The professional skepticism is one of the fundamental concepts necessary for practicing the audit profession, without which the auditor can not reach a reasonable assurance as to the correctness of the evidence and the information obtained by the auditing department. The auditor's possession of the characteristics of professional skepticism and his practice of professional skepticism during the audit process lead to an increase The quality of audit and thus raise the confidence of the financial community in the audit profession again after the exposure of several crises led to the loss of financial society confidence in the audit profession.

The aim of the study is to measure the impact of professional sk

Wind turbine (WT) is now a major renewable energy resource used in the modern world. One of the most significant technologies that use the wind speed (WS) to generate electric power is the horizontal-axis wind turbine. In order to enhance the output power over the rated WS, the blade pitch angle (BPA) is controlled and adjusted in WT. This paper proposes and compares three different controllers of BPA for a 500-kw WT. A PID controller (PIDC), a fuzzy logic controller (FLC) based on Mamdani and Sugeno fuzzy inference systems (FIS), and a hybrid fuzzy-PID controller (HFPIDC) have been applied and compared. Furthermore, Genetic Algorithm (GA) and Particle swarm optimization (PSO) have been applied and compared in order to identify the

<p>The current work investigated the combustion efficiency of biodiesel engines under diverse ratios of compression (15.5, 16.5, 17.5, and 18.5) and different biodiesel fuels produced from apricot oil, papaya oil, sunflower oil, and tomato seed oil. The combustion process of the biodiesel fuel inside the engine was simulated utilizing ANSYS Fluent v16 (CFD). On AV1 diesel engines (Kirloskar), numerical simulations were conducted at 1500 rpm. The outcomes of the simulation demonstrated that increasing the compression ratio (CR) led to increased peak temperature and pressures in the combustion chamber, as well as elevated levels of CO₂ and NO mass fractions and decreased CO emission values un

This research aims to study the effect of microwave furnace heat on the mechanical properties and fatigue life of aluminum alloy (AA 2024-T3). Four conditions were used inside microwave furnace (specimens subjected to heat as dry for 30 and 60min. and specimens subjected to heat as wet (water) for 30 and 60 min.), and compared all results with original alloy (AA 2024-T3). Tensile, fatigue, hardness and surface roughness tests were used in this investigation. It is found that hardness of dry conditions is higher than wet conditions and it increases with increasing of time duration inside microwave furnace for dry and wet conditions. Also, tensile strength has the same behavior of hardness, but it increases with decreasing

In the present study, MIG welding is carried out on low carbon steel type (AISI 1015) by using electrode ER308L of 1.5mm diameter with direct current straight polarity (DCSP). The joint geometry is of a single V-butt joint with one pass welding stroke for different plate thicknesses of 6, 8, and 10 mm. In welding experiments, AISI 1015 plates with dimensions of 200×100mm and edge angle of 60^o from both sides are utilized. In this work, three main parameters related to MIG welding process are investigated, which are welding current, welding speed, heat input and plate thickness, and to achieve that three groups of plates are employed each one consists of three plates. The results indicate that increasing the weld heat input (t

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In