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This project demonstrates three specific applications for integrating paper‐based information into the electronic environment. The first allows users to interactively select and view portions of page‐images, such as illustrations, subtitles, or text paragraphs, when either the screen resolution of their workstations or low transmission rates prevent viewing the bitmap of the entire page. The second application automates the selection of suitable portions of the document (e.g., abstracts) for subsequent optical recognition for full‐text searching of journals and reports in paper form. The third application automatically links pixel maps of illustrations with ASCII text files.

Many bike rental organizations permit customers to pick-up bikes from one bike station and return them at a different one. However, this service may result in bike imbalance, as bikes may accumulate in stations with low demand. To overcome the imbalance problem, this paper aims to develop a decision model to minimize the total costs of unmet demand and empty bike transport by determining bike fleet size, deployments and the vehicle routing schedule for bike transports.

This paper developed a constrained mixed-integer programming model to deal with this bike imbalance problem. The proposed model belongs to the non-deterministic polynomial-time (NP)-hard problem. This paper developed a two-phase heuristic approach to solve the model. In Phase 1, the approach determines fleet size, deployment level and the number of satisfied demands. In Phase 2, the approach determines the routing schedule for bike transfers.

Computational results show the following results that the proposed approach performs better than General Algebraic Modeling System (GAMS) in terms of solution quality, regardless of problem size. The objective values and the fleet size of rental bikes allocated increase as the number of rental stations increases. The cost of transportation is not directly proportional to the number of bike stations.

The authors provide an integrated model to simultaneously deal with the problems of fleet sizing, empty-resource repositioning and vehicle routing for bike transfer in multiple-station systems, and they also present an algorithm that can be applied to large-scale problems which cannot be solved by the well-known commercial software, GAMS/CPLEX.

Over the past two decades, the hub location domain has witnessed remarkable growth, yet no prior study reviewed and synthesised problem formulation and solution methodologies to address real-life challenges.

The current study conducts a comprehensive bibliometric literature review to develop a thematic framework that describes and presents hub location problems. The work employs cluster, bibliometric, and social network analyses to delve into the essential themes.

Key themes include cooperation, coopetition, sustainability, reshoring, and dynamic demand, contributing to the complex challenges in today’s hub location problems. As the first work in this field, the study serves as a valuable single-source reference, providing scholars and industry practitioners with key insights into the evolution of hub location research, prominent research clusters, influential authors, leading countries, and crucial keywords.

Findings have significant implications since they highlight the current state of hub location research and set the stage for future endeavours. Specifically, by identifying prominent research clusters, scholars can explore promising directions to push the boundaries of knowledge in this area.

This work is a valuable resource for scholars in this domain and offers practical insights for industry practitioners seeking to understand the hub location problems. Overall, the study’s holistic approach provides a solid foundation for advancing future research work in the hub location field.

Mandatory auditor firm rotation (mandatory rotation) has been a controversial issue in the United States for many decades. Mandatory rotation has been considered at various times as a means of improving auditor independence. For example, in the United States, the Public Company Accounting Oversight Board (PCAOB) has considered mandatory rotation as a solution to the independence problem (PCAOB, 2011) and the European Parliament approved legislation that will require mandatory rotation in the near future (Council of European Union, 2014). The concept of implementing a mandatory rotation policy has been encouraged by some constituents of audited financial statements and rejected by other constituents of audited financial statements. Although there are apparent pros and cons of such a policy, the developmental process of such a policy in this country has not necessarily been an open-democratic, objective process. Universal mandatory rotation may or may not be the ideal solution; however, an open-democratic, objective process is needed to facilitate the development of a solution that considers the needs of all major stakeholders of audited financial statements – not simply accounting firms and public companies, but also investors. The purpose of this paper is to critically examine key issues relating to mandatory rotation and to encourage and stimulate future research and ongoing dialogue regarding this issue, in spite of efforts by certain constituents to silence the issue. This paper provides an overview of the various reasons, including practical, theoretical, political, and self-motivated reasons, why a mandatory rotation policy has not been implemented in the United States in order to address the potential conflict of interest between the auditor and client. This paper will also discuss how some deliberations of mandatory rotation have been flawed. The paper concludes with a summary of key issues along with two approaches for regulators, policy makers, and academics to consider as ways to improve the process and address auditor independence. The authors are not advocating for any specific solution; however, we are advocating for a more objective, unified approach and for the dialogue regarding auditor rotation to continue.

Electrical discharge machining (EDM) of hard materials like NiTi 60 alloys is important as it finds application in different sectors of engineering such as automobile, aircraft, biomedical, oil industries, etc.

The first target of this investigation is to determine the effect of process parameters such as current, voltage, pulse on time and pulse off time on the material removal rate (MRR), surface roughness (SR) and white layer formation (WLT) for NiTi 60 smart material alloy. The secondary aim is to identify the presence of surface integrity parameters such as cracks, WLT, microvoids, globules and debris formation by using the scanning electron microscopy technique and with the use of ImageJ software for die sink EDM machining of NiTi 60 alloy.

The results reveal that current is significant for MRR, voltage and current influence SR, and for WLT, voltage is a significant factor. The experimentation study also shows the generation of oxide and carbide layers on the machined surface, which were evident with the use of the X-ray diffraction technique. The presence of these oxide and carbide layers causes to form WLT on the machined surface and thereby increases the hardness of the machined surface.

Hardness test was performed with Vickers hardness tester, which gives evidence for the increase in hardness of machined surface due to the generation of WLT.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

This study aims (i) to assess the quest for tax education among working adults that pursuing off‐campus non‐accounting program, (ii) to analyze the level of tax knowledge among the working adults, (iii) to elicit the relevant tax topics to be taught should tax education be integrated into non‐accounting curriculum in higher education. We surveyed 450 working adults pursuing off‐campus non‐accounting program in one Malaysian public university. 190 usable responses were received. The survey found 64 per cent of the respondents were keen to learn taxation, and only 23.7 per cent of the respondents possessed high level of tax knowledge. The topics that they desired to learn the most are basic tax principles, personal taxation, tax planning for individuals and taxation for small business and company. The findings suggest that as we moved into the era of self‐assessment tax system, it is imperative for the accounting academics and the education authorities to seriously consider introducing tax education in non‐accounting curriculum in higher education.

This study examines the social acceptance and potential for residential solar integration in Abu Dhabi to support sustainable urban innovation and prosperity. A survey of 216 Abu Dhabi residents analyzed four scenarios for incentivizing household solar panel adoption. Results demonstrate high interest and preference for governmental financial support mechanisms to spur solar integration. This research contributes an assessment framework and citizen engagement insights that could inform smart city development strategies focused on renewable energy transformation across the Gulf region. As urban centers like Abu Dhabi strive to enact their visions for economic diversification and environmental sustainability, understanding citizen perspectives on innovations like distributed solar will be critical to align technology advancement with societal needs and values. The methodology and findings of this Abu Dhabi case study provide a model to catalyze stakeholder buy-in for new energy solutions, with potential applications for other Gulf cities aiming to transition their energy systems and build integrative renewables infrastructure to power next-generation smart development. This solar integration acceptance research lays the groundwork for continued scholarly discourse and policymaker collaboration around smart cities in the Gulf that realize multidimensional progress for society, economy, and environment.

This chapter focuses on the largely unexplored within- and cross-category spillovers between category kings and commoners within the field of the sharing economy. Going beyond the reputational spill-overs that are the main focus of extant literature, the authors also consider spill-overs in awareness, regulation, and customer usage between category kings and commoners, providing a holistic view of what it means to be a commoner in the same or adjacent category as a king. On the basis of a mixed-method study based on semi-structured interviews with UK sharing platforms and a consumer survey, the authors show that category commoners are affected by kings differently depending whether they are in the exact same sub-category or in an adjacent one within the same larger category. This chapter expands extant work on within and cross-category dynamics by taking the less popular perspective of the less visible category members. Studying these dynamics in the setting of the sharing economy also contributes to the authors’ knowledge of what enables/hinders the growth of a new field as a whole. Finally, the findings have important policy implications.

Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed.

To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method.

The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research.

Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.