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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]

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]

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]

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.

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]

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]

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.)

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]

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]

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]

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.

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]

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]

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]

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.

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]

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. [ABSTRACT FROM AUTHOR]

Abstract: A new construction adder based on Chinese abacus algorithm is presented in this paper. There are two kinds of beads used in this construction. Each column element has three higher beads with a weight of four and three lower beads with a weight of one. The proposed 32-bit adder contains eight column elements. The construction was simulated by the technology of TSMC 0.18μm CMOS process. Layout was also made by the same technology. The maximum delay of the 32-bit abacus adder is 0.91ns and 14% less than that of Carry Look-ahead Adders for 0.18μm technology. The power consumption of the abacus adder is 3.1mW and 28% less than that of Carry Look-ahead Adders for 0.18μm technology. Recent researches are compared with the proposed adder. The construction was also simulated by Predictive Technology Model. The PTM results also presented. The use of Chinese abacus approach offers a competitive technique with respect to other adders. [Copyright &y& Elsevier]

A key problem in developing a virtual community for co-constructing learning resources is the need to have people volunteer to share their expertise. In this paper, we introduce the first phase of our research in which we examine strategies for encouraging participants to share in the system. We began by designing an intervention model based on creating and sustaining social capital among the participants for the entire resource development process. Data collected in this pilot for the first phase showed that this design was effective in attracting and maintaining users' interest in contributing to this virtual community, while leaving some designed functions and tools still in need of improvement. These findings will be used to make modifications for a future version of the system, and their implications to the social computing field are addressed also. [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]

Background: China's one-child-per-couple policy, introduced in 1979, led to profound demographic changes for nearly a quarter of the world's population. Several decades later, the consequences include decreased fertility rates, population aging, decreased household sizes, changes in family structure, and imbalanced sex ratios. The epidemiology of communicable diseases may have been affected by these changes since the transmission dynamics of infectious diseases depend on demographic characteristics of the population. Of particular interest is influenza because China and Southeast Asia lie at the center of a global transmission network of influenza. Moreover, changes in household structure may affect influenza transmission. Is it possible that the pronounced demographic changes that have occurred in China have affected influenza transmission? Methods and Findings: To address this question, we developed a continuous-time, stochastic, individual-based simulation model for influenza transmission. With this model, we simulated 30 years of influenza transmission and compared influenza transmission rates in populations with and without the one-child policy control. We found that the average annual attack rate is reduced by 6.08% (SD 2.21%) in the presence of the one-child policy compared to a population in which no demographic changes occurred. There was no discernible difference in the secondary attack rate, −0.15% (SD 1.85%), between the populations with and without a one-child policy. We also forecasted influenza transmission over a ten-year time period in a population with a two-child policy under a hypothesis that a two-child-per-couple policy will be carried out in 2015, and found a negligible difference in the average annual attack rate compared to the population with the one-child policy. Conclusions: This study found that the average annual attack rate is slightly lowered in a population with a one-child policy, which may have resulted from a decrease in household size and the proportion of children in the population. [ABSTRACT FROM AUTHOR]

Objective: The objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases. Methods: According to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model. Results: The simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025. Conclusion: The grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control. [ABSTRACT FROM AUTHOR]

Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM) to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP) using the annual and monthly mean temperatures from 1961–2010 at 144 meteorological stations in and near the TP. The results showed the following: (1) The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2) Spatially, the zones that became significantly smaller were the temperature zones of −6°C and −4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3) The elevation dependency of climate warming existed in the TP during 1961–2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991–2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions. [ABSTRACT FROM AUTHOR]

Forest management under a changing climate requires assessing the effects of climate warming and disturbance on the composition, age structure, and spatial patterns of tree species. We investigated these effects on a boreal forest in northeastern China using a factorial experimental design and simulation modeling. We used a spatially explicit forest landscape model (LANDIS) to evaluate the effects of three independent variables: climate (current and expected future), fire regime (current and increased fire), and timber harvesting (no harvest and legal harvest). Simulations indicate that this forested landscape would be significantly impacted under a changing climate. Climate warming would significantly increase the abundance of most trees, especially broadleaf species (aspen, poplar, and willow). However, climate warming would have less impact on the abundance of conifers, diversity of forest age structure, and variation in spatial landscape structure than burning and harvesting. Burning was the predominant influence in the abundance of conifers except larch and the abundance of trees in mid-stage. Harvesting impacts were greatest for the abundance of larch and birch, and the abundance of trees during establishment stage (1–40 years), early stage (41–80 years) and old- growth stage (>180 years). Disturbance by timber harvesting and burning may significantly alter forest ecosystem dynamics by increasing forest fragmentation and decreasing forest diversity. Results from the simulations provide insight into the long term management of this boreal forest. [ABSTRACT FROM AUTHOR]

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present. [ABSTRACT FROM AUTHOR]

Chinese microblogs have drawn global attention to this online application’s potential impact on the country’s social and political environment. However, representative and reliable statistics on Chinese microbloggers are limited. Using a random sampling approach, this study collected Chinese microblog data from the service provider, analyzing the profile and the pattern of usage for 29,998 microblog accounts. From our analysis, 57.4% (95% CI 56.9%,58.0%) of the accounts’ timelines were empty. Among the 12,774 non-zero statuses samples, 86.9% (95% CI 86.2%,87.4%) did not make original post in a 7-day study period. By contrast, 0.51% (95% CI 0.4%,0.65%) wrote twenty or more original posts and 0.45% (95% CI 0.35%,0.60%) reposted more than 40 unique messages within the 7-day period. A small group of microbloggers created a majority of contents and drew other users’ attention. About 4.8% (95% CI 4.4%,5.2%) of the 12,774 users contributed more than 80% (95% CI,78.6%,80.3%) of the original posts and about 4.8% (95% CI 4.5%,5.2%) managed to create posts that were reposted or received comments at least once. Moreover, a regression analysis revealed that volume of followers is a key determinant of creating original microblog posts, reposting messages, being reposted, and receiving comments. Volume of friends is found to be linked only with the number of reposts. Gender differences and regional disparities in using microblogs in China are also observed. [ABSTRACT FROM AUTHOR]

Background: Hypertension has been recognized as a health concern for developing countries. However, there are no current nationwide surveys on the prevalence of hypertension in China (the latest nationwide survey was ten years ago). The goal of this study was to estimate the pooled prevalence of hypertension in Chinese cities. Methods: We systematically reviewed published epidemiologic studies on the prevalence of hypertension in Chinese cities through meta-analysis. We searched for studies published between January 2002 and June 2012 using PubMed and two Chinese electronic publication libraries. The keywords ‘hypertension’ and ‘prevalence’ were used. Before pooling prevalence of hypertension, all raw prevalence data was age adjusted to the 2010 China standard population. Prevalence estimates were stratified by sex and geographic area. Results: 27 studies were identified with of a total of 195,027 study participants. The overall pooled prevalence of hypertension was 21.5% (19.4%, 23.6%). Subgroup analyses showed the following results north 25.8% (21.6%, 30.0%), south 20.4% (18.6%, 22.2%); male 22.2% (19.3%, 25.1%), female 19.9% (17.6%, 22.1%); large cities 18.9% (15.7%, 22.1%), medium-sized cities 24.6% (19.9%, 29.4%), small cities 20.6% (17.5%, 23.7%); study years in 2002–2006, 21.9% (18.9%, 24.8%), and study year in 2007–2011, 20.6% (17.3%, 23.9%). Conclusions: Comparing data from several previous national hypertension surveys, the prevalence of hypertension is higher in cities than the Chinese national average. Subgroup studies also found a higher prevalence of hypertension in northern cities and among males. Also, the prevalence of hypertension in medium-sized and small cities is likely to increase faster than in large cities. [ABSTRACT FROM AUTHOR]

Cunninghamia lanceolata (Lamb.) Hook. has been widely planted in subtropical China to meet increasing timber demands, leading to short-rotation practices that deplete soil nutrients. However, increased nitrogen (N) deposition offsets soil N depletion. While long-term experimental data investigating the coupled effects related to short rotation practices and increasing N deposition are scarce, applying model simulations may yield insights. In this study, the CenW3.1 model was validated and parameterized using data from pure C. lanceolata plantations. The model was then used to simulate various changes in long-term productivity. Results indicated that responses of productivity of C. lanceolata plantation to increased N deposition were more related to stand age than N addition, depending on the proportion and age of growing forests. Our results have also shown a rapid peak in growth and N dynamics. The peak is reached sooner and is higher under higher level of N deposition. Short rotation lengths had a greater effect on productivity and N dynamics than high N deposition levels. Productivity and N dynamics decreased as the rotation length decreased. Total productivity levels suggest that a 30-year rotation length maximizes productivity at the 4.9 kg N ha−1 year−1 deposition level. For a specific rotation length, higher N deposition levels resulted in greater overall ecosystem C and N storage, but this positive correlation tendency gradually slowed down with increasing N deposition levels. More pronounced differences in N deposition levels occurred as rotation length decreased. To sustain C. lanceolata plantation productivity without offsite detrimental N effects, the appropriate rotation length is about 20–30 years for N deposition levels below 50 kg N ha−1 year−1 and about 15–20 years for N deposition levels above 50 kg N ha−1 year−1. These results highlight the importance of assessing N effects on carbon management and the long-term productivity of forest ecosystems. [ABSTRACT FROM AUTHOR]