Tax revenues are considered one of the important sources of public revenues for the state as one of the tributaries that finance the public treasury to implement development plans. Real estate taxes are one of the sources of obtaining tax revenues in the state, as it follows that there are a number of factors that affect these revenues, which requires the development of necessary solutions and recommendations to address the resulting effects. Accordingly, this research sheds light on tax revenues in general and real estate tax revenues in particular to indicate the extent of their contribution to the total tax proceeds, as the tax proceeds of the real estate tax were analyzed into the total tax proceeds for the years (2017-2021). A number of conclusions were reached, the most important of which is the weak contribution of real estate tax to the total tax proceeds, as it ranged from (1.78-280) for the above years. The research recommended the tax proceeds.
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Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
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The purpose of this study was to investigate the difference in mandibular trauma caused by two mechanisms for the delivery of missile injuries: firearms and improvised explosive devices (IEDs). The data investigated included sex, age, mechanism of injury, and other clinical and radiographic manifestations. Seventy consecutive patients, predominantly male, with a mean age of 28.6 ± 14 years (range 2–60 years) were enrolled: 38 patients (54.3%) sustained mandibular fractures caused by bullet injuries and 32 patients (45.7%) had mandibular fractures caused by IED explosion injuries. The study revealed that the differences in most of the investigated variables were not statistically significant; the only significant differences were the inci
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