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Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.

This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.

The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.

The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to investigate the inventory-related optimization of an e-marketplace official store that works on a business-to-customer system when cashback promotion is used to attract more customers. Also, it proposes a new inventory model to maximize the e-commerce profit by optimizing the cashback amount and delivery period.

The proposed model assumes that customer demand is a function of price and delivery time and that price is affected by the cashback amount. The e-commerce operator has a profit-sharing contract with an e-payment company that facilitates the payment. E-commerce also builds collaboration under a cost-sharing contract with a supplier to ensure product delivery. A mathematical model is developed and the related theories are investigated. A numerical example illustrates the validity of the model and a sensitivity analysis is carried out to give useful insights.

A new inventory model for an e-market system has been introduced which shows the impact of a cashback promotion on the e-commerce business. This study shows that managers can optimize the cashback amount and its delivery time to get the maximum profit. In certain cases, the manager may set a high cashback amount (e.g. 100%) to attract customers to place more orders.

This study presents a new inventory model for today’s fast-growing e-commerce business; therefore, the results contribute to the understanding of promotion program practices and inventory management and provide insights to develop efficient e-commerce managerial decisions.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

Drawing on social information processing theory (SIP), this paper examines whether and how humble leadership affects employees' service improvisation (ESI) in the hospitality industry. Further, the study investigates the mediating role of psychological safety and the moderating role of creative self-efficacy (CSE).

To test the proposed relationships, the study adopts a cross-sectional design, administering questionnaires to 456 frontline staff in Egypt’s hospitality industry across three main sectors: restaurants, hotels and travel agencies. SPSS 27 and AMOS 22 were used for statistical analysis.

The study reveals a positive relationship between humble leadership and ESI, partially mediated by psychological safety. Furthermore, CSE not only strengthens the relationship between psychological safety and ESI but also enhances the indirect effect of humble leadership on ESI via psychological safety.

The study offers valuable insights for practitioners in the hospitality industry. To boost ESI, organizations can incorporate humble leadership attributes into their leadership development programs. Fostering a psychologically safe workplace would facilitate the positive impact of humble leadership on ESI. Recognizing CSE as a pivotal moderator underscores the importance of strategically selecting and developing employees with high CSE. These insights aim to cultivate a more service-oriented and effective workforce in the hospitality industry.

This study significantly contributes to leadership research in the hospitality industry by uncovering a previously unexplored link between humble leadership and ESI. Exploring psychological safety as a mediator and CSE as a moderator enhances our comprehension of how and when humble leadership influences ESI.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.

Although the development of public organisations and the continual enhancement of public services depend on the creative behaviour of public employees, it is uncertain from earlier studies how and when inclusive leadership (IL) affects innovative behaviours (IB). This study aims to resolve the inconsistency in the literature by applying social exchange theory to examine the effect of inclusive leadership on employees’ innovative behaviour, while also examining the mediating role of work engagement and the moderating effect of psychological safety within this relationship.

The research model’s analysis draws from a data set of 200 employee–supervisor dyads. Data was collected from employees across diverse public sector organisations in the United Arab Emirates (UAE).

The results demonstrate that IL indirectly (via WE) and directly has a positive influence on employees’ IBs. This influence is enhanced when employees feel safe and do not have to be concerned about negative consequences.

Our study highlights a less-explored sector, unveiling the motivations behind IB among public sector employees. Moreover, this study provides valuable insights within a non-Western context, offering a unique perspective on the intricate relationship between IL, PS, employee engagement and IBs in the UAE public sector.

The purpose of this study was to test a four-variable research model using organizational behavior, social and technical systems, and leadership theories. This study set out to determine how different leadership philosophies, such as transformational leadership and empowering leadership, affected innovation. In addition, the model’s mediating role for psychological empowerment was quantified.

This study used a quantitative approach, which is primarily a questionnaire, to gather information from 320 health-care sector workers at four public hospitals in the Basrah Governorate.

The majority of the relationships in the research model were shown to be positive by data analysis outcomes. The findings also showed how crucial the mediating variable was in preserving the link between the independent and dependent variables. Discussions were made on the theoretical and practical ramifications and suggestions for additional research.

This study concentrated on the application of contemporary leadership styles, gathered information on them and combined them into a single model to boost innovation. This study, which was conducted in the setting of the Iraqi health-care industry, stands out from previous studies because it used a large sample to provide conclusive and significant results, making it a valuable resource for academicians who seek to cultivate innovation.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.