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Everyone is extremely concerned about environmental protection and health safety due to the rise in living standards. Plant-derived natural dyes have garnered much industrial attention in food, pharmaceutical, textile, cosmetics, etc. owing to their health and environmental benefits. The present study aims to focus on the elimination of the use of synthetic dyes and provides brief information about natural dyes, their sources, extraction procedures with characterization and various advantages and disadvantages.

In producing natural colors, extraction and purification are essential steps. Various conventional methods used till date have a low yield, as these consume a lot of solvent volume, time, labor and energy or may destroy the coloring behavior of the actual molecules. The establishment of proper characterization and certification protocols for natural dyes would improve the yielding of natural dyes and benefit both producers and users.

However, scientists have found modern extraction methods to obtain maximum color yield. They are also modifying the fabric surface to appraise its uptake behavior of color. Various extraction techniques such as solvent, aqueous, enzymatic and fermentation and extraction with microwave or ultrasonic energy, supercritical fluid extraction and alkaline or acid extraction are currently available for these natural dyes and are summarized in the present review article.

If natural dye availability can be increased by the different extraction measures and the cost of purified dyes can be brought down with a proper certification mechanism, there is a wide scope for the adoption of these dyes by small-scale dyeing units.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Knowledge of how entrepreneurial alertness (EA) and effectuation impact small firms' performance in uncertain markets is limited. Suggesting effectuation as a mediation mechanism between EA and small firms' performance, the authors explore how entrepreneurs of small firms apply effectual logic to translate their individual alertness to market opportunities into firms' performance.

A set of hypotheses is tested by partial least squares analysis of survey data collected from small firms in New Zealand.

The results show that effectuation works as a mechanism that mediates a positive association between founders'/managers' alertness to market opportunities and small firms' performance.

Integrating EA with the effectuation theory, the authors contribute to the literature on new market opportunity development and firm performance. The authors argue that entrepreneurs concentrate on action and resources to further develop their marketing intelligence in developing new market opportunities. The authors also enhance the understanding of entrepreneurial marketing decision-making by small firms in a relatively small economy in the Asia–Pacific region.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Adopting a brand prominence perspective, this study aims to examine how textual and visual brand elements influence the success of crowdfunding campaigns for aftermarket offerings.

A Python-based Web scraper was used to collect data from 620 crowdfunding campaigns for aftermarket offerings hosted on Kickstarter. The linguistic inquiry and word count application programing interface was then used to analyze the linguistic aspects of these campaigns. A fixed effects regression model was used to evaluate the hypotheses.

Textual and visual brand prominence are positively associated with campaign success. Further, with some variations, both types of brand prominence augment the positive effects of narrative and graphic design elements on campaign success.

This study makes novel theoretical contributions to the literature on branding, crowdfunding and aftermarket products. The results also provide practical insights to aftermarket enterprises on creating compelling crowdfunding campaigns.

By analyzing the direct and synergistic effects of branding, narrative and graphic design elements on crowdfunding success, this study extends various literature streams and identifies several future research opportunities.

This chapter explores the concept of corporate social responsibility (CSR) from an Islamic Shari'ah-compliant perspective. It provides a comprehensive literature review on CSR with an explicit focus on the Islamic perspective of CSR, Islamic models of CSR, CSR practices in conventional and Islamic banks, and the consequences of CSR to Islamic banks. This chapter's main contribution lies in considering the current CSR literature from a Shari'ah perspective. Likewise, it identifies gaps in the current literature and suggests potential areas for future research. This chapter attempts to improve the understanding of how Islamic banks integrate social responsibility into their operations. The insights from this chapter are helpful to practitioners and academic scholars in Islamic finance, accounting, and CSR. This chapter emphasizes the importance of incorporating Islamic values and principles into CSR practices and encourages further research and investigation in this area.

Acute and chronic obstructive pulmonary disease (COPD) is a common and progressive lung disease that makes breathing difficult over time and can even lead to death. Despite this, there is no definitive treatment for it yet. This study aims to evaluate the studies on single and combined herbal interventions affecting COPD.

In this study, all articles published in English up to 2020 were extracted from the Web of Science (WoS) database and collected using Boolean tools based on keywords, titles and abstracts. Finally, the data required for bibliographic analysis, such as the author(s), publication year, academic journal, institution, country of origin, institution, financial institution and keywords were extracted from the database.

A total of 573 articles were analyzed. The number of papers in the lung disease field showed an upward trend from 1984 to 2021, and there was a surge in paper publications in 2013. China, Korea and Brazil published the highest number of studies on COPD, and Chinese medical universities published the most papers. Three journals that received the highest scores in this study were the Journal of Ethnopharmacology, International Immunopharmacology and Plos One. In the cloud map, expression, activation and expression were the most frequently researched subjects. In the plus and author keywords, acute lung injury was the most commonly used word. Inflammation, expression of various genes, nitric oxide-dependent pathways, NFkappa B, TNFalpha and lipopolysaccharide-dependent pathways were the mechanisms underlying COPD. Scientometric analysis of COPD provides a vision for future research and policymaking.

This study aimed to evaluate the studies on single and combined herbal interventions affecting COPD.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

The current study aimed to explore the extent of accounting graduates’ abilities (AGAs) in Jordan to use modern information technology systems (MITSs) to make optimal financial decisions (OFDs). To carry out this study, the descriptive analytical approach was used to collect and analyze data. Accordingly, an electronic questionnaire was designed and distributed to 5,000 students in 30 universities in Jordan. However, only 1,067 questionnaires were valid for analysis, which constituted the sample of study. To test the research hypotheses, the descriptive analysis and multivariate regression test were used. The results indicated that the majority of accounting graduates (AGs) in Jordanian universities (JUs) do not realize the importance role of MITSs to make OFDs. In addition, the biggest challenge face AGs in JUs is the weakness of current accounting curricula in JUs in terms of not including materials related to MITSs and the role of these systems in performing accounting tasks. Finally, the study found that the current abilities of AGs in JUs moderate negatively and significantly the positive relationship between the use of MITSs and making OFDs. Accordingly, the study recommended that JUs work on developing accounting curricula through including modern courses related to the use of MITSs in performing accounting tasks.