The research deals with the analysis of the city's commercial center using geographic information systems to solve the problem of congestion by evaluating the efficiency and adequacy of car parking lots according to local and Arab standards. Undoubtedly, the importance of car parking areas, as they are not within the desired efficiency within the city, will lead to congestion and traffic becomes very difficult. Thus, the transportation service loses its most important characteristic, which is the ease of movement. Therefore, there has become an urgent need to study and analyze it, as well as to verify the adequacy of the service, and the amount of deficit required to be provided to solve the transportation crisis and ease of traffic in the city center. For all that, the commercial center was chosen because it is the most crowded in the city of Al-Shatrah, to prepare a traffic study that includes collecting data on the locations and numbers of vehicles parked on both sides of the street, and analyzing the study area and its location besides ownership and prices. Therefore, the research methodology deals with descriptive analysis, which includes an analytical study of car parking lots within Arab and local standards, and comes up with analytical indicators, then the practical side deals with quantitative analysis represented by numbers of car parking lots, especially at rush hour, and then using (spatial analysis) based on the satellite image. As well as making a questionnaire and using statistical analysis represented by the normal distribution with binomial to analyze the results through the zero hypotheses and access to the actual need from the field survey and geographic information systems for car parks amounting to (252) car–parking spaces, which is greater than the available capacity .
A novel encapsulated deep eutectic solvent (DES) was introduced for biodiesel production via a two-step process. The DES was encapsulated in medical capsules and were used to reduce the free fatty acid (FFA) content of acidic crude palm oil (ACPO) to the minimum acceptable level (< 1%). The DES was synthesized from methyltriphenylphosphonium bromide (MTPB) and p-toluenesulfonic acid (PTSA). The effects pertaining to different operating conditions such as capsule dosage, reaction time, molar ratio, and reaction temperature were optimized. The FFA content of ACPO was reduced from existing 9.61% to less than 1% under optimum operating conditions. This indicated that encapsulated MTPB-DES performed high catalytic activity in FFA esterificatio
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A descriptive study, which was using an assessment approach, was conducted for the
determination of the impact of rheumatoid arthritis and osteoarthritis patient’s functional disability
upon their life style. The study was carried out at the Rheumatology and outpatients clinics of ALKarama
Teaching Hospital, Baghdad Teaching Hospital AL-Kindey Teaching Hospital and Specialized
surgeries Teaching Hospital for the period of October 15th 2003 through May 13th 2004 in Baghdad
City. A purposive (non-probability) sample of (245) arthritis patients which was comprised (111)
rheumatoid arthritis patients and (134) osteoarthritis patients, was selected out of the early stated
settings. The questionnaire was comprised of
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Deep Learning Techniques For Skull Stripping of Brain MR Images
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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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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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