respectively and 15.98% and 10.05% global citation share respectively during 2007-16. The top 30 journals contributed 35.15% share to the global journal output during 2007-16. [ABSTRACT FROM AUTHOR]

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.

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.)

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.)

Purpose — The aims of this article are to describe the current status, usage, and acceptance of domestic preprint servers in mainland China by investigating three integrated preprint servers in mainland China. These are the Qiji e-print archive (Qiji), the Chinese Preprint Server (CPS), Chinese Science Papers Online (CSPO)., Design/methodology/approach — This research gives a quantitative analysis of the submission numbers to three preprint servers, the subject distribution and citations. An investigation of university websites and their policies was also carried out. Findings — Preprint submissions have a strong correlation with official promotion and policies. The preprints communication pattern in mainland China concentrates on the disciplines of physics, mathematics and computer science which reflects international preprint communication behaviour. As yet the domestic preprint servers in mainland China attract little attention from excellent scholars and have a low impact on scientific communication. Research limitations/implications — No author survey was conducted. Originality/value — This is the first paper to characterize the usage and acceptance of domestic preprint servers in mainland China using a quantitative method. [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]

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 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]

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]

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]

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.)

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]

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]

Using the InCites tool of Thomson Reuters, this study compares normalized citation impact values calculated for China, Japan, France, Germany, United States, and the UK throughout the time period from 1981 to 2010. InCites offers a unique opportunity to study the normalized citation impacts of countries using (i) a long publication window (1981 to 2010), (ii) a differentiation in (broad or more narrow) subject areas, and (iii) allowing for the use of statistical procedures in order to obtain an insightful investigation of national citation trends across the years. Using four broad categories, our results show significantly increasing trends in citation impact values for France, the UK, and especially Germany across the last thirty years in all areas. The citation impact of papers from China is still at a relatively low level (mostly below the world average), but the country follows an increasing trend line. The USA exhibits a stable pattern of high citation impact values across the years. With small impact differences between the publication years, the US trend is increasing in engineering and technology but decreasing in medical and health sciences as well as in agricultural sciences. Similar to the USA, Japan follows increasing as well as decreasing trends in different subject areas, but the variability across the years is small. In most of the years, papers from Japan perform below or approximately at the world average in each subject area. [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]

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]

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]

It has been more than 10 years since Satoshi Nakamoto published his famous paper entitled 'Bitcoin: a peer-to-peer electronic cash system', which set the foundation of blockchain technology. Accompanied by the price volatility of bitcoins from 2017 to 2018, blockchain has been a hot word on the internet, and particularly hot in China. Blockchain offers a distributed and secure system for data storage and value transactions. Its applications are springing up in multiple fields. The Chinese government is considering these trends with great caution. Initial coin offering has been banned in China since September 2017. By contrast, an official white paper on China's blockchain technology, which was released in May 2018, said that blockchain technology will be widely applied in the real economy of China within 3 years. In a recent panel discussion held by National Science Review, experts talked about related topics. Their opinions may provide a quick view of the future development of blockchain in China and abroad. Jing Chen Assistant Professor of Computer Science Department, Stony Brook University and Chief Scientist at Algorand LLC, USA Xiaotie Deng Professor of School of Electronics Engineering and Computer Science, Peking University, China Guohua Gan Vice President of Beijing Tai Cloud Technology Corp. China Xiaoyun Wang Professor of Institute of Advanced Study, Tsinghua University, China Zhiming Zheng Professor of School of Mathematics and Systems Science, Beihang University, China Lei Guo (Chair) Professor of Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China [ABSTRACT FROM AUTHOR]

The article offers world news briefs related to science and technology. The journal of William Ross Ashby, a British pioneer in the fields of Cybernetics will be available on the Internet, which consists of more than one million words on 7400 pages in 25 volumes. Workshop on Intelligent Knowledge Management will be held in Chendu and Jiuzhaigou, China on June 21-26, 2009. The 3rd International Multi-Conference on Society, Cybernatics and Informatics will be held in Florida on July 10-13, 2009.

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]

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]

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]

This paper intends to explore the impact of Information technology (IT) development on the legal concept of `signatures'. To what extent and in which way does it impact on the legal concept of `signatures'? This paper attempts to examine this issue from an international and comparative perspective. It was found that IT development has different levels of impact on the legal concept of `signatures' in different jurisdictions. In the Common Law system such as the UK and the US, it does not change the legal concept of `signatures'. However, it does put the legal concept on such an important position. On the contrary, IT development changes the legal concept of `signatures' in the Civil Law system such as Germany and 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]

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]

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]

The Internet is undergoing a tremendous change towards the globalized computing environment. Due to the open, dynamic and uncontrollable natures of the Internet, software running in the Internet computing environment has some new features, which bring challenges to current software technologies in terms of software model, software operating platform, software engineering approaches and software quality. Researchers in China have proposed the term 'Internetware' to present the emerging software paradigm. Sponsored by the National Basic Research 973 Program, several research practices have been done on the Internetware in the past decade. This paper summarizes the progress and status of the Internetware researches. A technical solution framework for the Internetware paradigm is proposed from four aspects: the Internetware software model defines what the Internetware is to be; the Internetware middleware determines how to run the Internetware applications; the engineering methodology determines how to develop the Internetware applications; the Internetware quality assurance determines how well the Internetware applications can perform. The paper also discusses the ongoing research issues and future trends of Internetware. [ABSTRACT FROM AUTHOR]

Purpose Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel.Design/methodology/approach Users’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness.Findings The recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective.Originality/value Similar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites. [ABSTRACT FROM AUTHOR]

This paper draws attention to the specific aspect of institutional environments in international matchmaking for innovation. While technology is largely global this exploratory study of 18 cross-border cases between Europe and China points to the impact of distant institutional environments on the success and structure of cross-border collaborative innovation. Addressing differences in governance, market regulation, policy, IPR protection, language and culture like the Chineseguanxiare specific services based on dedicated capabilities of international innovation intermediaries. [ABSTRACT FROM AUTHOR]

Variation of streamflow and sediment load in the Yellow River basin has received considerable attention due to its drastic reduction during the past several decades. This paper presents a detailed investigation on the changes of streamflow and sediment load from 1952 to 2011 using monthly observations at four gauging stations along the Yellow River. The results show significant decreasing trends for both streamflow and sediment load at all four gauging stations over the past 60 years. The wavelet transform demonstrated discontinuous periodicities from 1969 to 1973 and after 1986 due to the construction of large reservoirs and implementation of numerous soil and water conservations practices. The sediment rating curves with the power-law function was applied to investigate the relationship between discharge and sediment load. The results indicate distinct variations of the relationship between streamflow and sediment and implied significant hydro-morphological changes within different periods. The reducing sediment supply from the source region and the increased erosive power of the river are detected at Lanzhou station, while the decrease of the transport capacity at Toudaoguai is caused by severe siltation. Significant changes in the relationship between streamflow and sediment load are found at Huayuankou and Gaocun stations, which are largely induced by evident sediment income and trapping effects of large reservoirs. It is estimated that numerous reservoirs have strongly altered the regime and magnitude of streamflow and trapped large amount of sediment, leading to severe siltation and evident reduction of their total volumes. A decrease in precipitation, incoming water from the upper reaches, soil and water conservation measures as well as water consumption contribute most to the significant reduction of streamflow. The decrease of sediment load mainly resulted from various soil and water conservation measures and trapping in reservoirs from 1986 to 2011. [ABSTRACT FROM AUTHOR]

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. [ABSTRACT FROM AUTHOR]

Background: syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings. Objective: this study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system. Methods: ISS was developed based on an existing platform ‘Crisis Information Sharing Platform’ (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting. Results: As of Jan. 31st 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74256, 79701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level. Conclusions: The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China. [ABSTRACT FROM AUTHOR]

Abstract: With aim to master early deformation stages and motion laws of landslide, the raw displacement data of landslide are very important for the landslide researchers and environmental scientists. With the recent advances of Global Position System (GPS) and computer science, the differential GPS technology now can be used for the remote displacement observation of landslide. Therefore, the paper developed a landslide displacement remote observation system based on differential GPS technology to get the raw displacement data continually. The system is divided into hardware and software functional segments. A real landslide in southwest of China was monitored for several months through observation using this GPS-based system placed around and the observed results are presented in this paper. The practical application shows that this system has such advantages as being highly-precise, nearly real-time, remote, continuous and automatic. [Copyright &y& Elsevier]

Collaboration patterns among scientists are becoming more and more complicated. Even sophisticated methods for taking the number of co-authors into account do not solve all problems related to the calculation of citation measures such as the h-index. In this article we introduce role-based h-indices and in particular the major contribution h-index, denoted as h-maj, which takes only those articles into account in which the scientist plays a major or core role. As an example we provide major contribution indices for scientists in the health sciences in China. Differences between the h-index and h-maj are shown for data based on the Web of Science (WoS), and separately, based on the China National Knowledge Infrastructure (CNKI) database. It is suggested to use the major contribution h-index as a supplementary index, especially in those fields where multiple 'first authors' and/or corresponding authors are common. [ABSTRACT FROM AUTHOR]

The journal of Supercomputing publishes original quality research throughout various areas, including Parallel and Distributed Processing. The scope of the journal includes novel applications as well as fundamental contributions to the field. The Volume 30 of The Journal of Supercomputing is a special issue containing selected extended articles from papers presented in the 2002 International Conference on Parallel and Distributed Processing Techniques and Applications that took place on June 24-27, 2002 at the Monte Carlo Resort in Las Vegas, Nevada. The papers appearing this special issue provides a variety and wealth of contributions and approaches in the field. G.A. Gravvanis and colleagues presents a class of normalized approximate inverse matrix techniques for solving finite difference linear systems. E. Santos considers the problem solving tridiagonal linear systems in parallel distributed-memory environments using direct methods. S.T. Xu and J. Zhang discusses schemes for web document clustering techniques used in web document retrieval systems.

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]