Objective(s): To evaluate students’ communication skills and their academic performance; to compare between the students relative to communication skills and their academic performance in the University of Baghdad and to identify the relationship between students’ communication skills, academic performance and their socio-demographic characteristics of age, gender, grade and socioeconomic status. Methodology: A descriptive design, using the evaluation approach, is carried through the present study to evaluate colleges’ students’ communication skills and their academic performance in the University of Baghdad for the period of January 7th 2019 to August 28th 2019. A non-probability, purposive sample, of (80) university students, is selected. Two questionnaires are utilized; the first iscontained of (15) items that measure the students’ communication skills and the second is comprised of (14) items that measure the students’ academic performance. Reliability and validity of the questionnaires are determined through pilot study. Data are collected through the use of study instruments and the structured interview technique as means for data collection. Data are analyzed through the application of the descriptive data analysis approach which includes frequency, percent and total scores and ranges and inferential statistical data analysis of approach of Multiple Linear Regression. Results: The study indicates that most of the students have fair level of Communication Skills (69%) and fair level of academic performance (67%). Students’ communication skills is influenced by their age and education stage and students’ academic performance is affected by their socioeconomic status. Recommendations: The study recommends that college students should be very well aware of the importance of communication skills and academic performance. Colleges’ curriculum should contain at least one course about communication skills and academic performance. Further research can be conducted on the same topic with wide-range sample size.
Solar collectors, in general, are utilized to convert the solar energy into heat energy, where it is employed to generate electricity. The non-concentrating solar collector with a circular shape was adopted in the present study. Ambient air is heated under a translucent roof where buoyant air is drawn from outside periphery towards the collector center (tower base). The present study is aimed to predict and visualize the thermal-hydrodynamic behavior for airflow under inclined roof of the solar air collector, SAC. Three-dimensional of the SAC model using the re-normalization group, RNG, k−ε turbulence viscus model is simulated. The simulation was carried out by using ANSYS-FLUENT 14.5. The simulation
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This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera
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High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
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Frustrated Total Internal Reflection FTIR phenomenon is manifested employing Newton‟s rings setup generated via a coherent light beam of a laser diode ( . All concentric bright and dark rings, except the central bright spot, were noticed to recede (disappear) when the incident angle exceeded the critical angle of 41o.
It was also shown that the current setup has proven its applicability for other tests and can give convenient results that conform with theory. Neither the concept nor the design is beyond what can be realized in an undergraduate laboratory. However, technical improvements in mounting the prism - lens may be advisable. As an extension of the experiments, the effect can be studied using hollow prism filled with liquids
Semiconductor laser is used in processing many issues related to the scientific, military, medical, industrial and agricultural fields due to its unique properties such as coherence and high strength where GaN-based components are the most efficient in this field. Current technological developments mention to the strong connection of GaN with sustainable electronic and optoelectronic devices which have high-efficiency. The threshold current density of Al0.1Ga0.9N/GaN triple quantum well laser structure was investigated to determine best values of the parameters affecting the threshold current density that are well width, average thickness of active region, cavity length, reflectivity of cavity mirrors and optical confinement factor. The opt
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Copper with different concentrations doped with zinc oxide nanoparticles were prepared from a mixture of zinc acetate and copper acetate with sodium hydroxide in aqueous solution. The structure of the prepared samples was done by X-ray diffraction, atomic force microscopy (AFM) and UV-VIS absorption spectrophotometer. Debye-Scherer formula was used to calculate the size of the prepared samples. The band gap of the nanoparticle ZnO was determined by using UV-VIS optical spectroscopy.
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