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A thermosyphon solar water heating system with in‐tank auxiliary electric heater has been simulated according Malaysian hot water consumption profile using TRNSYS simulation program. The optimum value of a parameter is defined as the value which maximized the annual solar fraction of the system. This paper has a good deal of information concerning sizing of common components of thermosyphon solar water heater operated under certain condition (load volume, distribution profile and temperature). The results indicate that collector should be tilted at Ø + (→ 7° 37°) for all collector areas. Vertical storage tank has better performance in tropical climate (The average annual solar fraction for vertical was 88% and for horizontal was 82.9%).

The impact of diet quality on mental health has gained strong ground. However, most studies on this relationship were performed before COVID-19, a pandemic that was accompanied by high levels of psychological stress. Stress disturbs normal physiology, which makes studying diet quality and mental health under high stress a necessity. In addition, COVID-19 has been associated with disturbances in sleep and has increased the prevalence of mental health issues in women more than in men. Therefore, the purpose of this study was to assess food group consumption and sleep during different stages of the pandemic in relation to mental distress among men and women. Secondary data collected from adults 18 years or older between September 2018 and November 2021 was analysed. Temporal stages were divided into pre-COVID-19 (as a baseline), during the lockdown, and after the ease of restriction (two periods of different psychological stress levels). Regression analyses using a Difference-in-Difference (DID) event study or a Dynamic DID modelling were used. COVID-19 seemed to have a modulatory effect on food groups and mental health. The pandemic appeared to have either magnified the negative impact of certain food groups or changed the tolerance threshold for the beneficial ones. Across the board, women’s moods exhibited higher sensitivity to several food groups. COVID-19, a period of high psychological stress, differentially altered the impact of food on the mood of men and women; which proposes the need to further evaluate diet quality and mood under stressful conditions.

The purpose of this study paper is to identify and prioritize the factors affecting the knowledge flow in high-tech industries.

This research is applied in terms of purpose and descriptive-survey in terms of data collection method. This research has been done in a qualitative–quantitative method. In the qualitative part, due to the nature of the data in this study, expert interviews have been used. The sample studied in this research includes 35 managers and expert professors with experience in the field of knowledge management working in universities and high-tech industries who have been selected by the method of snowball. In the quantitative part, the questionnaire tool and DANP multivariate decision-making method have been used.

In this study, a multicriteria decision-making technique using a combination of DEMATEL and ANP (DANP) was used to identify and prioritize the factors affecting the knowledge flow in high-tech industries. In this study, the factors affecting the knowledge flow, including 8 main factors and 31 subfactors, were selected. Human resources, organizational structure, organizational culture, knowledge communication, knowledge management tools, knowledge characteristics, laws, policies and regulations and financial resources were effective in improving knowledge flow, respectively.

By studying the research, it was found that the study area is limited, and the previous work has remained at the level of documentation and little practical use has been done. In previous research, the discussion of knowledge flow has not been very open, and doing incomplete work causes limited experiences and increases cost and time wastage, and parallel work may also occur. Therefore, to complete the knowledge management circle and fully achieve the research objectives, as well as to make available and transfer the experiences of people working in this field and also to save time and reduce costs, the contents and factors of previous models have been counted. It is designed for high-tech industries, a model for the flow of knowledge.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

This systematic review aims to identify the key determinants of knowledge transfer (KT) activities among researchers.

This study systematically reviewed KT literature in academic settings from 1995–2023. The authors searched Web of Science and Scopus using predefined keywords, following PRISMA guidelines for screening and eligibility assessment. From 158 selected articles, the authors extracted data and conducted a descriptive analysis to map KT activities’ evolution. A narrative synthesis approach categorized determinants of researchers’ KT activities.

The systematic review findings revealed a general conceptual framework that categorizes the identified determinants of KT into four categories. At the individual level, the factors are related to the sociodemographic characteristics of the researcher (e.g. gender, age, experience), their psychological aspects (e.g. attitude, intrinsic motivation, intention) and personal characteristics (e.g. self-efficacy, communication skills). At the research team level, leadership style and team dynamics. At the organizational level, the findings emphasize university characteristics (e.g. size, structure and ranking), KT culture installed and university resources. At the inter-organizational level, the key determinants were funding sources, network strength and trust.

The studies included in our database were different in terms of contexts, country of the study, the disciplines of KT and the types of KT activities examined. This variety restricts the direct comparison of research findings thus the generalizability of our conclusions. Future research should focus on specific contexts, disciplines, countries or types of KT activities to provide generalizable findings.

A better understanding of all the factors influencing KT among university researchers is essential for several reasons. First, it will enable the government to develop effective policies to promote KT ecosystems. Second, universities can create strategies, policies and programs to support researchers’ engagement in KT activities. Finally, researchers can be more strategic in their KT efforts.

This systematic review contributes to the literature by providing a comprehensive conceptual framework that identifies KT determinants at different levels and fills a gap in the existing literature that only addresses specific aspects of KT determinants. This framework can be a theoretical reference for future empirical studies. Furthermore, it practically provides recommendations for different actors including, government, universities and researchers.

This paper aims to develop a causal feedback structure that explains the dynamics of entrepreneurship development in Iran’s photovoltaic (PV) technological innovation system (TIS) to design effective policy interventions for fostering PV innovation.

This study adopts the system dynamics approach to develop the causal structure model. The methodology follows a systematic method to elicit the causal structure from qualitative data gathered by interviewing several stakeholders with extensive knowledge about different aspects of Iran’s PV TIS.

Lack of technological knowledge and financial resources within Iranian PV panel-producing firms are the main barriers to entrepreneurship development in Iran’s PV TIS. This study proposes two policy enforcement mechanisms to tackle these problems. The proposed feedback mechanisms contribute to the domestic PV market size and knowledge transfer from public research organizations to the PV industry.

The proposed policy mechanisms aid Iranian policymakers in designing effective policy interventions stimulating innovation in Iran’s PV industry.

The main contributions of this study include conceptualizing the causal structure capturing entrepreneurship dynamics in emerging PV TIS and proposing policy mechanisms fostering entrepreneurship and innovation in PV sectors.

The increasing accumulation of synthetic plastic waste in oceans and landfills, along with the depletion of non-renewable fossil-based resources, has sparked environmental concerns and prompted the search for environmentally friendly alternatives. Biodegradable plastics derived from lignocellulosic materials are emerging as substitutes for synthetic plastics, offering significant potential to reduce landfill stress and minimise environmental impacts. This study highlights a sustainable and cost-effective solution by utilising agricultural residues and invasive plant materials as carbon substrates for the production of biopolymers, particularly polyhydroxybutyrate (PHB), through microbiological processes. Locally sourced residual materials were preferred to reduce transportation costs and ensure accessibility. The selection of suitable residue streams was based on various criteria, including strength properties, cellulose content, low ash and lignin content, affordability, non-toxicity, biocompatibility, shelf-life, mechanical and physical properties, short maturation period, antibacterial properties and compatibility with global food security. Life cycle assessments confirm that PHB dramatically lowers CO₂ emissions compared to traditional plastics, while the growing use of lignocellulosic biomass in biopolymeric applications offers renewable and readily available resources. Governments worldwide are increasingly inclined to develop comprehensive bioeconomy policies and specialised bioplastics initiatives, driven by customer acceptability and the rising demand for environmentally friendly solutions. The implications of climate change, price volatility in fossil materials, and the imperative to reduce dependence on fossil resources further contribute to the desirability of biopolymers. The study involves fermentation, turbidity measurements, extraction and purification of PHB, and the manufacturing and testing of composite biopolymers using various physical, mechanical and chemical tests.

This study explores the socio-demographic and psychological factors influencing pro-environmental behavior among Generation Z individuals. Aimed at deciphering the impact of socio-demographic characteristics on psychological drivers and identifying significant psychological factors affecting pro-environmental behavior, the research utilizes an inductive approach with a sample of 225 Generation Z members from Splitsko-Dalmatia County, Croatia. Data were collected via an online questionnaire focusing on attitudes, beliefs, and behaviors related to the environment. Findings reveal that gender, education level, and residential area significantly influence psychological drivers such as guilt, moral obligations, and self-identity, with women, individuals with higher education levels, and those residing in suburban areas exhibiting higher levels of these drivers. This study contributes to the understanding of pro-environmental behavior in Generation Z by highlighting the importance of socio-demographic variables and psychological factors, thus offering insights for promoting sustainable behaviors among this demographic.