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This study aims to investigate the impact of audit fees on audit quality, the impact of audit quality on firm value and whether these effects are conditional on audit tenure by bringing evidence from an emerging market.

Different regression techniques are used, such as logistic regression, probit regression, ordinary least squares regression and fixed effects regression. The authors used panel data of 80 nonfinancial Egyptian-listed firms over 2016–2020.

The authors found a significant positive relationship between audit fees and audit quality and a significant positive relationship between audit quality and firm value. Furthermore, the authors found that the positive relationship between audit fees and audit quality is less pronounced for higher audit tenure firms. Finally, the authors also found that the positive relationship between audit quality and firm value is stronger for lower audit tenure firms.

To the best of the authors’ knowledge, this is the first study to bring evidence from an emerging African market about the joint association between audit tenure, audit fees, audit quality and firm value. It provides beneficial insights to regulators regarding the possibility and the benefits of improving audit quality, which is critically needed in contexts with weak governance systems.

The purpose of this study is to examine the impact of firm financial and operational characteristics on the level of forward-looking information disclosure (FLID) by Egyptian-listed non-financial companies. The present research also aims to investigate the moderating role of gender diversity on the board of directors.

The sample incorporates the non-financial companies included in the EGX 100 of the Egyptian Stock Exchange (ESE), whose reports were available during the study period from 2013 to 2018. The final sample comprises 49 companies with 294 observations. Statistical analysis is performed using multiple regression analysis.

This study found a significant positive impact of return on assets, leverage, company size and age on the level FLID, while external audit firm type and industry were found to impact the level of FLID negatively. Further, the board gender diversity (BGD) is found to have a moderating impact as it strengthens the effect of financial and operational characteristics on the level of FLID.

The present study has some implications for Egyptian companies, investors in the Egyptian market and regulators in emerging economies, which include paying more attention to BGD when selecting the board members by companies as well as following up the female representation in all the listed companies by regulators.

To the best of the authors’ knowledge, this is the first study to investigate the moderating role of BGD and its impact on the level of FLID in emerging markets. This extends the disclosure literature as the present study brings new evidence from an emerging market regarding BGD moderating role as early research concentrated on the direct impact of BGD on the level of FLID.

The aim of this paper is to evaluate the effect of surface treatment on slurry erosion behavior of AISI 5,117 steel using artificial neural network (ANN) technique.

The slurry erosion wear behavior of electroless nickel-phosphorus (Ni-P) coated, carburized and untreated AISI 5,117 alloy steel was investigated experimentally and theoretically using ANN technique based on error back propagation learning algorithm.

From the obtained results, it can be concluded that the proposed AAN model can be successfully used for evaluating slurry erosion behavior of the Ni-P coated, carburized and untreated AISI 5,117 steel for wide range of operating conditions and Ni-P coating and carburizing improve the slurry erosion resistance of AISI 5,117 steel; however, the coating is more efficient.

Slurry erosion is a serious problem for the performance, reliability and service life of engineering components used in many industrial applications. To improve the performance of these components, engineering surface technologies have been attracting a great deal of attention. The extent of slurry erosion is dependent on a wide range of variables. To account all variables that effect on erosion behavior, prediction of erosion behavior by soft computational technique is one of the most important requirements. ANN has the ability to tackle the problem of complex relationships among variables that cannot be accomplished by traditional analytical methods.

The purpose of the study is to obtain explicit formulas to determine the stability of periodic solutions to the new system and study the extent of the stability of those periodic solutions and the direction of bifurcated periodic solutions. More than that, the authors did a numerical simulation to confirm the results that the authors obtained and presented through numerical analysis are the periodic and stable solutions and when the system returns again to the state of out of control.

The authors studied local bifurcation and verified its occurrence after choosing the delay as a parameter of control in Zhou 2019’s dynamical system with delayed feedback control. The authors investigated the normal form theory and the center manifold theorem.

The occurrence of local Hopf bifurcations at the Zhou's system is verified. By using the normal form theory and the center manifold theorem, the authors obtain the explicit formulas for determining the stability and direction of bifurcated periodic solutions. The theoretical results obtained and the corresponding numerical simulations showed that the chaos phenomenon in the Zhou's system can be controlled using a method of time-delay auto-synchronization.

As the delay increases further, the numerical simulations show that the periodic solution disappears, and the chaos attractor appears again. The obtained results can also be applied to the control and anti-control of chaos phenomena of system (1). There are still abundant and complex dynamical behaviors, and the topological structure of the new system should be completely and thoroughly investigated and exploited.

The purpose of this paper is to carry out erosion wear investigation on high-velocity oxy-fuel (HVOF)-deposited 86WC-10Co4Cr and synergistic Ni/Chromia powder (i.e. 80Ni-20Cr₂O₃) on AISI 316L.

Design of experiments-artificial neural network (DOE-ANN) methodology was adopted to calculate the erosion wear. Taguchi’s orthogonal array L16 (42) was used to perform set-of-erosion experiments followed by lower-the-better rule. The artificial neural network (ANN) model is used on erosion wear data obtained from the experiments.

Experimental results indicate that 86WC-10Co4Cr provided better erosion wear resistance as compared to Ni/Chromia. The erosion wear of 86WC-10Co4Cr and synergistic Ni/Chromia coatings increases with an increase in time duration, solid concentration and time. The magnitude of erosion generated by ashes was comparatively lower than sand. The arithmetic mean roughness (Ra) of finished AISI 316L, 86WC-10Co4Cr and Ni/Chromia coating was found as 0.46 ± 0.13, 6.50 ± 0.16 and 7.04 ± 0.23 µm, respectively. Surface microhardness of AISI 316L, 86WC-10Co4Cr and Ni/Chromia coating was found as 197 ± 18, 1,156 ± 18 and 1,021± 21 HV, respectively.

The present results can be useful for estimation of erosion wear in slurry pumps used in mining industry for the conveying of sand and in thermal power plants for the conveying of ashes to the dyke area.

The erosion wear of HVOF-sprayed 86WC-10Co4Cr and Synergistic Ni/Chromia powders was studied experimentally as well as predicted by the ANN model, and wear mechanisms are well discussed by scanning electron micrographs.

This study aims to clarify the relationship between inclination angle of hot surface of CPU and its temperature in absence and presence of aluminum foam as a cooling system. It proposes application of the artificial neural [multi-layer perceptron (MLP) and radial basis function] networks and adaptive neuron-fuzzy inference system (ANFIS) to predict interface temperature of central processing unit (CPU)/metal foam heat sink.

To provide a consistent set of data, the surface of an aluminum cone with and without installing Duocel aluminum foam was heated in a natural convection using an electrical resistor. The hot surface temperature was measured using five K-type thermocouples (±0.1°C). To develop the predictive models, ambient temperature, input power and inclination angle are taken as input which varied from 23°C to 32°C, 4 to 20 W and 0° to 90°, respectively. The hot surface temperature is taken as the output.

The results show that in the presence of foam, the hot surface temperature was less sensitive to the variations of angle, and the maximum enhancement of the heat transfer coefficient was 23 per cent at the vertical position. Both MLP network and ANFIS are comparable, but the values predicted by MLP network are in more conformity with the measured values.

The effect of metal foam on the inclination angle/hot surface temperature dependence is identified. The optimum angle is clarified. The applicability of the MLP networks to predict interface temperature of CPU/heat sink is approved.

In this study, artificial neural networks (ANNs) are constructed and validated by using the bearing data generated numerically from a thermohydrodynamic (THD) lubrication model. In many tribological simulations, a surrogate model (meta-model) for obtaining a fast solution with sufficient accuracy is highly desired.

The THD model is represented by two coupled partial differential equations, a simplified generalized Reynolds equation, considering the viscosity variation across the film thickness direction and a transient energy equation for the 3-D film temperature distribution. The ANNs tested are having a single- or dual-hidden-layer with two inputs and one output. The root-mean-square error and maximum/minimum absolute errors of validation points, when comparing with the THD solutions, were used to evaluate the prediction accuracy of the ANNs.

It is demonstrated that a properly constructed ANN surrogate model can predict the THD lubrication performance almost instantly with accuracy adequately retained.

This study extends the use of ANNs to the applications other than the analyses dealing with experimental data. A similar procedure can be used to build a surrogate model for computationally intensive tribological models to have fast results. One of such applications is conducting extensive optimum design of tribological components or systems.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0109/

This study aims to examine how female directors on corporate boards and audit committees, and auditor affiliations (Big 4 versus Egyptian firms affiliated with foreign auditors), influence audit fees. This examination is driven by the global call for increased female representation in leadership roles and its potential implications for audit quality and financial transparency.

A sample of non-financial companies listed on the Egyptian Stock Exchange is used for the period 2011–2020. The authors used multivariate regression models, the Heckman two-stage and tokenism to support the analysis.

The results are threefold. First, this analysis reveals that female directors, whether on corporate boards or audit committees, are more likely to choose higher-quality audits in the form of high audit fees. Second, both Big 4 firms and Egyptian audit firms affiliated with foreign auditors are positively associated with audit fees and earn significant audit fee premiums. Third, a minor difference in audit fee premiums could be attributed to the existence of female directors.

Future research may expand the analysis performed in this study by investigating the characteristics related to female directors (e.g. education, experience and age) on audit fees.

This study suggests insights for regulatory bodies, corporate decision-makers, auditors and corporate governance researchers. For instance, this study reveals that the Big 4 are not homogenous and provide different audit quality levels along with significant audit fee premiums.

This study extends and contributes to the growing literature on female representation in corporate leadership. First, this study adds to the limited research in Egypt by examining the effect of female board representation on audit quality. Second, this study adds to the extant literature on the gender of financial experts by demonstrating that female financial expert is more likely to demand high-quality audits. Finally, the results have significant implications for policymakers. For instance, this study reveals that the Big 4 are not homogenous and provide different audit quality levels along with significant audit fee premiums.

The purpose of this paper is to analyze and compare four numerical methods to estimate the most suitable one which describes wind speed distribution of M’Sila, a province of northern Algeria.

The site chosen in this investigation is characterized by calm winds; in this case, the appropriate wind speed distribution is that of hybrid Weibull.

The four numerical methods used in the present paper are the maximum likelihood method, the graphical method, the moment method and the energy pattern factor method. The hybrid Weibull distributions using the abovementioned approaches are compared with the measured data via three statistical parameters, namely, the correlation coefficient, the root mean square error and the Chi-square error.

The obtained results showed that the moment method is the suitable one in describing month and annual wind speed hybrid Weibull parameters of this region.