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The purpose of this paper is to derive a new fractal active low-pass filter (LPF) within the local fractional derivative (LFD) calculus on the Cantor set (CS).

To the best of the author’s knowledge, a new fractal active LPF within the LFD on the CS is proposed for the first time in this work. By defining the nondifferentiable (ND) lumped elements on the fractal set, the author successfully extracted its ND transfer function by applying the local fractional Laplace transform. The properties of the ND transfer function on the CS are elaborated in detail.

The comparative results between the fractal active LPF (for γ = ln2/ln3) and the classic one (for γ = 1) on the amplitude–frequency and phase–frequency characteristics show that the proposed method is correct and effective, and is expected to shed light on the theory study of the fractal electrical systems.

To the best of the author’s knowledge, the fractal active LPF within the LFD calculus on the CS is proposed for the first time in this study. The proposed method can be used to study the other problems in the fractal electrical systems, and is expected to shed a light on the theory study of the fractal electrical systems.

The chapter deals with the interface between the law of succession and corporate law and explains the completely different objects of these two fields of law. Succession law tries to shift and contribute assets to the successors, whereas corporate law focuses on the well-being of the company. However, in a family business, it is necessary to find legal, social, and psychological techniques to combine these two areas and to establish strong and binding relations. This is the function of shareholder agreements and family constitutions.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

This study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation.

This contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently.

A total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges.

To the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.

The purpose of this paper is to support total productive maintenance implementers by providing a roadmap for autonomous maintenance (AM) preparation phase.

The authors use the axiomatic design (AD) methodology with lean philosophy as a paradigm.

This is an exploratory research to find the most important factors in AM preparation phase. A decoupled AD design ensures an effective usage of training within industry (TWI) and the introduction of standardized work (SW). TWI provides value in importance it assigns to leaders, with its “train the trainers” approach and in preparing a training program. Besides being an effective training method, TWI job instruction (TWI JI) provides needed information infrastructure to front load operators SW and equipment trainings.

Although AD, TWI and lean artifacts are generally field proven, the research is limited due to the lack of an industrial application.

In many real-life projects, companies do not know where to start and how to proceed, which leads to costly iterations. The proposed roadmap minimizes iterations and increases the chance of project success.

The authors apply AD for the first time to AM preparation phase despite it is used in the analysis of lean manufacturing. AD permits to structure holistically the most relevant lean manufacturing solutions to obtain a risk free roadmap. TWI has emerged as a training infrastructure; TWI JI-based operator SW training and the adaptation of JI structure to equipment training are original additions.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

This research takes the transfer behavior of users from Tencent QQ to WeChat as an example to discuss the wider transfer behavior of social media users on the Internet.

This paper collects data through a combination of offline interviews and online questionnaire surveys, and utilizes data analysis tools to construct structural equation modeling (SEM). Using Statistical Product and Service Solutions (SPSS) Statistics 22.0 and Analysis of Moment Structures (AMOS) 22.0 software with SEM, this study was carried out to provide reasonable statistical support for relevant proposed hypotheses based on 368 effective samples acquired through the questionnaire.

The findings of this study show that subjective norm, transfer experience, social communication, and knowledge acquisition all have significant associations with transfer intention and switching behavior. To be specific, transfer intention exerts a positive association on switching behavior; function setting, privacy protection and personal innovation have a favorable association with transfer intention; transfer cost has a significantly negative relationship with transfer intention and switching behavior; function setting has no important relationship on switching behavior.

The research results provide a reference for improving the viscosity and loyalty of social media users in the new era and resolving the problem of user churn.