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In this chapter, the authors interrogate the structures, natures, processes, and variables that shape globalized collegiate desegregation. The authors pay attention to the history of segregation in South African culture, then proceed to current efforts to dismantle and rebuild the country’s educational enterprise. Drawing parallels with segregation policy in the United States, the authors argue that both nations may draw from global lessons about systemic global anti-Black oppression and its structural forms (e.g., apartheid, inequities in higher education). More specifically, the authors ground arguments in an analysis of the linguistic hegemony that continues to inculcate the college-aspiring students of South Africa. Understanding fundamental desegregation characteristics of racial hegemonic nations (e.g., United States) vis-à-vis racial and linguistic hegemonic nations (e.g., South Africa) is imperative to increase understanding of democratization of educational systems throughout the world.

The purpose of the study is to develop a cloud supporting model for green computing. In today's contemporary world, information technology (IT) plays a significant role. Because of the rapid growth of the IT business and the high level of greenhouse gas emissions, salient data centers are increasingly considering green IT techniques to reduce their environmental impacts. Both developing and underdeveloped countries are widely adopting green infrastructure and services over the cloud because of its cost-effectiveness, scalability and guaranteed high uptime. Several studies have investigated the fact that cloud computing provides beyond green information and communication technology (ICT) services and solutions. Therefore, anything offered over clouds also needs to be green to reduce the adverse influence on the environment.

This paper examines the rationale for the use of green ICT in higher education and finds crucial success variables for the implementation of green ICT on the basis of an analysis of chosen educational organizations and interviews with key academic experts from the Universities of Ethiopia, in general, and BuleHora University, in particular.

Finally, this paper described the design and development of a green cloud selection supporting model for green ICTs in higher educational institutions that helps cloud service customers choose the most green cloud-based ICT products as well as services.

This study may be a significant source of new information for green ICT design and implementation in higher education institutions to preserve the environment and its impact on human life.

The effect of throughflow and Coriolis force on convective instabilities in micropolar fluid layer heated from below for free‐free, isothermal and micro‐rotation free boundaries is investigated. Calculations are made using a lower order Galerkin approximation to solve the eigenvalue problem for stationary instability. It is observed that both stabilizing and destabilizing factors due to constant vertical throughflow can be enhanced by rotation.

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.

The Black Belt of the Deep South with rural areas in the states of Alabama, Georgia, and Mississippi has historically faced challenges that come with rural isolation, limited industry and financial services, poor healthcare options, and lack of educational opportunities. In the early 1990s, some institutions of higher education, including Historically Black Colleges and Universities, sought to increase educational opportunities for African Americans living in these areas. This chapter provides a historic case study of a doctoral education program that was founded to increase the number of education leaders, especially African Americans with advanced degrees, who would work in Alabama. As a historic case study, it provides a general overview of the founding of the program including mission and vision, a retrospective of the types of opportunities provided to doctoral students, and reflections on ways in which the program has improved the students' professional practices from both faculty and students. One component of this retrospective is to trace those students living in and working in the Alabama Black Belt. A key understanding undergirding the importance of this work is that as school administrators educational knowledge levels increase, so does the personal knowledge base that they can contribute to the communities in which they live and work. In this way, the educational reach of the doctor of education program leads to improvements in the larger Alabama Black Belt through both community and P-12 school interactions.

Nowadays, ample industries are fascinated to look for high strength and light weight materials for the development of robust parts. Because of light weight and high stiffness to weight ratio; usage of aluminum parts is growing rapidly, especially in automotive engineering. Process improvement of Al alloys and their grain structure refinement is the current area of interest in casting companies. In this research work, an investigation has been carried out to enhance the process improvement of die casting by optimization of various significant parameters and their refinement of grains by the effect of Nb-C novel grain refiner.

L27 orthogonal array (OA) has been considered to optimize the preferred casting input parameters such as molten metal temperature (°C), die temperature (°C), injection pressure (bar), Al-3.5Nb-1.5 C novel grain refiner and Ni alloying additions as key process parameters in order to increase the quality and efficiency of Al-9Si-3Cu aluminum alloy die casting by reducing the porosity formation.

It was observed that the porosity values have significantly decreased from 0.88% to 0.25% particularly at 0.1 wt.% of new grain refiner and 0.5 wt. % of Al-6Ni master alloy. As per the ANOVA results, it was observed that Al-3.5FeNb-1.5 C grain refiner (F value 2609.22), Al-6Ni alloying addition (F value 1329.13), molten metal temperature (F value 1002.43) and, injection pressure (F value 448.06) are the factors that significantly affects the porosity, whereas die temperature was found to be insignificant. The results show that new grain refiner is one the most significant factor among the other selected parameters. The contribution of the new grain refiner to the variation of mean casting porosity is around 57.74%. confidence interval (CI) has also been estimated as 0.013 for 95% consistency level to validate the predicted range of optimum casting porosity of aforesaid alloy.

To the best of the authors' knowledge, no study has been conducted in the past to investigate the combined effect of these die casting parameters and composition factors for the development of Al-Si robust cast parts. The paper represents original research and provides new information for the fabrication of die casting parts.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

The purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.

To determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.

The optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.

Machine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.

In this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

The Government of India dramatically altered the dynamic between business and society when it introduced the Companies Act 2013, which mandated firms to expend at least 2 per cent of average net profits on corporate responsibility (CR) programmes. This reconfiguration of social value creation may serve as a template for a closer and participatory relationship between the private sector and government in emerging economies and beyond. This paper aims to analyse how CR expectations have taken shape in the print media in India. Specifically, the authors ask the following: What are the dimensions of CR expectations in mainstream Indian newspapers?, and Why, according to the newspaper narratives, do corporations have these responsibilities?

In this qualitative study, the authors randomly selected and analysed 50 per cent (n = 442) of the newspaper articles that dealt explicitly with CR. The articles appeared in the top five Indian English-language newspapers and the top two Hindi-language newspapers between 1 January and 31 December 2015. Using Content Configuration Analysis (CCA), the authors developed a typology of CR expectations and analysed their associated justifications. Finally, they used CCA to analyse how this typology and its justifications connect to the two main stakeholders: the business sector and government.

The analyses reveal how the introduction of the Companies Act 2013 had a major impact on CR expectations by explicitly and legally casting the business sector as the engine of social development. The authors were able to describe how contextual and cultural dimensions frame evolving interests and societal demands towards corporations, and how difficult it may be for corporations to fulfil CR expectations that are well beyond their core business and that reach domains usually pertaining to government.

This study contributes an empirical exploration of media discourse on contemporary CR expectations in India and its associated notions of social value creation, and how these are shaped by various cultural and contextual influences. The authors discuss how this novel approach to CR modifies the relations between business and society, and they reflect on the opportunities and limits of this model for other emerging economies, which struggle to formulate a symbiotic relationship between business and society.