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Rough set theory is a simple and potential methodology in extracting and minimizing rules from decision tables. Its concepts are core, reduct and discovering knowledge in the form of rules. The decision rules explain the decision state to predict and support the new situation. Initially it was proposed as a useful tool for analysis of decision states. This approach produces a set of decision rules involves two types namely certain and possible rules based on approximation. The prediction may highly be affected if the data size varies in larger numbers. Application of Rough set theory towards this direction has not been considered yet. Hence the main objective of this paper is to study the influence of data size and the number of rules generated by rough set methods. The performance of these methods is presented through the metric like accuracy and quality of classification. The results obtained show the range of performance and first of its kind in current research trend.

This paper proposes the combination of rapid prototyping and physical modelling as a set-based concept evaluation method in the early stage of new product development.

The concept evaluation method is applied in a case study of a new metal additive manufacturing process for aluminium, where a set of four extruder concepts has been modelled and evaluated. Rapid prototyping was used to produce plastic models of the different designs, and plasticine feedstock material was used to physically model the metal flow during operation. Finally, the selected concept has been verified in full-scale for processing of aluminium feedstock material.

The proposed method led to several valuable insights on critical factors that were unknown at the outset of the development project. Overall, these insights enabled concept exploration and concept selection that led to a substantially better solution than the original design.

This method can be applied for other projects where numerical approaches are not applicable or capable, and where the costs or time required for producing full-scale prototypes are high.

Employing this method can enable a more thorough exploration of the design space, allowing new solutions to be discovered.

The proposed method allows a design team to test and evaluate multiple concepts at lower cost and time than what is usually required to produce full-scale prototypes. It is, therefore, concluded to be a valuable design strategy for the early development stages of complex products or technologies.

The purpose of this paper is to establish and implement a direct topological reanalysis algorithm for general successive structural modifications, based on the updating matrix triangular factorization (UMTF) method for non-topological modification proposed by Song et al. [Computers and Structures, 143(2014):60-72].

In this method, topological modifications are viewed as a union of symbolic and numerical change of structural matrices. The numerical part is dealt with UMTF by directly updating the matrix triangular factors. For symbolic change, an integral structure which consists of all potential nodes/elements is introduced to avoid side effects on the efficiency during successive modifications. Necessary pre- and post processing are also developed for memory-economic matrix manipulation.

The new reanalysis algorithm is applicable to successive general structural modifications for arbitrary modification amplitudes and locations. It explicitly updates the factor matrices of the modified structure and thus guarantees the accuracy as full direct analysis while greatly enhancing the efficiency.

Examples including evolutionary structural optimization and sequential construction analysis show the capability and efficiency of the algorithm.

This innovative paper makes direct topological reanalysis be applicable for successive structural modifications in many different areas.

This article investigates how Sustainable Development Goals (SDGs) can be integrated into the strategic planning and management processes of local governments (LGs). It draws from the classic strategic planning and control framework developed in management studies and elaborates some propositions for adapting, implementing and monitoring the SDGs at the city level.

As a first step in the assessment of the ways the principles of sustainable development can be integrated into LG management, this research scrutinizes the incorporation of sustainability goals in the strategic plans of all medium-to-large capital cities of provinces in Italy, a context in which there has been a National Strategy for Sustainable Development (NSSD) since 2016.

The focus on SDGs at the LG level in Italy is in its initial stage, and few capital cities have started to integrate sustainable development concerns into their comprehensive strategic plans. SDGs are used mainly as a reference framework in the strategic plans to demonstrate the contribution of LG strategies to global concerns on sustainable development.

The paper offers insights for political leaders and public managers to rethink their strategic management systems, including the continuous process of evaluating and updating of strategic plans, in accordance with the multidimensional perspective of sustainability. To this end, the study has identified possible patterns of actions that public managers elsewhere will find useful.

The managerial approach behind the proposed conceptual framework might contribute to effectively localize the SDGs in multilevel government settings and to integrate the concept of sustainability as a guiding principle into organizational routines.

This chapter analyzes what happens to media use when everyday life is suddenly disrupted, focusing on how the COVID-19 pandemic transformed work, socializing, communication and everyday living. The empirical case is changing media use in Norway during the pandemic, building on a qualitative questionnaire survey conducted in early lockdown, and follow-up interviews eight months later. Expanding on the ideas of destabilization of media repertoires developed in the former chapter, this analysis discusses transforming media repertoires as more digital, as less mobile (but still smartphone-centric) and as essentially social. The chapter further explains new concepts for pandemic media use practices, such as doomscrolling and Zoom fatigue.

This paper aims to understand the issue of interest rate benchmarking in Islamic financial institutions (IFIs) from a macro-economic perspective and assessing the relevance of creating a Sharīʿah-compliant profit rate benchmark to solve this issue. This paper also aims at suggesting an Islamic alternative that will handle both the negative economic impact on IFIs as well as on their financial performance.

The paper is based on literature review of conventional finance and Islamic finance theories to construct a theoretical model to assess the impact of interest rate benchmarking on the ability of IFIs to achieve the objectives of the Islamic economy.

The macro-economic perspective concludes that conceiving a profit rate benchmark for the Islamic finance industry is not relevant to raising the Sharīʿah credibility of the industry. Indeed, several adjustments need to be introduced in terms of the business model.

The recommendations of this paper require the involvement of financial authorities and governments for their implementation. Indeed, the adjustments require a macro-economic review.

The paper considers a profit rate benchmark irrelevant and inefficient. Instead, it suggests the necessary adjustments in terms of business model and economic approach for IFIs to achieve their objectives.

The paper considers zakat implementation and the adjustment of IFIs as the real path to implement a fair wealth distribution in the society.

The creation of a profit rate benchmark has always been the only solution for the pricing issue in IFIs. This paper challenges this idea and tries to give a deeper understanding of the situation.

To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.

At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.

The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.

Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.

This study aims to examine how lecturers in European higher education institutions perceive the service-learning approach as a teaching concept and feel prepared for its implementation after a short-term training. Service-learning connects theory and practice by allowing students to participate in a service that meets community needs, reflect on this experience and gain an enhanced sense of civic engagement.

The evaluation study drew on data from four focus group discussions with n = 21 lecturers from five European countries following a Winter School on service-learning in 2020. The qualitative data were analyzed using reflexive thematic analysis.

The findings indicate four themes of preparedness: (1) overall conceptions of service-learning, (2) continuum of preparedness for service-learning, (3) influential factors for preparedness and (d) transfer to home universities. The participants viewed themselves as multipliers for service-learning in their home universities; however, they were skeptical about being able to fully implement the service-learning approach after only one training and without a community of practice with lecturers with similar experiences in their home universities.

This study complements previous studies by adding a cross-national perspective of higher education lecturers. It underlines the importance of continuing training in didactics of university lecturers and a support network for the implementation of complex teaching concepts in higher education.

The purpose of this paper is to extend the recent results of Okeke et al. (2018) to the class of multivalued ρ-quasi-contractive mappings in modular function spaces. We approximate fixed points of this class of nonlinear multivalued mappings in modular function spaces. Moreover, we extend the concepts of T-stability, almost T-stability and summably almost T-stability to modular function spaces and give some results.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.