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This research study uses the theory of planned behavior to investigate the factors such as environmental concern, perceived control behavior and subjective norms that foster green purchasing intention in the developing market, with the moderating influence of environmental knowledge.

Convenience and purposive sampling were used to obtain data from 418 respondents, which were analyzed using structural equation modeling.

The findings revealed that environmental concern had a significant impact on the subjective norms, green attitude and perceived behavioral control; subjective norms had a positive impact on the green attitude, did not on perceived behavioral control and green purchase intention directly; perceived behavioral control had a significant impact on green attitude directly but did not green purchase intention; green attitude had a significantly impacted the green purchase intention. Environmental knowledge had a strong moderation association between green attitude and green purchase intention.

The study explores the complex relationships between various factors, including environmental concern, subjective norms, perceived behavioral control, green attitude and environmental knowledge, and their impact on green purchase intention which can provide a more nuanced understanding of the drivers for sustainable consumption. Additionally, the study's finding on the moderating effect of environmental knowledge on the relationship between green attitude and green purchase intention may be a novel contribution to the existing literature.

This study aims to develop and evaluate a modular smart garment design framework that simplifies the technical content associated with smart garment design.

Smart garment design challenges were first identified through literature review and interviews. Then, a modular framework and toolkit was created to address these challenges. Finally, workshops were held to evaluate the modular toolkit.

Interviews highlighted the need for easier attachment of hard devices to soft textile materials, simpler electrical connection creation and straightforward device selection. A modular framework was proposed and divided into four elements: (1) the Central Computation Module, (2) Peripheral Electrical Modules, (3) Securely Attaching Modules with Substrates and (4) Managing Intra-garment Connections. Workshops showed the modular framework had statistically significant improvements in function and certain ease ratings when compared to non-modular components.

This research identified specific technical challenges faced by smart garment designers and alleviated them through a modular smart garment framework that in workshops outperformed non-modular components in key function and ease ratings.

This study aims to assess the association of Healthy Eating Index (HEI) with levels of fasting blood sugar (FBS) and lipid profile in normoglycemic and elevated FBS patients.

This case-control study was conducted on 144 participants, namely, 72 normoglycemic subjects (FBS < 100 mg/dl) and 72 high-glycemic patients (FBS ≥ 100 mg/dl) aged 20–60 years of age, who were selected from the nutrition and diet clinics in Tehran city. The dietary intake was collected by using a validated food frequency questionnaire to determine the HEI score.

The mean±SD age and body mass index of participants were 47.1 ± 12.7 years and 29.6 ± 6.0 kg/m², respectively. The median (interquartile range) of HEI scores in the normoglycemic group and the high-glycemia group were 19.34 (15.24–24.31) and 16.53 (13.35–24.07), respectively. In the overall population, the findings of the multi-variable linear regression model indicated a positive association between the HEI score and high-density lipoprotein-cholesterol (HDL-C) (ß = 0.34; 95%CI: 0.05–0.64, P = 0.01). However, there is no significant association between HEI and HDL-C in normoglycemic (ß = 0.19; 95%CI: −0.31, 0.69, P = 0.45) and hyperglycemic subjects (ß = 0.28; 95%CI: −0.10–0.66, P = 0.15). Furthermore, the association of HEI with levels of FBS, triglycerides (TGs) and low-density lipoprotein-cholesterol (LDL-C) was not significant in any of the analyzed groups, including the total population, normoglycemic individuals and hyperglycemic subjects.

This study was the first study to assess the role of HEI and its components with levels of FBS and lipid profile in normoglycemic and hyperglycemic individuals in Iran. The findings suggested that higher adherence to HEI may be associated with an increase in the HDL-C level. However, HEI could not predict FBS, TGs and LDL-C levels in the adult population.

The present article seeks to further the analysis by examining the epitext employed by the press seeing as the epitext in the digital spaces might have given Animal Farm and its Thai re-translations a new lease on life.

The interest in the study of translation and paratext has primarily been in analysing peritextual material of translated texts, not on the epitext, the distanced elements located outside the book. To add to a limited amount of research into epitext, this study focusses on the element that is external to the published re-translations: the news items published by the media in the Thai and English languages from May–June 2019, immediately after the Thai PM’s book recommendation.

These news items, as an epitextual element, primed, explained, contextualised, justified and tempted readers. The “Afterlife” of Animal Farm in Thailand is sustained by political upheavals and re-translations. Rather than through their textual qualities, the re-translations of Animal Farm compete with each other through epitext.

In discussing literary re-translation of Animal Farm in the digital age, Genette’s categories of paratextual field are not without their merits. The materials examined in this article are posted by web administrators with collective identity or institutional affiliation. In some of these news items or articles, materials created by different paratextual creators are selectively coalesced within a singular textual space. The site users or news readers encounter various elements in the texts that had been curated by journalists. In other words, these elements had been consciously crafted.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.