The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).  In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

In the framework of this study, the phenomenon of transfer is probed pragma-linguistically and socio-linguistically concerning marriage situations among Iraqi EFL learners. The study also strives to look at the refusal strategies most commonly employed by Iraqi female English as a foreign Language (EFL) learners compared to their counterparts, American native speakers of English. The study involved 70 female participants who answered a Discourse Completion Task (DCT), which contained ten marriage proposals to be refused. Each situation entailed refusal of a person from a higher, an equal, and lower status. The researchers adapted Beebe, Takahashi, and Uliss Weltz’s (1990) taxonomy of refusal for analyzing the data comprehensively. The

This work deals with kinetics and chemical equilibrium studies of esterification reaction of ethanol with acetic acid. The esterification reaction was catalyzed by an acidic ion exchange resin (Amberlyst- 15) using a batch stirred tank reactor. The pseudo-homogenous and Eley-Rideal models were successfully fitted with experimental data. At first, Eley-Rideal model was examined for heterogeneous esterification of acetic acid and ethanol. The pseudo-homogenous model was investigated with a power-law model. The apparent reaction order was determined to be (0.88) for Ethanol and (0.92) for acetic acid with a correlation coefficient (R2) of 0.981 and 0.988, respectively. The reaction order was determined to be 4.1087x10-3 L0.8/(mol0.8.min) with

In this paper, a random transistor-transistor logic signal generator and a synchronization circuit are designed and implemented in lab-scale measurement device independent–quantum key distribution systems. The random operation of the weak coherent sources and the system’s synchronization signals were tested by a time to digital convertor.

Introduction. Epilepsy is a progressive, chronic neurological disorder characterized by recurrent seizures. Peppermint (Mentha piperita L.) (MP) is one of the most commonly ingested herbal teas or tisanes with a single component. Aim. We aimed to assess the potential antiepileptic and neuroprotective features of MP essential oil (MPO) in pilocarpine (P) and pentylenetetrazol (PTZ) models of epilepsy. Methods. The study used eight groups of mice to assess the anticonvulsant activity of MPO in both the P and PTZ acute models in mice. P (350 mg/kg, i.p.) was given 30 minutes after MPO (1.6, 3.2, and 6.4 ml/kg, i.p.). As a positive control group, diazepam (1 mg/kg, i.p) was used. PTZ (95 mg/kg, i.p.) was given 30 minutes after M

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbon