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The increase in population in the world has brought an overpopulation in various parts of the world, with those cities where their countries are developing being most affected, mainly because these usually have corrupt governments that make urban planning difficult in an optimal way. The increase in the construction of urban areas generates problems in the environment due to the increase in carbon emissions, greenhouse gases, and the extraction of natural resources. Therefore, governments must promote urban innovation based on compliance with sustainable development that allows for preserving the ecosystem and the population's quality of life. Urban innovation must incorporate technologies that allow sustainable activities to be carried out efficiently and monitor the progress of environmental regulations.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.

Professional touching behavior (PTB), defined as intentional touching behavior that occurs between organizational members and that falls within the boundaries of appropriateness and professionalism in the workplace, is prevalent in organizations. Scholars from multiple disciplines, including human resources researchers, have acknowledged the importance of physical contact for facilitating interpersonal communication and relationship-building. However, PTB may not only elicit positive reactions from those who receive it but also negative reactions as well, with implications for social dynamics in organizations. PTB can, on the one hand, fulfill employees’ desires for interpersonal connection; at the same time, such physical contact at work can represent a threat to employees’ health. To explain the nature and implications of these divergent effects of receiving PTB, the authors draw upon sociometer theory and behavioral immune system (BIS) theory to model the emotional, cognitive, and physiological processes via which, and the conditions under which, receiving such behavior will result in socially functional responses and prompt subsequent prosocial behavior, and when PTB will be perceived as a health risk and prompt withdrawal behavior. The theoretical framework of this chapter expands our conceptual understanding of the consequences of interpersonal physical contact at work and has important human resources management (HRM) implications for organizational managers.

In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of artificial intelligence (AI). Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multimodal, and virtually real time. Such explosion in feedback content has also been accompanied by a rapid development of AI and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field.

Purpose – The numbers of health care transparency initiatives are increasing. Despite the growing availability of quality data, there seems to be a shortage of evidence about the effects and effectiveness of such initiatives. The aim of this systematic review is to document the effects of transparency, defined as the public release of quality performance data, on hospital care outcomes.

Design/methodology/approach – Through a review of the literature, we chose 46 keywords to use in our searches and focused on empirical studies published in English between 2010 and 2015. The use of combinations of these keywords in searches of four databases (PubMed, Scopus, Web of Science, and the Cochrane Library) generated 13,849 publications. The removal of duplicates and exclusion of studies that were not empirical or not relevant to transparency and quality resulted in 39 studies to be reviewed.

Findings – Our review of the literature confirmed the growth of health care transparency efforts, led by the United States, and found mixed results regarding the effects of transparency on hospital care outcomes. For example, mortality, the most frequently researched performance measure (n = 15), exhibited this mixed pattern by having studies showing a reduction (n = 4), increase (n = 1), mixed findings (n = 4), and no significant relationship (n = 6) as a result of public release. We also found a limited number of articles related to unintended consequences of public reporting. When compared with earlier systematic reviews, there seems to be a trend in the reduction of unintended consequences. Therefore, we recommend exploration of this potential trend in future studies empirically.

Practical Implications – The research findings summarized in this systematic review can be used to understand the results of existing transparency efforts and to develop future transparency initiatives that may better enhance hospital quality performance.

Originality/value – This is the latest and most comprehensive systematic review summarizing the effects of transparency of quality metrics on hospital care outcomes.