What distinguishes the athlete in dealing with all stimuli is the ability to understand the cognitive rules through which he acts and directs behavior through thinking and regular planning methods in dealing with the environment in a realistic manner, and this comes through techniques and means based on modernity in obtaining information that makes the athlete arrange in His memory is the programs that are the most important crutch for relying on when he asks for them in applying and executing the skill assignment. One of the enhancers of awareness of variables is the ability of coaches to provide openness in modern ideas to find solutions, through which the player can sense and interpret events and produce outputs for quick and successful

A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle
swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA)

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

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

The effect of Wood Flour addition to the gamma alumina powder used in the preparation of gamma alumina (ɤ-Al₂O₃) catalyst carrier extrudates on the pore volume and BET surface area physical properties was investigated. Two parameters which are size of wood flour particles and its quantity were studied. The sizes of wood flour particles used are 150 µm, 212 µm and 500 µm and the weight percentage added to the gamma alumina powder during the preparation of the extrudates are (1%, 3%, 5% and 10%). The results showed that the addition of wood flour to the gamma alumina powder in order to get gamma alumina extrudates used as catalyst carrier is one of the successful methods to improve the pore volume

Background: The most widely used material for fabrication of denture base is poly methyl methacrylate, despite its popularity, the main problems associated with it as a denture base material are poor strength particularly under fatigue failure inside the patient mouth, impact failure outside the patient mouth, which are the main causes for fracture of denture, several studies was done to increase mechanical properties of denture base. The present study was conducted to evaluate and compare the effect of addition single walled carbon nanotubes in different concentrations to polymethyl methacrylate on some mechanical properties (surface hardness, surface roughness, impact strength and transverse strength). Materials and methods: Forty eight

In this work, (CdO)1-x (CoO)x thin films were prepared on glass slides by laser-induced plasma using Nd:YAG laser with (λ=1064 nm) and duration (9 ns) at different laser energies (200-500 mJ) with ratio (x=0.5), The influence of laser energy on structural and optical properties has been studied. XRD patterns show the films have a structure of polycrystalline wurtzite. As for AFM tests results for the topography of the surface of the film, where the results showed that the grain size and the average roughness increase with increasing laser energy. The optical properties of all films were also studied and the results showed that the absorption coefficient for within the wavelength range (280-1100 nm), The value of the optical power gap fo