The Electro-Fenton oxidation process is one of the essential advanced electrochemical oxidation processes used to treat Phenol and its derivatives in wastewater. The Electro-Fenton oxidation process was carried out at an ambient temperature at different current density (2, 4, 6, 8 mA/cm²) for up to 6 h. Sodium Sulfate at a concentration of 0.05M was used as a supporting electrolyte, and 0.4 mM of Ferrous ion concentration (Fe²⁺) was used as a catalyst. The electrolyte cell consists of graphite modified by an electrodepositing layer of PbO₂ on its surface as anode and carbon fiber modified with Graphene as a cathode. The results indicated that Phenol concentration decreases with an increase in current dens

To determine the relationship between chronic hepatitis B virus and autoimmune celiac disease, seventy five patients with chronic hepatitis B virus of ages (8-70) years have been investigated and compared to 50 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA and IgG by ELISA test and anti-endomysial antibodies IgA and IgG by IIFT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P< 0.01) .The prevalence of antiTtG IgA and IgG was 14.67% and 12.0% respectively.There was a highly significant difference (P<0.01) when compared between studied groups .While the prevalence of anti-en

This study concerns the isolation of oil degraded bacterial samples from oil polluted soil in Al-Dora refinery/ Baghdad – Iraq. Soil samples (15) were on mineral salt agar medium (MSM) used to screen the oil degrading bacteria by forming clear zones around the colonies. To confirm the degradation of oil by these bacteria, the isolates were inoculated in mineral salt broth, 15 isolates of Pseudomonas spp. was detected from which two isolates identified as P. aeruginosa by morphological, physical and biochemical characteristics that confirmed by using Vitick identification system. Growth was estimated in terms of whole cell by measuring optical density at 620 nm and free extract protein was estimated by protein measurement with Folin phe

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA wit