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This paper aims to examine the impact of environmental, social and governance (ESG) disclosure and firm value (FV), as well as, pinpoints the role of the audit quality (AQ) as a moderating variable on such impact; where the authors hypothesize that AQ modulates the relationship between ESG disclosure and the FV.

Data of a sample of firms listed on the Egyptian Stock Exchange Market (EGX) were collected over the period of 2017–2021 and analyzed using the regression and 2SLS models.

The results suggested that: (1) the ESG has a significant positive impact on the FV in the EGX, and (2) AQ has a significant impact, as a moderating variable, on the relationship between ESG disclosure and FV.

The findings would help the Egyptian market authorities in realizing the importance of integrating ESG information within the financial reports of the listed firms. The findings could also help in developing effective disclosure procedures to provide shareholders with useful information.

This paper contributes to the literature regarding the ESG disclosure components and the FV value by considering AQ in testing such relationship.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

Within the framework of the valorization of natural resources, a characterization of the biochemical composition of the edible parts of Adansonia Digitata is applied. The antibacterial effect against bacteria is also realized and compared to some synthetic antibiotics.

The biochemical characterization is carried out according to the norms of the French Association of Normalization, methods of Association of Official Analytical Chemists (AOAC International) and gas chromatography (GC). The antibacterial activity is tested by disk diffusion on a solid medium. Parametric tests are used to compare the differences between groups and heat maps to show the expression of the mean inhibitions according to the studied parameters. Multivariate logistic modeling is applied to study the effect of extracts and antibiotics on bacteria.

Biochemical characterization showed a variable importance of proteins, fibers and total sugars, with the presence of highly desired fatty acids such as palmitic, oleic, stearic, linoleic and a-linolenic acids. This gives the tested parts important energy values, especially in the seeds very rich in fatty acids. Methanol proved to be a better extraction solvent than dichloromethane. Antibacterial activity showed that pulp and leaves extracted with methanol had quite similar inhibitory activities against Enterococcus faecalis ATCC29212 and that this effect was better than some antibiotics. Multivariate analysis showed that the leaves had a similar effect to antibiotics, and a significant effect against Staphylococcus aureus ATCC29213.

This important activity and the attractive nutritional value of this plant could justify its extensive use in the traditional pharmacopoeia.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Risks in implementing green building (GB) projects have emerged as a significant obstacle for GB development, especially in developing countries. In recent years, both academics and construction practitioners have paid considerable attention to the risks associated with GB. In this study, the authors aimed to create a comprehensive risk assessment model that considers three crucial risk features: impact level, probability of occurrence and risk manageability.

In the research, authors adopted the mean scoring and fuzzy synthetic evaluation method to assess GB risks. Based on expert assessments, this model can determine the significance of risk factors, risk groups and overall risk. Notably, this research applied the proposed model to assess GB risks in Vietnam by surveying 58 GB experienced professionals.

The findings revealed that GB risks are relatively high in Vietnam, implying that risk management is essential for GB projects to succeed. The results also showed that “lack of experience of GB designers” is the most critical factor, and “human resources risk in the design phase” is the top crucial risk group.

This study contributes a novel and practical model to help practitioners assess risks in GB projects. In addition, this research offers detailed GB risk evaluations in Vietnam and thus could be a valuable reference for construction practitioners and future studies.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

The aim of this study is to conduct a comparative analysis between Islamic and conventional banks in terms of whether Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. It also examines the role of capital in improving the performance and stability within the two banking systems.

This study uses 82 banks from MENA (Middle East and North Africa) region for periods across 2011–2020, and employs a dynamic panel data approach to examine the resilience within both banking systems during the Covid-19 pandemic.

The results show that the Covid-19 pandemic has a negative impact on conventional banks' stability. However, Islamic banks performed better and were less risky than conventional ones. Banks with high-quality capital are more effective at controlling their risks and improving their performance during the pandemic.

The results offer important financial observations and policy implications to many stakeholders engaging with banks. Actually, the findings of this study facilitate to the stakeholders and bankers to have an alluded picture about determinants of risk and performance. The results can be used by bankers’ policy decision-makers to improve and enhance their consideration for risk management, taking into consideration the type of banking systems.

Compared to the various studies on the stability of Islamic and conventional banks, researchers have not sufficiently addressed the effect of the Covid-19 pandemic on risk and performance. Moreover, none of these studies has examined if Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. This leads the authors to identify the similarities and differences between two types of banks in the MENA region in a pandemic shock context.

This was a cross-sectional study to assess the psychological response to quarantine during COVID-19 pandemic and knowledge level about the disease among inmates of a Custodial Center in Enugu, Nigeria. A total of 66 new prison inmates were assessed for psychological distress using the Kessler Psychological Distress Scale (K10); and inmates’ knowledge about COVID-19 using a COVID-19 Knowledge Questionnaire developed by the researchers.

Participants had a mean age of 28.39±8.71 years; 63 (95.5%) were male inmates and 3 (4.5%) were female inmates. Sixty-one (92.4%) were awaiting-trial inmates, 43 (65.1%) had psychological distress, and 35 (53%) had completed at least secondary school. COVID-19 questionnaire mean score was 3.82±3.33. Thirty-one (21.8%) had adequate knowledge of COVID-19 (7-10 score), while 35 (53%) and 10 (15.2%) had poor (0-3) and average (4-6) knowledge, respectively. Adequate COVID-19 knowledge level was significantly higher among those that completed at least secondary school (48.6%) than those who did not (12.9%). Though not statistically significant, adequate COVID-19 knowledge level was commoner among those without psychological distress (43.5%) than those with psychological distress (25.6%).

Considering responses to specific questions contained in the questionnaire, knowledge about some questions was relatively high. For example, a majority of the participants 45 (68.2%) correctly responded that death can be a complication of COVID-19; whereas half of them 33 (50%) correctly responded that regular hand washing with soap and water can help prevent the spread of the disease. Nevertheless, responses to some questions showed poor knowledge about the disease as 18 (27.3%) correctly answered that COVID-19 can affect the lungs, while 20 (30.3%) correctly responded that COVID-19 is caused by a virus.

Our study highlighted the need to have all quarantined persons educated about the disease for which they are being quarantined. It also provided the opportunity to raise awareness of COVD-19 among the inmates.

The Arab world is made up of 22 countries in the Middle East and North Africa. These countries are subjected to many social, economic, political and geographical vulnerabilities contributing to increased risks or ineffective emergency and disaster management. This paper examines these vulnerabilities, how they may impact the country's ability to face disasters, and how they can improve disasters' overall management.

The author selected Qatar, Oman to represent the Arab oil-rich countries, while Jordan, Egypt and Morocco to represent non-oil rich countries. The research was conducted in a qualitative, inductive systematic literature review based on a well-established systematic literature review methodology. Selected literature was based on its recency and the countries in question.

The review reveals population gaps that could threaten the social system in the event of a disaster in countries like Qatar and Oman. The majority of the countries lack community engagement and pre-planning for emergency preparedness due to social and cultural barriers. Other nations like Jordan, Egypt and Morocco are prone to long-lasting economic challenges due to lack of resources, mismanagement or corruption. The paper also highlights the need to raise the educational attainment among citizens to understand disaster risk reduction.

This study utilized the research method developed by Williams et al. (2017) to present a comprehensive systematic and comparative review of disaster management in the Arab world. Considering that disaster and emergency management has remained disproportionately unexplored in the Arab world, this paper reviewed several vulnerabilities and how those vulnerabilities may affect disaster and emergency management efforts in the Arab countries.

The present study focused on examining the effect of treated wastewater (TWW) on soil chemical properties. Also, efforts were made to compare the soil chemical properties under TWW irrigation with that under groundwater (GW).

During the years 2021 and 2022, surface and subsurface soil samples were randomly collected in triplicate by using an auger fortnightly at two depths (20 and 40 cm) from the selected spot areas to represent the different types of irrigation water sources: TWW and GW. Samples of the GW and the TWW were collected for analysis.

This study examines the impact of TWW on soil characteristics and the surrounding environment. TWW use enhances soil organic matter, nutrient availability and salt redistribution, while reducing calcium carbonate accumulation in the topsoil. However, it negatively affects soil pH, electrical conductivity and sodium adsorption ratio, although remaining within acceptable limits. Generally, irrigating with TWW improves most soil chemical properties compared to GW.

In general, almost all of the soil’s chemical properties were improved by irrigating with TWW rather than GW. Following that, wastewater is used to irrigate the soil. Additionally, the application of gypsum to control the K/Na and Ca/Na ratios should be considered under long-term TWW and GW usage in this study area in order to control the salt accumulation as well as prevent soil conversion to saline-sodic soil in the future. However, more research is needed to thoroughly investigate the long-term effects of using TWW on soil properties as well as heavy metal accumulation in soil.