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The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets (PFSs) from 2013 to 2020 in order to comprehensively understand their historical progress and current situation, as well as future development trend.

First, this paper describes the fundamental information of these publications on PFSs, including their data information, annual trend and prediction and basic features. Second, the most productive and influential authors, countries/regions, institutions and the most cited documents are presented in the form of evaluation indicators. Third, with the help of VOSviewer software, the visualization analysis is conducted to show the development status of PFSs publications at the level of authors, countries/regions, institutions and keywords. Finally, the burst detection of keywords, timezone review and timeline review are exported from CiteSpace software to analyze the hotspots and development trend on PFSs.

The annual PFSs publications present a quickly increasing trend. The most productive author is Wei Guiwu (China). Wei Guiwu and Wei Cun have the strongest cooperative relationship.

The implication of this study is to provide a comprehensive perspective for the scholars who take a fancy to PFSs, and it is valuable for scholars to grasp the hotspots in this field in time.

It is the first paper that uses the bibliometric analysis to comprehensively analyze the publications on PFSs. It can help the scholars in the field of PFSs to quickly understand the development status and trend of PFSs.

Criteria importance through intercriteria correlation (CRITIC) is a multicriteria decision-making method that helps compute the weights of decision criteria by considering the contrast intensity and conflicting relationships inherent in each criterion. This study aims to conduct a bibliometric analysis to provide quantitative insights into the research trends concerning the CRITIC method.

The study assembled bibliographic data from 220 CRITIC-based publications retrieved from the Scopus database. Subsequently, the gathered data were processed using Tableau software, using specific functions within the software to format them to suit the analysis requirements. Following data preparation, a visual analysis was then conducted based on five parameters that can characterize the research evolutions in CRITIC. These parameters include research productivity across years, dominant countries, dominant researchers, dominant publication outlets and popular research topics. Various visualization techniques, such as combined charts, geographical maps and word clouds, were used to draw conclusions for each parameter.

The study discovered a burgeoning trend in CRITIC research in recent years, particularly from 2019 onwards. The COVID-19 pandemic unexpectedly contributed to this upward trend, prompting remarkable collaboration among researchers who used diverse decision-making methods, such as CRITIC, to provide data-driven solutions for addressing COVID-19 challenges. Additionally, the study identified China and Iran as the leading countries in CRITIC research, with notable researchers such as Xindong Peng and Mehdi Keshavarz-Ghorabaee predominantly affiliated with institutions in these countries. Keyword analysis indicated the application of CRITIC across various trending topics, including Industry 4.0 and environmental sustainability.

No bibliometric analyses have been conducted on the CRITIC method in the literature since its inception in 1995, leaving the scientific community clueless about its research trends. To the best of the author’s knowledge, this study serves as the first bibliometric analysis, providing quantitative evidence on the research trends associated with the CRITIC method. By shedding light on these trends, this study enables the scientific community, including researchers and funding agencies, to make informed decisions regarding future research endeavors involving the CRITIC method.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

This paper aims to devise a robust controller for the non-linear aircraft model using output feedback control topology in the presence of uncertain aerodynamic parameters.

Feedback linearization-based state feedback (SFB) controller is considered along with a robust outer loop control which is designed using Lyapunov’s second method. A high-gain observer (HGO) in accordance with the separation principle is used to implement the output feedback (OFB) control scheme. The robustness of the controller and observer is assessed by introducing uncertain aerodynamics coefficients in the dynamic model. The proposed scheme is validated using MATLAB/SIMULINK.

The efficacy of the proposed scheme is authenticated with the simulation results which show that HGO-based OFB control achieves the SFB control performance for a small value of the high-gain parameter in the presence of uncertain aerodynamic parameters.

A HGO for the non-linear model of aircraft with uncertain parameters is a novel contribution which could be further used for the unmanned aerial vehicles autopilot, flight trajectory tracking and path following.

This case traces through a 20-year history of a Chinese high-tech company, Maipu Communications Technology Company. Throughout the company’s growth, Maipu adjusted its innovation models in order to ensure that they remained compatible with corporate strategies, resources and external environments. However, as the company grew bigger, it was finding it more and more difficult to meet its innovation goals. Its current innovation model is a market-driven platform + distributed innovation. While Maipu has achieved some success under this model, it is faced with a myriad of challenges during the execution of the model. The key questions raised by this case are whether Maipu’s current innovation model is suitable for the company at this stage and how the innovation model should be adjusted to propel new innovation and growth opportunities for Maipu in this increasingly competitive market.

This case was a field research case. The authors paid three visits to Maipu Communications Technology Company, during which the authors conducted in-depth interviews with Mr Zhao, the Head of Maipu’s R&D and Innovation group, and several senior and functional managers of the company. Follow-up communication via telephone and e-mail was conducted to verify the accuracy of the written case.

This case is well suited for courses in the areas of strategic management, innovation management, high-tech management, entrepreneurship, and international business. The target audiences of the case are primarily MBA students, although this case can also be used in upper-level undergraduate business courses.

The theoretical basis for this case includes the following management theories: strategy formulation and strategy implementation, business-level and corporate-level strategies, enterprise life-cycle, corporate strategies at various stages of growth, patterns of innovation and applications, and implementation of innovation strategies.

This chapter reviews the status of Education For All (EFA) in China and identifies four gaps: between rural and urban residents, between residents of geographic regions, between ethnicity groups, and between the genders. It turns to examine the educational situation and interests of girls weighed down by the crushing burden of multiple disadvantages in “left-behind” Western China. Based on analysis of macro-level socio-economic and educational conditions, along with rich micro-level data on girls’ vigorous pursuit of education, the authors argue that the changing conditions of rural girls’ lives and their education can best be understood from a critical empowerment perspective. Summarizing the global discourse and cross national evidence on the benefits of girls’ education, the chapter and looks beyond a utilitarian perspective and argues for the cogency of a critical empowerment framework. Filled with telling stories and case studies of Han Chinese, Tibetan, and Muslim girls, this chapter proposes that prioritizing girls’ education in Western China is crucial and required for achieving the MDG of gender parity. Even though girls are often stranded by family financial conditions, their actions and ideas seeing education as their future reflect a changing gender identity and role in the family and society. The fieldwork suggests that educating girls promotes localized development, reduces dangerous levels of economic gaps and social instability, but also advances hard to measure effects: personal and civil empowerment, and sustainable, harmonious cultural change – as well as MDG.

The purpose of this paper is to report on an exploratory study, Project En-ELT (enhancing English language learning and teaching), which used lesson study to mediate curriculum innovation to enhance student learning by engaging teachers in learning and implementing effective English language teaching strategies and formative assessment practices in seven lower secondary schools in Singapore over two years. It aims to portray how lesson study can be adapted to build teacher pedagogical capacity in carrying out the language development goals formulated in the revised national English Language Syllabus 2010.

Project evaluation is embedded systematically into the research design from the very beginning of the pilot to in between each step of lesson study process across three consecutive cycles in order to evaluate the effectiveness of the pilot program from the project advisors’, participating teachers’ and students’ perspectives. Both the quantitative and qualitative data were collected in and across the instructional steps and lesson study cycles to create immediate evidence-based feedback to inform continuous on-going adjustment and improvement.

Findings indicate that across the three cycles the lesson study teams moved from isolated to collaborative planning; from poor understanding and mechanical execution of the retelling strategy to a more sophisticated and skilful use of reciprocal teaching. An increase was found in teacher confidence and positive attitude towards the value of the project in developing their language and teaching effectiveness. There was enhanced student engagement and collaborative participation in the lessons while assessment for learning was fostered in the classroom.

Program evaluation provided feedback loops to ensure that each enactment stage and cycle learns from and builds on the limitations and strengths of the previous one(s) so internal consistency, continuity and coherence can be achieved for concrete implementation; different perspectives from the project officers/researchers, teachers and students were collected consistently and analyzed to gauge the accuracy of the findings; the collaboration between Ministry of Education curriculum officers, specialists and teachers, through lesson study, was able to create democratic relations rested upon interdependence, and mutual respect and trust; and it provides an illustrative case of how lesson study can be used effectively to help schools carry out national curriculum and pedagogical innovations. The project has important implications for addressing the issues of implementation and sustainability of innovative curriculum practices.