Acontaminated ophthalmic solutions represent a potential cause of avoidable ocular infection. This study aimed to determine the magnitude and pattern of microbial contamination of eye drops in out patient at the department of ophthalmology, at Baghdad national hospital, Iraq. Fifty four vials from the out patient clinic were obtained for microbial examination after an average use of 2 weeks. The dropper tip and the residual eye drop were examined for contamination. The specimens were cultured, the number of colonies counted, the organisms identified. Eight (15%)  out of 54 analyzed vials were contaminated , most bacteria identi

The finding of novel pollutants in a multitude of surface water bodies worldwide has recently caused environmentalists to face challenges. Therefore, the development of low-cost, efficient technologies is required to provide a safe, pollution-free water environment. The attapulgite surface was used in this study because it has a good absorbent surface for removing bromocresol purple dye from its aqueous solutions. Because attapulgite has a high absorption capacity, it can be used to remove pollutants in both its normal and modified states. Factors affecting adsorption were studied, such as the weight of the adsorbent surface, the initial concentration of the adsorbent, contact time, and the effect of temperature. Clay was ground, cleaned mu

The finding of novel pollutants in a multitude of surface water bodies worldwide has recently caused environmentalists to face challenges. Therefore, the development of low-cost, efficient technologies is required to provide a safe, pollution-free water environment. The attapulgite surface was used in this study because it has a good absorbent surface for removing bromocresol purple dye from its aqueous solutions. Because attapulgite has a high absorption capacity, it can be used to remove pollutants in both its normal and modified states. Factors affecting adsorption were studied, such as the weight of the adsorbent surface, the initial concentration of the adsorbent, contact time, and the effect of temperature. Clay was ground, cleaned mu

The removal of boron from aqueous solution was carried out by electrocoagulation (EC) using magnesium electrodes as anode and stainless steel electrodes as cathode. Several operating parameters on the removal efficiency of boron were investigated, such as initial pH, current density, initial boron ion concentration, NaCl concentration, spacing between electrodes, electrode material, and presence of carbonate concentration. The optimum removal efficiency of 91. 5 % was achieved at a current density of 3 mA/cm² and  pH = 7 using (Mg/St. St. ) electrodes, within 45 min of operating time. The concentration of NaCl was o. 1 g/l with a 0.5cm spacing between the electrodes. First and second order rate equation were applied to study adsorp

Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a