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This article analyzes the representation ofhijaband of hijab-wearing women in two post-9/11 British literary texts, Leila Aboulela'sMinaret(2005) and Shelina Janmohamed'sLove in a Headscarf(2009). It discusses the strong resolve of the heroines of these works with regard to wearing the hijab despite opposition to it from within their peers, friends and family members as well as Islamophobic hostility to this most overt and visible marker of Muslim identity. While many women wear hijab instinctively and without question in order to follow their religion and cultural tradition, Najwa in the fictional workMinaretand Shelina in the memoirLove in a Headscarfdecide to wear it reflectively after long contemplation and much soul searching. Such experiences convincingly and creatively refute the assumption that hijab is imposed on Muslim women by male relatives and dispel the most widespread stereotype that it is synonymous with female oppression.

AbstractIn this article, we examine the relation between managerial ability and the use of supplier‐provided trade credit. The literature documents the positive effects of high‐ability managers, including more accurate earnings forecasts, improved earnings quality, and overall improvement in corporate disclosure policies. We argue that customers (those seeking trade credit) with high‐ability managers are better able to negotiate with suppliers, provide more transparent disclosure, and maintain strong relationships. Likewise, suppliers are willing to provide more trade credit to customers with high‐ability managers because of reduced information asymmetry, creating an environment of trust and transparency. Our empirical results show that suppliers extend more trade credit to customers with high‐ability managers and that this relation is more pronounced for financially constrained firms.

AbstractThus far, limited research has been performed on resilient supplier selection—a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise decision‐relevant information (DRI), this problem becomes even more difficult to solve with the classical optimization approaches. Possibility distribution‐based multicriteria decision analysis (MCDA) is a viable alternative approach for handling inherent uncertainty of imprecise DRI associated with the evaluation offered by a group of contradicting decision makers. However, prior research focusing on MCDA‐based supplier selection problem has been lacking in the ability to provide a seamless integration of numerical and linguistic evaluation criteria along with the consideration of multiple decision makers. To address these challenges, we present a comprehensive decision‐making framework for ranking a set of suppliers from resiliency perspective. The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers. We adapt two popular tools—single‐valued neutrosophic sets (SVNS) and interval‐valued fuzzy sets (IVFS)—and for the first time, extend them to incorporate both crisp and linguistic evaluations in a group decision‐making platform to obtain aggregated SVNS and IVFS decision matrixes. This information is then used to rank the resilient suppliers by using technique for order preference by similarity to an ideal solution method. We present a case study to illustrate the mechanism of the proposed algorithm. A sensitivity analysis demonstrates the strength of the proposed algorithm to generate alternative ranking scheme with respect to the shifting priorities of evaluation criteria and shows the reliability of the decision‐making framework.

Methane (CH_₄), a potent greenhouse gas, significantly contributes to climate change and global warming. Its impact over 100 years surpasses carbon dioxide (CO_₂) by 28 times. Addressing methane emissions, particularly from oil and gas production activities such as transmission pipelines, is imperative. One promising avenue is the development of reliable sensors to detect and mitigate methane leaks and prevent hazardous issues. Optical-based methods present notable advantages, including versatility and remote operation, making them pivotal in this endeavor. This review article provides a concise overview of optical-based methane identification technologies, encompassing sensing materials, absorption spectra, operational mechanisms, and recent advancements. Potential perspectives are explored, and inferences from this assessment are also derived. Emphasizing the significance of optical fiber-based methane detection methods, the authors advocate for further research to support ongoing efforts and foster innovation in this critical area.