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When Ernie DePropso and I were research assistants at the Institute of Public Administration at Penn State in the early 1960s, we experienced two technological miracles (or so it seemed). The first was the Thermofax copier, and the second was a Monroe calculator with a memory key that could hold one constant. The Monroe cost $1,500 at the time—today a calculator superior to the Monroe can be had for less than $5.

The purpose of this paper is to investigate a fuzzy hybrid approach for ranking the flare gas recovery methods and allocating to refineries.

The proposed approach is containing four stages: in the first stage, experts' assessment is applied to identify relevant criteria and sub-criteria in the evaluation of flare gas recovery methods. In the second stage, the corresponding weights of criteria and sub-criteria are determined via fuzzy decision-making trial and evaluation (DEMATEL)-analytical network process (ANP) (DANP) method. In the third stage, the flare gas recovery methods are ranked using fuzzy weighted aggregated sum product assessment method (WASPAS) multi-criteria decision-making (MADM) technique. In the fourth stage, an optimization model is developed to allocate gas recovery methods to refineries while maximizing the total utility of allocations based on model constraints.

According to the results of fuzzy DANP method, technical and operational criterion was the most important followed by economic, political, managerial and environmental criteria. With respect to sub-criteria, international sanctions and political stability were the most important. The results of fuzzy WASPAS method indicated that gas injection was the first ranked alternative. Finally, the mathematical modeling allocated the recovery methods to five refineries of South Pars gas field in Iran based on budget and time constraints.

The proposed approach provides a systematic tool in the selection of flare recovery methods and allocation to refineries. This approach uses a new combination of fuzzy DEMATEL-ANP (DANP) method, fuzzy WASPAS method and mathematical programming. The approach is effectively implemented in a case study for ranking the flare gas recovery methods and allocating to refineries of South Pars gas field in Iran.

Although sustainability is a popular topic in the past decade, there is a lack of research to identify the driving factors for developing countries. The purpose of this paper is to investigate the driving factors for achieving eco-innovation.

An in-depth case study is employed to address the objective outlined above. A Chinese company with more than 1,200 employees was selected to address the research question.

By fuzzy decision-making trial and evaluation laboratory and interpretive structural modelling analysis, the driving factors for eco-innovation are identified, and the priority of different factors has also been extracted.

This is among the first studies to carry similar analysis regarding eco-innovation. More specifically, this is perhaps the first study to take this approach and to analyse this topic in a developing country.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

Claims and disputes are often unavoidable in the construction industry due to its unique and complex characteristics involving the massive investment of capital, lengthy project duration, and multiple project stakeholders. This chapter intends to identify the critical construction claims attributes, compare the perceptions of major stakeholders on different claim attributes, and investigate the contrast of the top five claim attributes between this study and previous ones. The literature review resulted in 48 claim attributes responsible for the construction project schedule delays. These attributes were then presented to Vietnam construction industry (VCI) practitioners in the form of a questionnaire survey. Data analysis was done based on the collected 113 qualified samples. Relative importance index (RII) was applied to determine the ranking of claim attributes. The results were that the top five causes of claims, that is, payment delays, mistakes by contractor during construction stage, delays in work progress by the contractor, financial failure of the contractor, and frequently changing requirements by the owner, lead to the schedule delays in VCI. These findings can assist the local industry practitioners and foreign companies seeking a share in the VCI market in understanding the causes of construction claims comprehensively and formulating the countermeasures to minimise their impacts and hence reduce the unnecessary losses and raise the likelihood of success as well as maintain sustainable relationships among stakeholders.

The purpose of this study is to evaluate the effects of partial or total substitution of milk fat by various levels of jojoba oil on the nutritional, physicochemical and sensory properties of Domiati cheese during cold storage at 6°C for 45 days.

The fat percentage of milk used for cheese manufacture was standardized to 4% fat. Five formulas were produced by replacing milk fat with 0%, 25%, 50%, 75% and 100% of jojoba oil, respectively. The physicochemical and sensory properties of cheese samples at 0, 15, 30 and 45th days of cold store were evaluated.

No significant differences in moisture, fat, protein and acidity values amongst the control samples and those samples supplemented with different concentrations of jojoba oil. Significant differences (p = 0.05) in total volatile fatty acids were detected amongst experimental cheese samples. The highest values were recorded for control sample containing 100% milk fat, while the lowest values were recorded for cheese sample containing 100% jojoba oil (p = 0.05). Total nitrogen (TN), water-soluble nitrogen (WSN) and trichloroacetic acid soluble nitrogen (TCA-SN) levels of experimental cheeses were nearly same as for control. The results of fatty acids profile showed that jojoba oil has a unique structure of straight monounsaturated alcohols. Significantly (p = 0.05), the highest cholesterol content was recorded for control sample containing 100% milk fat; however, the lowest (p = 0.05) value was recorded for cheese sample containing 100% jojoba oil. At the end time of storage period, control sample manufactured with 100% milk fat had the highest (p = 0.05) values of free fatty acids, peroxide and thiobarbituric acid, while the lowest values were recorded for cheese sample with 100% jojoba oil as a source of fat. The sensory evaluation results demonstrated that blending milk fat with jojoba oil improved the sensory properties of Domiati cheese.

It is concluded that substitution of 25%, 50%, 75% and 100% of milk fat by equal amounts of jojoba oil enhanced the nutritional and sensory characteristics of Domiati cheese.

It is concluded that Domiati cheese formulated with substitution of 25%, 50%, 75% and 100% of milk fat by equal amounts of jojoba oil enhanced the nutritional characteristics and improved the sensory properties of produced cheese.

The purpose of this paper is to analyze the job quality in Benin between 2007 and 2011. To do this, the study constructed a multidimensional measure of job quality and identified the determinants of the quality of the job. The measure adopted by the authors includes four dimensions: wages; extra-wage benefits and regularity of employment; conditions and career opportunities; and, finally, social security. Two methods, including the construction of measure of job quality and the estimation of determinants of the job quality index, were used to test the robustness of the effects. The results show that the quality of job improved slightly between 2007 and 2011 and that factors such as experience, the type of contract, the level of education, the formal character of the company and the work hours explain the job quality of workers in Benin.

The measure adopted by the authors includes four dimensions: wages; extra-wage benefits and regularity of employment; conditions and career opportunities; and, finally, social security. Two methods, including the construction of measure of job quality and the estimation of determinants of the job quality index, were used to test the robustness of the effects.

The results show that the quality of job improved slightly between 2007 and 2011 and that factors such as experience, the type of contract, the level of education, the formal character of the company and the work hours explain the job quality of workers in Benin.

The contribution of this study lies in its analytical approach and in the fact that it reinforces the knowledge that exists on this theme, which is still little studied in African countries.

In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

The purpose of this research is to explore the critical success factors of mobile animation games, by exploring the game itself, information systems, game motivation and promotional activities, as well as conducting research and analysis on mobile animation game players.

This research used the Analysis Hierarchy Process (AHP) method and the consistent fuzzy preference relationship for data analysis. In this study, collect 1,286 valid questionnaires through online questionnaire surveys. And comparing the two games “Legend Showdown” and “Tower of Gods and Demons”, players believe that the more successful mobile animation game is “Legend Showdown”.

Through experimental design, and the consistent fuzzy preference relationship for data analysis. The results found that the critical factors considered by the player in relation to the mobile animation game were firstly the information system, followed by promotional activities, game motivation and finally the game itself.

Because this research does not involve the concept of fuzzy theory at all, it is easy to produce subjective, uncertain and ambiguity issues when comparing pairwise. We recommended that follow-up researchers can use fuzzy semantic preference relations to solve this problem.

This study proposed a new approach that takes the critical factors for the mobile animation game. According to the research results, the critical success factor of mobile animation games is the information system, as it could provide a reference direction for game manufacturers when designing or formulating marketing strategies in the future.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.