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The objective of this study is to mitigate the risks of a blood shortage. The authors designed two simulation studies to identify the superior methodology that can decrease the impact of a massive national donor shortage.

The simulation designs are triggered by the COVID-19 pandemic. The first simulation examines the company’s choice of strategic partners (regionally and nationally), and the second inspects creating a national coordinated effort to organize a pooled blood inventory that would require blood centers to contribute a small percentage of their monthly donations to become a member.

The results indicate that both methods can significantly manage the risk of stockouts regardless of the availability of safety inventory in a blood center; however, although more effective in reducing the number of shortages per month, creating a national blood pool causes the shortages to be recognized earlier than desired.

The authors contribute to the literature by focusing on the potential risk of blood shortage because it directly impacts healthcare, hospitals’ costs and their ability to provide care. Though a handful of researchers have targeted the study of the blood supply chain, there is not any article that is similar to this study.

The application of statistical inversion theory provides a powerful approach for solving estimation problems including the ability for uncertainty quantification (UQ) by means of Markov chain Monte Carlo (MCMC) methods and Monte Carlo integration. This paper aims to analyze the application of a state reduction technique within different MCMC techniques to improve the computational efficiency and the tuning process of these algorithms.

A reduced state representation is constructed from a general prior distribution. For sampling the Metropolis Hastings (MH) Algorithm and the Gibbs sampler are used. Efficient proposal generation techniques and techniques for conditional sampling are proposed and evaluated for an exemplary inverse problem.

For the MH-algorithm, high acceptance rates can be obtained with a simple proposal kernel. For the Gibbs sampler, an efficient technique for conditional sampling was found. The state reduction scheme stabilizes the ill-posed inverse problem, allowing a solution without a dedicated prior distribution. The state reduction is suitable to represent general material distributions.

The state reduction scheme and the MCMC techniques can be applied in different imaging problems. The stabilizing nature of the state reduction improves the solution of ill-posed problems. The tuning of the MCMC methods is simplified.

The paper presents a method to improve the solution process of inverse problems within the Bayesian framework. The stabilization of the inverse problem due to the state reduction improves the solution. The approach simplifies the tuning of MCMC methods.

Used clothes supply chains are becoming increasingly complex, fragmented and less transparent due to rising volumes of discarded clothes and its dispersed reverse logistics operations across the Global North (GN) and Global South (GS). While it has a promising impact on circular economy and international trade growth, increasing exports of used clothes and overflowing landfills raise some negative concerns on its overall sustainability. This paper addresses the dichotomy that exists in terms of interpreting the sustainability credentials of used clothes supply chains.

A systematic literature review was carried out and 55 articles were examined to identify the triple bottom line (TBL) sustainability impacts of used clothes supply chains. TBL sustainability issues were identified, reflected through the lens of natural resource-based view and interpreted in the form of propositions.

The paper pinpoints seven TBL sustainability concerns and prescribes three sets of strategic resources required in glocal used clothes supply chains for mitigating these. These are (1) slowing the supply chain by tackling poor quality, overproduction and oversupply issues, (2) improving logistics/supply chain infrastructure and ecosystem collaboration and (2) embedding transparent environmental, social and governance (ESG) measures taken by both value chain actors and regulatory bodies, for embracing system-level sustainable development.

This is one of the first studies to analyse TBL sustainability of glocal north–south used clothes supply chains. The study is unique in terms of its scope and contribution to the sustainable supply chain literature.

This paper aims to provide a typical example of accounting for the carbon dioxide equivalent (CO₂e) in underdeveloped cities, especially for the Poyang Lake area in China. The accounting can increase public understanding and trust in climate mitigation strategies by showing more detailed data.

The paper uses the “Global Protocol for Community-scale greenhouse gas emission inventories (GPC)” method, a worldwide comparable framework for calculating urban CO₂e emission (CE). The empirical case is an underdeveloped city, Nanchang, in China.

The results show the total CE of Nanchang, containing the electricity CE of Scope 2, grew rapidly from 12.49 Mt in 1994 to 55.00 Mt in 2014, with the only recession caused by the global financial crisis in 2008. The biggest three contributors were industrial energy consumption, transportation and industrial processes, which contributed 44.71-72.06, 4.10-25.07 and 9.07-22.28 per cent, respectively, to the total CE. Almost always, more than 74.41 per cent of Nanchang’s CE was related to coal. When considering only the CEs from coal, oil and gas, these CEs per unit area of Nanchang were always greater than those of China and the world. Similarly, these CEs per gross domestic product of Nanchang were always bigger than those of the world. Thus, based on these conclusions, some specific countermeasures were recommended.

This paper argues that the CO₂e accounting of underdeveloped cities by using the GPC framework should be promoted when designing climate mitigation policies. They can provide more scientific data to justify related countermeasures.

This chapter offers novel insights into how global corporations can innovate to tackle the global waste crisis and gain sustainable competitive positions. Using two of the most prominent types of global waste crises – food and plastic wastes – we discuss the dilemma of food and plastic waste, why innovations in global firms are needed to address them, and argue that a different perspective among those firms is needed, one which conceptualizes the development, dissemination and use of innovations in waste management, and one which recognizes that innovations, thus, created contribute to advancing the creation of economic, environmental and social value. We conclude using an overarching conceptual framework that depicts the complexity of the new perspective.

Leadership cannot exist without followership. The phenomenon of direction and guidance, coaching and mentoring, has at least three components: the leader, leadership, and followers. With each component, the composition of purpose and goals, ethics and morals, rights and duties, and skills and talents is critically important. While the leader is the central and the most important part of the leadership phenomenon, followers are important and necessary factors in the leadership equation. Leaders and followers are engaged in a common enterprise: they are dependent upon each other; their fortunes rise and fall together. Relational qualities define the leadership–followership phenomenon. A major component of such a relationship is how the leaders create and communicate new meaning to followers, perceive themselves relative to followers, and how the followers, in turn, perceive their leader. This mutual perception has serious ethical and moral implications – how leader uses or abuses power, and how followers are augmented or diminished. This chapter features the essentials of ethical and moral, corporate executive leadership in two parts: (1) the Theory of Ethical and Moral Leadership and (2) the Art of Ethical and Moral Leadership. Several contemporary cases such as inspirational leadership of JRD Tata, Crisis of Leadership at Infosys, and Headhunting for CEOs will illustrate our discussions on the ethics and morals of corporate executive leadership.

Menstruation is a part of the female reproductive cycle that begins with adolescence. Menstruation is a natural change; it relates to several malpractices and misconceptions that may contribute to adverse health outcomes.

The authors have searched relevant papers using Google Scholar and PubMed to write this mini review.

During menstruation, poor hygiene maintenance can cause serious illness, which includes the urinary tract and reproductive tract infection. Menstruation management is a hygienic system, and it is essential for females because poor hygiene maintenance during menstruation can cause some infections and numerous sexually transmitted diseases. There are a few nanotechnology-based products that have come into the market to offer some relief to females during their periods.

This mini review will help researchers to design innovative female hygiene products that can relieve the discomfort caused to women during their reproductive age.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

This study aims to investigate the merits of the unified model of task-specific motivation (UMTM) in predicting transfer of training and to investigate (relationships between) changes in UMTM components over time. In doing so, this study takes the multidimensionality of transfer motivation into account.

The authors collected data among 514 employees of the judiciary who filled in the UMTM questionnaire directly after the training and after three weeks. The data were analyzed by means of structural equation modelling.

The outcomes show that transfer motivation predicts transfer intention and transfer of training over time. Moreover, the study shows that (change in) transfer motivation is predicted by (change in) personal and contextual factors identified by the UMTM as antecedents of motivation.

This study describes the first longitudinal evaluation of the UMTM in the literature and shows its applicability for predicting transfer of training. It is also one of the few studies that investigate transfer motivation multidimensionally and the role it plays for transfer of training. As such, this study informs other transfer of training models about the nature of transfer motivation and how transfer of training could be predicted.