The present work includes a design and characteristics study of a controlling the wavelength of high power diode laser by thermoelectric cooler [TEC] . The work includes the operation of the [TEC] to control the temperature of the diode laser between ( 0- +30) °C by changing the resistance of thermistor. We can control a limited temperature of a diode laser by changing the phase cooling between hot and cold faces of the diode, this process can be attempted by comparator type [LM –311] .The theoretical results give a model for controlling the temperature with, the suitable wavelength.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

This paper features the modeling and design of a pole placement and output Feedback control technique for the Active Vibration Control (AVC) of a smart flexible cantilever beam for a Single Input Single Output (SISO) case. Measurements and actuation actions done by using patches of piezoelectric layer, it is bonded to the master structure as sensor/actuator at a certain position of the cantilever beam.
The smart structure is modeled based on the concept of piezoelectric theory, Bernoulli -Euler beam theory, using Finite Element Method (FEM) and the state space techniques. The number of modes is reduced using the controllability and observability grammians retaining the first three
dominant vibratory modes, and for the reduced syste

Abstract The Object of the study aims to identify the effectiveness of using the 7E’s learning cycle to learn movement chains on uneven bars, for this purpose, we used the method SPSS. On a sample composed (20) students on collage of physical education at the university of Baghdad Chosen as two groups experimental and control group (10) student for each group, and for data collection, we used SPSS After collecting the results and having treated them statistically, we conclude the use 7E’s learning cycle has achieved remarkable positive progress, but it has diverged between to methods, On this basis, the study recommended the necessity of applying 7E’s learning cycle strategy in learning the movement chain on uneven bar

Inflammation of the tonsils could be described as acute tonsillitis, mainly due to infection. Recurrent tonsillitis could be defined as 3-7 episodes during the first 3 years of age. Vitamin D, which is a neuro-hormone with pleiotropic biological activities may modulate the immune response by alleviation, and stimulation of Th1 and Th2 cell proliferation, respectively, that influence the stimulation, synthesis, and secretion of both pro and anti-inflammatory cytokines. In this study we aimed to shed light on the levels of vitamin D in children with different episodes of tonsillitis in association with levels of interleukins (TNFα, IL-2, IL-4, IL-10). Blood samples were collected from 48 participants in 3 groups: control, acute tonsilliti

The study aimed to reveal the obstacles of administrative creativity among the leaders of secondary schools (the curriculum system) in the northern border region, which relate to the obstacles (organizational, motivational, and psychological). In addition, to identify the most important statistical differences between the responses of the members of the study sample, which attribute to the variables (educational qualification, year’s Administrative expertise). To achieve the goal of the study, the researcher used the descriptive analytical approach, and to verify the validity of the tool, it has presented to several referees and faculty members in the educational field. The validity of the internal consistency was also calculated for t

Scheduling considered being one of the most fundamental and essential bases of the project management. Several methods are used for project scheduling such as CPM, PERT and GERT. Since too many uncertainties are involved in methods for estimating the duration and cost of activities, these methods lack the capability of modeling practical projects. Although schedules can be developed for construction projects at early stage, there is always a possibility for unexpected material or technical shortages during construction stage. The objective of this research is to build a fuzzy mathematical model including time cost tradeoff and resource constraints analysis to be applied concurrently. The proposed model has been formulated using fuzzy the

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Research summarized in applying the model  of fuzzy goal programming for aggregate production planning , in General Company for hydraulic industries / plastic factory to get an optimal production plan  trying to cope with the impact that fluctuations in demand and  employs all available resources using two strategies where they are available   inventories  strategy and  the strategy of  change in the level of the workforce, these   strategies  costs are usually imprecise/fuzzy. The plant administration trying to minimize total production costs, minimize carrying costs and minimize changes in labour levels. depending on the gained data from th

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,