The electrocoagulation process became one of the most important technologies used for water treatment processes in the last few years. It’s the preferred method to remove suspended solids and heavy metals from water for treating drinking water and wastewater from textile, diary, and electroplating factories. This research aims to study the effect of using the electrocoagulation process with aluminum electrodes on the removal efficiency of suspended solids and turbidity presented in raw water and optimizing by the response surface methodology (RSM). The most important variables studied in this research included electrode spacing, the applied voltage, and the operating time of the electrocoagulation process. The samples

This paper proposes a collaborative system called Recycle Rewarding System (RRS), and focuses on the aspect of using information communication technology (ICT) as a tool to promote greening. The idea behind RRS is to encourage recycling collectors by paying them for earning points. In doing so, both the industries and individuals reap the economical benefits of such system. Finally, and more importantly, the system intends to achieve a green environment for the Earth. This paper discusses the design and implementation of the RRS, involves: the architectural design, selection of components, and implementation issues. Five modules are used to construct the system, namely: database, data entry, points collecting and recording, points reward

This paper was aimed to study the efficiency of forward osmosis (FO) process as a new application for the treatment of wastewater from textile effluent and the factors affecting the performance of forward osmosis process.
The draw solutions used were magnesium chloride (MgCl2), and aluminum sulphate (Al2 ( SO4)3 .18 H2O), and the feed solutions used were reactive red, and disperse blue dyes.
Experimental work were includes operating the forward osmosis process using thin film composite (TFC) membrane as flat sheet for different draw solutions and feed solutions. The operating parameters studied were : draw solutions concentration (10 – 90 g/l), feed solutions concentration (5 – 30 mg/l), draw solutions flow rate (10 – 50 l/hr

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable

Lacing reinforcement plays a critical role in the design and performance of reinforced concrete (RC) slabs by distributing the applied loads more evenly across the slab, ensuring that no specific area of the slab is overloaded. In this study, nine slabs, divided into three groups according to the investigated parameters, were meticulously designed and evaluated to study the interplay between the lacing reinforcement and other key parameters. Each slab was crafted for simple support and was subjected to both static and repeated two-point load tests. The lacing reinforcement had an angle of 45° with various tension and lacing steel. The repeated-tested specimens with lacing reinforcement experienced smaller ductility than those of s