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This paper aims to investigate the prepared modified alkyd and poly(ester-amide) (PEA) resins as antimicrobial and insecticide binders for surface coating applications.

Salicylic diethanolamine and 4-(N, N-dimethylamino) benzylidene glutamic acid were prepared and used as new sources of polyol and dibasic acid for PEA and alkyd resins, then confirmed by: acid value, FT-IR and 1H-NMR. The coating performance of the resins was determined using measurements of physico-mechanical properties. The biological and insecticide activities of the prepared resins were investigated.

The tests carried out revealed that the modified PEA and alkyd enhanced both phyisco-mechanical and chemical properties in addition to the biological and insecticide activities. The results of this paper illustrate that the introduction of salicylic diethanolamine and 4-(N, N-dimethylamino) benzylidene glutamic acid within the resin structure improved the film performance and enhanced the antimicrobial activity performance of PEA and alkyd resins.

The modified alkyd and PEA organic resins can be used as biocidal binders when incorporated into paint formulations for multiple surface applications, especially those that are exposed to several organisms.

Modified alkyd and PEA resins based on newly synthesized modifiers have a significant potential to be promising in the production and development of antimicrobial and insecticide paints, allowing them to function to restrict the spread of insects and microbial infection.

This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

This paper aims to compare the impacts of adaptive daily and seasonal cooling setpoints on cooling energy consumption and overheating hours to determine which approach is more effective in a desert climate, develop a methodology that effectively integrates passive strategies with adaptive daily and seasonal cooling setpoint strategies and assess how future climate conditions will impact these strategies in the medium and long term.

(1) Integrate adaptive thermal comfort principles into mechanical cooling systems to find the optimized cooling setpoint. (2) Evaluating the optimized cooling setpoints using a mixed-mode operation: In this step, the natural ventilation is activated by opening 40% of the window area when the indoor temperature is higher than 23°C and the outdoor temperature. Both the adaptive seasonal and daily setpoint strategies are evaluated. (3) If overheating hours exceed acceptable limits gradually add mitigation measures (e.g. exterior shading, cool roofs and green roofs). (4) If necessary, further reduce the cooling setpoint until acceptable limits are met. (5) Generate extreme future climate scenarios and evaluate the optimized model. (6) Implement additional measures and setpoint adjustments to maintain acceptable overheating hours in future conditions.

Although the building complies with the Dubai Green Code and uses external shading, its cooling energy consumption was 92 kWh/m² in 2021 with a 24°C setpoint. Using the adaptive seasonal setpoint combined with a cool roof, night cooling and cross-ventilation reduces cooling energy consumption by 52, 48 and 35% in 2020, 2050 and 2090, respectively, with overheating hours not exceeding 40 h annually. Using an adaptive daily setpoint strategy with the same mitigation measures is similarly effective; it achieved a 57, 42 and 34% reduction in cooling energy consumption in 2020, 2050 and 2090, respectively, while eliminating overheating hours.

The originality and value of this study lie in optimizing cooling setpoints without the effect of overheating hours in desert climates. Using the adaptive seasonal setpoint combined with a cool roof, night cooling and cross-ventilation reduces cooling energy consumption by 52, 48 and 35% in 2020, 2050 and 2090, respectively, with overheating hours not exceeding 40 h annually. Using an adaptive daily setpoint strategy with the same mitigation measures is similarly effective; it achieved a 57, 42 and 34% reduction in cooling energy consumption in 2020, 2050 and 2090, respectively, while eliminating overheating hours.

• (1)

  A methodology is developed to find the optimal cooling setpoints

• (2)

  Adaptive thermal comfort concept is extended for integration with a cooling system

• (3)

  Validation simulation model is used using certain building information

• (4)

  Climate change effect is studied using current and future warmer typical years

• (5)

  Effective passive summer mitigation measures are studied

A methodology is developed to find the optimal cooling setpoints

Adaptive thermal comfort concept is extended for integration with a cooling system

Validation simulation model is used using certain building information

Climate change effect is studied using current and future warmer typical years

Effective passive summer mitigation measures are studied

The single-supplier multi-retailer cold chain is a widely adopted type of supply chain in the real-world food industry. This paper aims to consider the problem of effectively designing and managing a single-supplier multi-retailer cold chain for fresh produce with deterministic demand to minimize the total cost, which includes cooling, loss of value and carbon emission costs.

The global stability index (GSI) method and the non-Arrhenius model are integrated to describe the behavior of food quality degradation. The power-of-two (PoT) policy is adopted in determining the coordinated replenishment policies for the suppliers and retailers, and an appropriate wholesale price structure that can achieve the coordination of the chain is presented.

The properties of the cold chain are uncovered, and an appropriate wholesale price scheme that achieves chain coordination with the optimal PoT decision is provided. In the numerical examples, different scenarios are investigated, and it is found that the cold chain parameters influence the optimal decisions in certain ways.

The PoT policy – an efficient policy to determine the replenishment strategy – has not been adopted in finding the solution of a single-supplier multi-retailer cold chain in the literature. Also, no study has compared the uncoordinated and coordinated cold chain. Moreover, in the existing literature, the wholesale price is usually a constant rather than having a coordinated scheme. This research aims to fill these research gaps.

This study aims to investigate the relationship between Chief Executive Officer (CEO) characteristics and audit report lag (ARL) in Egypt, an emerging economy characterized by high power distance and a culture of secrecy. The study utilizes a theoretical framework that integrates agency theory, stewardship theory, and upper echelons theory as the foundation for examining this relationship.

The sample consists of 587 firm-year observations from non-financial firms listed on the EGX100, covering the period from 2012 to 2019. The primary variable of the study (ARL) is measured using different proxies. The analysis utilizes both Ordinary Least Squares (OLS) and logistic regression models, with additional analysis considering CEO power and using board gender diversity as a moderating variable.

The study finds that CEO characteristics significantly affect ARL, demonstrating a negative association between CEO ownership, founder status, family ties, duality and ARL. These findings remain robust after a series of tests using alternative measures. Additional analysis reveals that CEO power is negatively and significantly related to ARL. Interestingly, the negative association between CEO characteristics and ARL is more pronounced in boards without female members.

Although extensive research has been conducted on the factors determining ARL, few studies have examined the impact of CEO characteristics on ARL, particularly in emerging economies such as Egypt. The business environment in Egypt is characterized by high power distance and a secretive culture, providing a unique context for this study.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

To assist Malaysian public-listed companies (PLCs) in preparing corporate social responsibility (CSR) reports, Bursa Malaysia Berhad (BMB) launched the second edition of the Sustainability Reporting Guide (SRG) in 2018. This new SRG edition has several additional requirements for CSR reporting (CSRR), the most important of which is a chapter on assurance which provides detailed guidance on how it may be carried out. This study aims to determine whether the new SRG edition influences the extent of CSRR, and whether such effect is moderated by the provision of assurance on CSRR. It also aims to identify whether amending CSRR regulations and providing assurance on such reporting indirectly influences firm value through the possible improvement in the extent of CSRR.

This study performed a content analysis of the CSRR of a sample of Malaysian PLCs that maintained their positions among the top 100 companies by market capitalization between 2017 and 2020 to determine the extent of CSRR for the two years before and two years after the implementation of the new edition of SRG. This study conducted different statistical analyses to indicate whether the implementation of the second edition of SRG has an effect on enhancing the extent of CSRR, and whether the provision of assurance on such reporting moderates such an effect. This study then used instrumental variable regressions to examine the influence of the predicted extent of CSRR on firms’ value measured by Tobin’s Q.

This study found that the implementation of the second edition of SRG has a positive and significant influence on the extent of CSRR. This effect is strengthened by the provision of assurance on CSRR. Instrumental variable regressions also indicate that enhancing the extent of CSRR affected by the second edition of SRG is linked to higher firm value.

To the best of the authors’ knowledge, this study is one of the first to assess the determinants and implications of CSRR among Malaysian companies after adopting the second edition of SRG.

This study examines the effect of external debt and domestic capital formation on economic development in Sub-Saharan African (SSA) economies.

Using the Dynamic Common Correlation Effects (DCCE) technique and the Driscoll and Kraay fixed-effect technique, this paper conducts a multidimensional assessment of external debt and domestic investment on economic development across a panel of 35 SSA countries from 1995 to 2018. The data utilized are sourced from the World Development Indicators (2021) and the United Nations Development Program (UNDP) database (2021).

The results reveal that domestic investment has a positive impact on economic development in SSA countries, consistent across all three dimensions of the human development index (income, education and life expectancy). However, external debt exhibits an adverse effect on economic development, consistently yielding negative outcomes for life expectancy, education and income.

Based on these findings, the authors recommend that SSA economies implement appropriate policies, such as reducing bureaucratic requirements and addressing corruption, to enhance domestic capital investment. Additionally, efforts should be directed toward channeling contracted debt into productive sectors like road construction and electricity provision.

This study is among the first to assess the impact of domestic investment and external debt on the three dimensions of human development outlined by the UNDP. Furthermore, it employs a robust econometric method that considers cross-sectional dependence (CD).

The primary goal of this review is to identify emerging themes in the cold supply chain (CSC) and their future research directions, methodologies, and theories.

The review looks at CSC related articles from Scopus database published in the years 2000–2020. Thereafter, bibliometric and co-citation analyses have been conducted to identify emerging themes, methodologies, and theoretical perspectives related to CSC management.

This study revealed a clear research gap in CSC literature with emerging themes relevant to diverse aspects. Primarily, the most prominent authors, methodologies, and theories were identified from bibliometric analysis. Next, we generated clusters to identify the insights of each cluster using co-citation analysis. Consequently, the significance of clusters concerning the number of articles, theoretical frameworks, methodologies, and themes was recognized. Finally, a few future research questions regarding emerging themes have been identified.

The importance of co-citation and bibliometric analyses in studying the evolution of research over a definite time is emphasized in this work. As per emerging themes, implementing digital technologies has increased the efficiency of traditional CSC and transformed it into digital CSC.

As per the authors' knowledge, this work is the first in literature to explore the significance of identifying emerging areas and future research directions in managing CSC through literature review based on bibliometric and co-citation analysis.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.