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This paper aims to assess the effect of different leadership types, the client's identity and auditors' self-confidence on auditors' impartiality.

This paper is a descriptive-survey type, and the collected data are based on a predesigned questionnaire distributed in January 2020. The PLS software is used for data analysis, and the statistical population of this paper includes employed auditors in enlisted audit firms on the Official Association of Auditors. When the parameters were insignificant, the obtained probability from the model fitting was used for hypothesis testing, and the appropriateness of the model was assessed via the structural equations.

The results show a significant relationship between charismatic, transformational, participatory, delegating and bureaucratic leadership and auditors' impartiality. There is also an association between the client's identity and the auditor's impartiality. The client's identity mediates the relationship between transformational leadership and the auditor's impartiality. Moreover, there is a significant relationship between self-confidence and auditor's impartiality.

This paper enjoys an innovative method in the field of behavioural auditing. The effect of transformational leadership on auditor's impartiality with the mediatory role of the client's identity shows the in-depth client–auditor relationship has been taken for granted and not examined previously, so the results of this paper can lend a helping hand to audit firms to enhance the organisational performance.

This study aims to design and implement a multimaterial system for printing multifunctional specimens suitable for various sectors, with a particular focus on biomedical applications such as addressing mandibular bone loss.

To enhance both the mechanical and biological properties of scaffolds, an automatic multimaterial setup using vat photopolymerization was developed. This setup features a linear system with two resin vats and one ultrasonic cleaning tank, facilitating the integration of diverse materials and structures to optimize scaffold composition. Such versatility allows for the simultaneous achievement of various characteristics in scaffold design.

The printed multimaterial scaffolds, featuring 20 Wt.% hydroxylapatite (HA) on the interior and poly-L-lactic acid (PLLA) with 1 Wt.% graphene oxide (GO) on the exterior, exhibited favorable mechanical and biological properties at the optimum postcuring and heat-treatment time. Using an edited triply periodic minimal surface (TPMS) lattice structure further enhanced these properties. Various multimaterial specimens were successfully printed and evaluated, showcasing the capability of the setup to ensure functionality, cleanliness and adequate interface bonding. Additionally, a novel Gyroid TPMS scaffold with a nominal porosity of 50% was developed and experimentally validated.

This study demonstrates the successful fabrication of multimaterial components with minimal contaminations and suitable mechanical and biological properties. By combining PLLA-HA and PLLA-GO, this innovative technique holds significant promise for enhancing the effectiveness of regenerative procedures, particularly in the realm of dentistry.

This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions.

The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness.

The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels.

By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions.

The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

In vernacular buildings, many climatic and passive solutions have been used to create indoor thermal comfort. Seasonal occupant movement is an example of a traditional response to increasing thermal comfort. This article investigates the influence of these user behaviours on thermal comfort in courtyard houses.

Parametric models of three different scenarios of courtyard houses are simulated. The courtyard houses are located in Shiraz, Iran, and share the same orientation and construction materials. To enhance the accuracy of the study, the indoor adaptive thermal comfort (ATC) analysis is performed with three different window-to-wall ratios (WWR) of 25, 50 and 75%. The ACT analysis is performed on an hourly basis for summer and winter scenarios.

The results demonstrate that the indoor ATC is 8.3% higher in winter than in the summer in the seasonal zones. During the summer, the amount of ATC is relatively sustained in all zones. Unlike common beliefs, seasonal movement can enhance the ATC, especially during winter, specifically in the northern part of the courtyard. In northern zones, the seasonal movement of occupants improves the indoor ATC from 10.1 to 23.7%, and in southern zones, the improvement is from 2.2 to 4.8%.

This research presents a new numerical investigation into occupants' seasonal movements in courtyard houses during summer and winter. It provides a precise pattern to show how much this seasonal movement can affect the habitant's ATC.

Investigating the import of pharmaceutical products, as one of the essential goods of the country to improve health and medical conditions, is important in accelerating the economic growth and enhancing the welfare of the community. This paper aims to estimate the income and price elasticity of pharmaceutical import demand in Iran.

In this research, the function of Iran's pharmaceutical import demand was estimated and calculated using the co-integration method and error correction techniques with the seasonal data of 2005–2016 to identify factors related to import, as well as relevant price, and income elasticity.

The research results showed that the price elasticity of Iran's long-term pharmaceutical import demand was 0.04; that is, the rate of pharmaceutical import demanded by Iran was not sensitive to change in relative price. Additionally, the long-term income elasticity of Iran's pharmaceutical demand was 0.69, meaning that an increase in Iran's income did not have a highly positive impact on the volume of Iran's pharmaceutical import.

Estimating the function of the demand for pharmaceutical import may pave the way to adopt appropriate economic policies. Furthermore, estimating this function and calculating income and price elasticity is a step toward minimizing government expenditures and can be a great contribution in designing trade policies.

Energy has a strategic role in the social and economic development of countries. Affordability, accessibility and availability of energy sources are the priorities of the governments in energy supply. Therefore, understanding the robustness of energy supply is an important subject of energy researchers and policymakers. This paper aims to analyze the robustness of the electricity system at the national level.

First, the strengths, weaknesses, opportunities and threats analysis is implemented for a selected case study. Then, the expert panel weighed the parameters’ effect on sustainable power generation, the survey is quantified using fuzzy logic. Finally, cross functional analysis is applied to evaluate the influence/dependence of the parameters.

The results show three determinant parameters which have the most influence on the system: fluctuations in oil prices, governmental acts and sanctions against the country. The most dependent parameters, as objectives variables, are the share of renewables and distributed generation (DG), system reliability, power generation diversity and transmission efficiency.

Using future studies methods in the energy level at the nation level has been done for the first time in the current work.

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.