Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

some ecological (physical and chemical varible) of water samples were studies monthly from December 2008 to May 2009 at two stations( St.1) Al - Chibayesh marsh and (St.2) Abu – Zirik marsh which are located in the south of Iraq . These variables included : Temperature, pH, EC, Dissolved oxygen , Total alkalinity, Nitrate, Sulphate, and phosphate, Si-SiO2 and Ca ,Mg, Cl, The marsh Considered as fresh water and alkaline. Abu-Zirik less than Al-Chibayesh.

In this research, the efficiency of low-cost unmodified wool fibers were used to remove zinc ion from industrial wastewater. Removal of zinc ion was achieved at 99.52% by using simple wool column. The experiment was carried out under varying conditions of (2h) contact time, metal ion concentration (50mg/l), wool fibers quantity to treated water (70g/l), pH(7) & acid concentration (0.05M). The aim of this method is to use a high sensitive, available & cheep natural material which applied successfully for industrial wastewater& synthetic water, where zinc ion concentration was reduced from (14.6mg/l) to (0.07mg/l) & consequently the hazardous effect of contamination was minimized.

In this work a study was made in centrifugal fan blower to investigate the effect of impeller blade design on sound pressure level (SPL). Shroud and unshroud impeller of nine blades are used. The sound generation from flow inside the test rig at different positions was displayed by using spectral analyzer. The experiments were carried out in anechoic chamber with small holes in its walls, under ambient condition about (25-27) C ° to avoid the effect of temperature on the sound pressure level. The results showed that (SPL) decreased with the increase of distance from the source about (3-4)dB when distance varied about (0.8-1.06)m, and the (SPL) decreased with the decrease of velocity about (8-12)dB when velocity varied between (13000-260

Grass trimming operation is widely done in Malaysia for the purpose of maintaining highways. Large number of operators engaged in this work encounters high level of noise generated by back pack type grass trimmer used for this purpose. High level of noise exposure gives different kinds of ill effect on human operators. Exact nature of deteriorated work performance is not known. For predicting the work efficiency deterioration, fuzzy tool has been used in present research. It has been established that a fuzzy computing system will help in identification and analysis of fuzzy models fuzzy system offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. The paper presents

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T