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The contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.

This paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.

This study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.

The model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Plant-based diets have been related to decreasing morbidity and mortality of many non-communicable diseases. The purpose of this study was to investigate the relationship between plant-based diets and lipid profiles and anthropometric indices.

This cross-sectional study was performed on 236 men and women selected from Shiraz health-care centers. This study used a 168-item food frequency questionnaire to calculate plant-based diet index (PDI), healthy plant-based diet index (hPDI) and unhealthy plant-based diet index (uPDI). Total cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol and triglycerides were measured. Furthermore, body mass index, a body shape index and conicity index (CI) were calculated after measuring weight, height and waist circumference.

Higher score of PDI was significantly related to higher triglycerides level (OR = 2.16; 95% CI: 1.04, 4.48; P = 0.03). In the fully adjusted model, there was a significant association between ABSI and hPDI (OR = 4.88; 95% CI: 1.17, 20.24; P = 0.03). A significant inverse association was observed between uPDI and high-density lipoprotein (HDL) (OR = 0.45; 95% CI = 0.21, 0.98; P = 0.03). Also, this study found a decreasing, but insignificant trend in relation of ABSI (OR = 0.72; 95% CI = 0.22, 2.34) and CI (OR = 0.41; 95% CI = 0.06, 0.56) with PDI.

Further studies are needed to explore the association of PDI with anthropometric indices and lipid profile and also to assess the potential causality of the observed associations. Plant-based diets according to their contents could affect triglycerides, HDL and anthropometric properties.

Hence, dietitians should consider the findings of this study such as the inverse effect of unhealthy plant-based diets on HDL and the relation between healthy plant-based diets and WC and abdominal obesity.

This study showed that adherence to a plant-based diet was related to higher triglycerides levels. Also, uPDI was inversely associated with HDL level. Furthermore, participants who adhered more to a healthy plant-based diet had higher abdominal adiposity.

The paper clarifies research gaps and future directions in building information modeling (BIM) research by analyzing research trends and publication patterns. It aims to (1) systematically categorize the vast array of BIM literature into coherent main topics, (2) identify the most and least explored areas and (3) propose directions for future research based on identified research gaps.

This study uses the Latent Dirichlet Allocation (LDA) method to manage large datasets and uncover hidden patterns in academic journals and conference articles. To clarify the scholarly focus, the main topics in BIM research are categorized into three groups: (1) primary areas of focus, (2) moderately explored topics and (3) least investigated topics.

The findings revealed 10 main topics (MTs) and 57 subtopics (STs), identifying key areas such as project design and management (20%), innovative construction technology (14%) and sustainable construction/life cycle management (14%). Conversely, it also highlighted underexplored areas like Facility/safety management and urban data development, suitable for future research.

While this work provides a structured overview of the BIM domain, it reveals opportunities for further exploring the complexity of the interrelation among interdisciplinary topics.

The novelty of this study is its extensive scope, analyzing over fifteen thousand BIM articles from 2013 to 2023, which significantly expands the literature scale previously reviewed. This comprehensive approach maps BIM research trends and gaps and also shows the hierarchical trend line of publications in each main topic, setting a benchmark for future studies.

Due to the limitations of conventional urban planning, it is essential to develop novel techniques of urban futruing. This paper aimed to use the scenario technique to create four plausible narratives of the future of Isfahan. Also, the authors described the problems of city foresight in the Global South.

This paper chronicles the Schwartzian steps taken to build explorative scenarios of Isfahan City in Iran in 2040. After using a STEEPV (Social, Technological, Environmental, Economic, Political, Value) analysis, the authors prioritized the collected variables by combining influence diagrams, the iceberg metaphor and an expert-based survey. Once the key uncertainties were derived, four scenarios were developed and discussed.

Through thematic analysis of the official visions of Isfahan’s future and the juxtaposition of these narratives with insight yielded in the scenario-development process, the paper concludes that the Northernness of the prevailing urban imaginaries, uncritical mimetic benchmarking, depoliticization of urban futures and the decorative reductionistic visions colonize urban futures in Isfahan/Iran. Critical/deconstructive city foresight and application of discomfort/ignorance criteria in the generation of scenarios can improve the rigor and quality of city foresight in the Global South.

The application of city foresight in the Global South has been limited. The paper is a step toward bridging this gap and providing some recommendations on how city foresight in the Global South might differ from its counterparts in the Global North.

The COVID-19 pandemic has caused significant mortality and morbidity worldwide. However, the role of dietary patterns as a potential risk factor for COVID-19 has not been well established, especially in studies with large samples. Therefore, this study aims to identify and evaluate the association between major dietary patterns and COVID-19 among adults from Iran.

In this cross-sectional study, the authors included 9,189 participants aged 20–70 who participated in the Yazd Health Study (YaHS) and Taghzieh Mardom-e-Yazd study (TAMIZ). They used factor analysis to extract dietary patterns based on a food frequency questionnaire (FFQ). Then, they assessed the relationship between these dietary patterns and the odds of COVID-19.

This study identified two major dietary patterns: “high protein and high fiber” and “transitional”. Participants in the highest tertile of the “high protein and high fiber” dietary pattern, which included vegetables, fruits, dairy and various kinds of meats such as red meat, fish and poultry, had a lower odds of COVID-19 compared with those in the lowest tertile. However, the “transitional” dietary pattern did not affect the risk of COVID-19.

In conclusion, a “high protein, high fiber” diet may lower the odds of COVID-19. This study suggests that dietary patterns may influence the severity and spread of future similar pandemics.

The enrichment of bread with non-digestible prebiotic ingredients may exert health-promoting effects and provide healthier food choices for those suffering from metabolic diseases, including obesity and diabetes. The purpose of this study was to investigate the effects of ß-glucan and resistant starch incorporation on the glycemic index (GI) and glycemic load (GL) of white bread.

Seven different formulations of prebiotic bread were produced using different proportions of ß-glucan (0.8, 1 and 1.2 per cent), resistant starch (5.5, 8 and 10.5 per cent) and the combination of resistant starch and ß-glucan in a ratio of 4:0.5.

The GI and GL of the prebiotic bread prepared with 1 per cent ß-glucan (w/w) were 55.7 and 7.8, respectively, whereas those of the prebiotic bread prepared with 8 per cent resistant starch (w/w) were 64.8 and 8.42, respectively, with both breads having significantly lower GI and GL values than the control (P < 0.05). It was concluded that the incorporation of 1 per cent ß-glucan may be beneficial in producing prebiotic bread with both low GI and low GL.

Although white bread is a main food source in human diet, its high GI and GL make it an unhealthy food choice. The incorporation of ingredients with prebiotic effects, such as ß-glucan and resistant starch, can improve the nutritional value of this product by lowering its GI and GL.

Considering innovation and its improvement as an essential strategy to enable organizations to continue their lives in the new competitive environment leads to a focus on employees' self-development as a factor that affects human resource agility (HRA) and the tendency toward organizational innovation. Consequently, the purpose of the study was to explain the impact of self-development on the tendency toward organizational innovation with the role of the mediator, HRA in higher education institutions as one of the most important and vital organizations in any society.

The study was an applicable one with the quantitative approach using the descriptive–correlative method. The population consisted of 477 nonacademic employees of Kharazmi University among whom 214 ones were selected as the sample group, using a simple random sampling technique. Data were collected through the self-development, HRA and the tendency toward organizational innovation questionnaires and then analyzed using the structural equation modeling approach.

The study findings revealed a positive impact of self-development on the HRA (γ = 0/79) and HRA on the tendency toward organizational innovation (β = 0/6). Also, self-development with mediating HRA impacts the tendency toward organizational innovation (β = 0/58). Finally, self-development had no direct impact on the tendency toward organizational innovation.

Taking the circumstances of doing this research into consideration, if there were the opportunity to do the research on the staffs of more than one university simultaneously and categorize the university staff into executives, managers and experts, more favorable results could be achieved. Also, considering group and organizational factors with the attention to the self-development approach and its factors would provide more awareness-training information on the higher education system in Iran. For future researches, both the individual and group factors are suggested to be surveyed and compared, to assess the weight and impact of these factors all together and to provide an adequate clarification of the role of the group and the organization. Finally, in future studies, it is also recommended that a qualitative approach be used to reach deeper clarifications on the aspects of these variables in the context of higher education.

These findings have major practical implications concerning the higher educational settings. The findings of this study must give significant and practical insights for policymakers of universities and other higher education stakeholders, as well as recommendations to the academic community for further research in this area. First, they should recognize that nonacademic staff members are professional employees who contribute to improving organizational innovation. Higher education must focus on designing and implementing successful mechanisms and a well-planned self-development program that can help and promote the self-development approach among all staff. If the above-mentioned programs are designed based on the employees' needs analysis, they will get trained in a way to enhance mental and behavioral flexibility. The programs with such an approach can result in the proactive, adaptive, resilient behavior and agility of HR.

The model for this study has integrated and prioritized the key innovation drivers that would help universities design, adopt and implement policies and practices that facilitate and encourage improvements and adaptation to a fast-paced environment. Furthermore, the convincing reason for the significance of the current research is that although several types of research have been carried out on each of these three variables in different contexts separately, very few studies, like this, have directly examined the correlation between these three variables among the non-academic staff in higher education institutes. So, given the importance of the issue and rare availability of evidence in this regard, the authors were intrigued to discover whether the self-development through the mediation of HRA could reinforce and strengthen the tendency toward organizational innovation and whether HRA could be an appropriate mediator of the relationship between self-development and the tendency toward organizational innovation among the nonacademic staff of Kharazmi University as one of the most prestigious and celebrated universities in Iran.

The purpose of this study is to explore the extent to which white-collar crime makes sense. Understanding business offending reflects the degree of sensemaking among respondents in the current survey research. Making sense implies a number of factors that influence understandability. An understandable act is not necessarily acceptable or justifiable. At a university in Iran, criminal law and criminology students answered a questionnaire regarding their extent of understanding of business offenders.

The research method is the use of experimental data using a questionnaire in one of the units of the Islamic Azad University in Iran, where 300 students were invited to respond to an online survey.

The respondents found it on average understandable that top executives and other privileged individuals abuse their positions to commit financial crime when they have problems with their personal finances, when the business struggles financially and faces the threat of bankruptcy, and when they offer bribes in corrupt countries to obtain business contracts. The extent of understandability varies with a number of propositions in the convenience theory.

This article has not been submitted elsewhere and is original.

Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.

Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.

The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.

There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.

The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.

This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.