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Globally, higher education institutions have become reliant on a quality assurance (QA) system over the past four decades. QA is considered to be one of the most important drivers of continuous improvement and an essential tool for promoting excellence in teaching, learning, and research. It provides a framework for establishing and maintaining standards, and for assuring stakeholders that programs and services meet or exceed these standards. Despite the wide range of quality assurance models available to higher education institutions, there remains considerable debate over which model is the most effective, as well as what criteria should be used for evaluating the effectiveness of these models. Universities in the Middle East have tried to adopt different models of QA that can meet their needs and expectations and provide a framework for continuous improvement. A major objective of this book is to provide an overview account of the QA process as implemented in the higher education systems of a number of Middle East countries, including Egypt, Iraq, Lebanon, Saudi Arabia, and the United Arab Emirates. This book lays out essential theoretical and practical insights into the role of QA policies and practices in higher education in the Middle East countries and builds upon this idea to provide a blueprint for future academic leaders in these countries and other Middle Eastern countries to enhance their QA policies and practices and drive higher education in those countries forward.

The leisure industry is colossally impacted by varied types of crisis. Assessing the volatility; an attempt is made towards disaster planning and a response system. This chapter indicates an all-inclusive integrated approach to deal with disasters and narrates conceptual and latest factual findings in the space of disaster management. An efficient and self-equipped attraction demands a competent and efficient disaster management system in place.

This chapter devises measures to deal with the capacity of a destination during pandemic and proposes recovering strategies for the leisure business. Destination governance and disaster management techniques are well explored in the proposed chapter.

An imperative study of this nature will determine the role of cultural perceptions of varied risk and threats in a pandemic scenario. Innovative practices of disaster governance and Post-disaster recovery strategies are crucial mechanisms for the sustenance of tourism and hospitality sector.

The conceptual ideas and outcomes obtained in this chapter helps policy makers not only to find new strategies to placate the negative impacts of COVID-19 on the organic image of tourist destinations but also assists in accelerating the recovery timeframe just after the pandemic.

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 aims to answer whether foreign multinational corporations (MNCs) operating within the Chinese context differ from indigenous firms on several essential labor standards indicators: white- and blue-collar salaries, pension insurance, and working hours. In drawing upon neo-institutional and organizational imprinting theories and applying these to the Chinese context, the study addresses competing arguments regarding the expected effects of ownership type on these indicators. We employ seemingly unrelated regressions (SURs) to empirically examine a novel national survey of 1,268 firms in 12 Chinese cities. The regression results show that foreign MNCs do not provide uniquely beneficial labor practice packages to workers when compared with various indigenous firm types, including state-owned enterprises (SOEs), affiliate businesses of Hong Kong, Macau, and Taiwan, and domestic private enterprises (DPEs). Specifically, although MNCs provide relatively higher wage rates, they underperform relative to SOEs concerning social insurance. However, DPEs consistently underperform relative to MNCs across most indicators. The mixture of the results contributes important nuances to the application of neo-institutional and organizational imprinting theories to the Chinese context.

No carbon gas is released into the atmosphere in electricity production with wind energy. Therefore, this type of energy is significantly environmentally friendly. On the other hand, thanks to wind energy investments, a country can produce its own energy. This reduces the country’s foreign dependency on energy. Due to these positive aspects, increasing wind energy investments is very important for both the social and economic development of the country. The aim of this study is to define the most appropriate government support to improve wind energy investments. In this context, four different criteria are defined in this regard by making a detailed literature review. In the analysis process, fuzzy AHP methodology is considered. Within this framework, the comparative evaluations of three different experts are obtained. The findings indicate that providing loans with low-interest rate is the most appropriate government support for the wind energy investors. Additionally, it is also defined that tax deduction is another important issue for this purpose. However, it is also identified that appropriate location supply and purchase guarantee of the product produced have lower importance in comparison with the others. It is strongly recommended that governments should provide loans to the wind energy investors. This strategy can minimize the problem of high initial cost so that it can be much easier to attract the attention of the investors.

Competitiveness is an important personality trait that has been studied in various disciplines and has been shown to predict critical work outcomes at the individual level. Despite this, the role of competitiveness in groups and teams has received scant attention amongst organizational researchers. Aiming to promote future research on the role of competitiveness as both an adaptive and maladaptive trait – particularly in the context of work – the authors review competitiveness and its effects on individual and team stress and Well-Being, giving special attention to the processes of cohesion and conflict and situational moderators. The authors illustrate a dynamic multilevel model of individual and team difference factors, competitive processes, and individual and team outcomes to highlight competitiveness as a consequential occupational stressor. Furthermore, the authors discuss the feedback loops that inform the different factors, highlight important avenues for future research, and offer practical solutions for managers to reduce unhealthy competition.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.

The COVID-19 pandemic disrupted travel patterns, use of space and modal choice. Cities took actions in a way they did not before, trying to accommodate economic and travel needs with the goal of reducing the spreading of the virus. Active travel (AT) played an important role in accommodating travel needs and in increasing the resilience and environmental friendliness of the urban transport system. As cities gradually return to their normal life, transport planners must decide which role to assign to AT in future urban plans. In particular, whether to confirm the temporary policies incentivising AT enacted to counteract the reduction in the use of public transport or to return to the previous road space allocation that dedicated considerable urban space to motorised vehicular traffic. After reviewing the empirical evidence on the AT evolution during the various pandemic phases and illustrating the main policies planned and implemented at city level in many countries, this chapter summarises the lessons learnt, derives some policy suggestions, and identifies future research needs.