Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realist

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various

Background: H.pylori colonized gastric mucosal
epithelium will virtually develop gastritis and had the
capacity to persist for decades. Pathogenesis is
dependent upon strain, virulence host genetic
susceptibility, and environmental cofactors. Leptin is
a member of the class 1 cytokine family so altered
leptin production during ifnect and inflammation that
leptin part of the cytokine cascade ,which
orchestrates the defense mechanism.
Objective: Examin the effect of H.pylori infection
on serum leptin level.
Methods: One hundred and thirty(130) Patients
attending the Endoscopic Unit at "Gastroenterology
and Hepatology Teaching Hospital/ Baghdad Medical
City"were included in this study with ages rang

The aim of this study was spot light to the possibility of confection between hepatitis C&G viruses in Iraqi patients. The groups was studied included two groups as follow: 1- first group: its (Infected group) the patients of this group have Anti-HCV abs in their serum by using Indirect ELISA technique. 2- second group: its (Control group) the persons of this group have no Anti-HCV abs in their serum by using indirect ELISA technique. The result of this study was observed there was two patients from the first group have Anti-HGVabs in their serum with infection percent 2.70%, also the same result was found in the second group wich mean there was two persons have Anti-HGVabs in their serum with infection percent 8.33% .So when we comparin

The physical behavior for the energy distribution function (EDF) of the reactant particles depending upon the gases (fuel) temperature are completely described by a physical model covering the global formulas controlling the EDF profile. Results about the energy distribution for the reactant system indicate a standard EDF, in which it’s arrive a steady state form shape and intern lead to fix the optimum selected temperature.

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

Pseudomonas aeruginosa is an opportunistic pathogen responsible for serious infections. At least three different exopolysaccharides, alginate, polysaccharide synthesis locus (Psl), and pellicle exopolysaccharide (Pel) make up the biofilm matrix in P. aeruginosa . The effect of temperature on the biofilm formation and gene expression was examined by microtiter plate and real-time quantitative polymerase chain reaction (qRT-PCR). To be able to determine the effect of temperature on biofilm formation and gene expression of P. aeruginosa, 303 clinical and environmental samples were collected. Pseudomonas aeruginosa was isolated from 61 (20.1%) and 48 (15.8%) of the clinical and e