Baghdad Science Journal

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Copper iodide nanoparticles, CuI NPs, are one of the promising materials for use in many electronic applications. In this work, transparent CuI nanoparticles were prepared by the green force synthesis, which was done by red cabbage extract used not only as a reduction but also as a capping agent to reduce the toxicity in the preparation of CuI nanostructures. The structural properties were investigated by the X-ray diffraction analysis technique, XRD. The X-ray analysis shows the structure has a polycrystalline nature with a cubic phase and appears to have a preferential orientation direction along the (111) plane. Atomic force microscopy surface morphology, AFM, of CuI thin film was used to examine and measure the morphology, average diameter and roughness. The optical measurements were confirmed by UV-Visible, which showed the CuI nanoparticles have a directly allowed energy gap and a change in absorbance to a shorter wavelength (to Ultraviolet wavelength shifted region). The Fourier Transform infrared spectroscopy, FT-IR, verified the presence of C=O, N-O and C-H bonds, this study confirms the high reducing and capping capacity of CuI NPs via biomolecules found in the plant extract. Field emission scanning electron microscopy, FE-SEM, analysis displayed the morphology of the CuI NPs, the results showed images of the produced copper iodide nanoparticles appear to be sheets of triangular flakes. The distribution pattern of CuI NPs particle size and stability was studied using a zeta potential analyzer. Zeta sizer analysis revealed -5.08 mV zeta potential demonstrating poor stability because of the accumulation of the material.

The research seeks to examine a new form of equations as well as possible criteria for oscillation and non-oscillation for all solutions of first-order neutral differential equations with the piecewise constant, whether these oscillatory solutions are convergent or non-convergent, by finding sufficient conditions that guarantee the oscillation of all solutions of these equations. According to NDEPC, these equations contain three delays and three coefficients, which requires that part of the coefficients in this equation be piecewise continuous. In witch A function that assumes constant values over predetermined intervals is known as a piecewise constant argument (PCA). The biggest integer function [ω], which rounds down any real number ω to the closest integer, is a typical example. The oscillation criteria for this type of equation necessitate the acquisition of some essential and sufficient conditions to ensure the achievement of this objective. The biggest integer function represents the piecewise constant. All solutions to this type of problem must be oscillatory, and necessary and sufficient conditions have been devised to assure this. Based on these conditions, one can deduce that the technique used for producing results relied on classifying coefficients into two independent types.

Waste management is a key to sustainable development and environmental safety, particularly in the environment of the fast evolving smart cities. This study proposes a new decision-making model that will assist in the process of choosing effective sustainable methods in waste management for smart cities. It compares the four methods of waste management in relation to seven factors. Two Multi-Criteria Decision Making (MCDM) are used i) Analytical Hierarchy Process (AHP) and ii) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in a Neutrosophic Soft Set (NSS) environment. NSS have a more adaptive structure on dealing with the uncertainty and incapacity to be precise in the decision-making on waste management. In this methodology, AHP determines the weights of decision criteria, while TOPSIS ranks the effective waste management strategies for final selection. Ten professionals in the universities, industry, and government sector were used. This assessment was done by way of structured questionnaires of subjective opinions. As a result, the alternatives were ranked based on their closeness coefficient values as follows: A2 (0.632) > A1 (0.509) > A4 (0.352) > A3 (0.261). The given ranking shows that A2 is more effective than A1, A4, and A3.

A set S⊆ V is said to be a relatively prime detour dominating set of a graph G if it has at least two members is a detour set and a dominating set for any pair of vertices b and w such that (deg(b),deg(w)) = 1. The lowest cardinality of a relatively prime detour dominating set is indicated by γ_rpdn (G) and defines the lowest cardinality of a relatively prime detour dominating set of order γ_rpdn (G) said to as a γ_rpdn- set of G. If there is no relatively prime detour dominating set, the relatively prime detour domination number is zero. In this paper, the idea of switching graphs and providing some conclusions on the relatively prime detour domination number for path graphs, star graphs, bistar graphs, cycle graphs and various standard graphs are probed. This paper could be framed from the terms from the abundant epitome of domination graphs. The speculations are explicated with legitimate illustrations. The proof strategy is given lucidly, and the assessment of finding the detour set, domination set and degree of the vertices are stated plainly. Graph domination is wielded plenty in numerous areas and also we begin research on the concept of relatively prime detour domination number for switching graphs. The difficulty of relatively prime detour domination number is examined for us we starts a study on this idea of relatively prime detour domination number for switching graph. `

The paper deals with the problem of queueing-inventory system that has uncertain service conditions, and it discusses the contribution of server availability and customer behaviours like balking and feedback that are also added in to the system performance. In real life service and production situations, servers are not always available because of maintenance processes, capacity constraints, or other unforeseen disruptions. In order to simulate such characteristics, the system is modelled in a Markovian context where the interdependency between inventory levels, customer inflows, and server capacity is simulated. The time of customer arrivals is random and the time to serve is memoryless, which maintains the Markovian property of the model. Customer impatience is captured through balking and feedback, representing multiple service interactions within the system. The resulting stochastic process has a state space whose structure may be characterized by a quasi-birth-and-death formulation. With this structure the matrix-geometric method may be applied to the obtained long-run behaviour of the system. Some of the measures of performance of practical interest obtained out of the steady-state distribution include average inventory levels, system congestion, service completion rates. An inclusive cost framework is then developed by including ordering costs, holding costs, procurement costs and the service costs. System parameters sensitivity analysis and numerical experiments indicate the way they affect the operational performance and the total inventory cost. The findings demonstrate the significance of the balking and feedback effects to be managed successfully so that to enhance efficiency and customer satisfaction in systems with intermittently available servers.

Electroencephalography (EEG) data comes with a large size due to the data's high sampling rate. Therefore, compressing EEG data is very important for storing the EEG files efficiently with less space and bandwidth capacity requirement. This research develops an efficient system for EEG data compression. The recorded EEG data are preprocessed and scaled using certain Resolution Factor and truncated to integer numbers, then the scaled EEG samples are classified into small and large vectors using a proposed adaptive thresholding which is based on using three computed factors: Standard deviation, Average of samples (Mean), and the multiplier factor (α). Then, each sample is passed through one of three procedures, then saved into the output file using multi-shift coding algorithm The best values are chosen as the tradeoff between the compression ratio and the processing time. The results indicated that the value of α parameter is significantly affects the threshold calculation, where the best-proven value for α is 1.30; the system achieves a compression gain of 65% while managing a reasonable processing time of 4.007 Second. The resolution factor affected the Mean Squared Error (MSE) and Mean Absolute Error (MEA) significantly, but it had a slight effect on the Compression Ratio (Cr). The α parameter has a great effect on Cr and a slight on MSE. The findings show a consistent trend whereby, as the resolution factor gradually decreases from 2 to 0.1, a concurrent decrease is observed in the MAE, MSE, Bitrate, Cr, and the overall processing time.

By quick computer and communication technology improvement, Distributed Denial of Service DDoS attack harm is getting more important. DDoS attacks research is the important study domain; a number of methods exist which have been presented like the algorithm of evolutionary as well as artificial intelligence in literature to diagnose attacks of DDoS. Unfortunately, new popular models of DDoS diagnosis are deteriorating for validating DDoS attacks objective and prior identification. Because of DDoS attack modes diversity as well as various attack traffic amount, still there is not the technique of diagnosis with promising accuracy of diagnosis currently. By choosing the best subset of features, a feature selection FS approach helps to shorten computing times and increase computational complexity. For mitigating attacks of denial of service, this paper applies honey badger algorithm HBA with algorithm of machine learning known as HBIDS. Present strategy is given the making intrusion detection system IDS for meeting controlled area needs and could recognize between attack and normal traffics. Moreover, HBIDS chooses the most related from basic dataset of IDS which could aid recognizing normal low-speed DDoS attacks, then chosen features are conveyed to classifiers like decision tree, multilayer perceptron, naïve Bayes, and support vector machine for identifying attack kind. Generally accessible dataset as CIC-IDS 2017 and KDD Cup 99 are applied for our experimental research. From simulation outcomes, this is obvious that HBIDS with decision tree needs high diagnosis with the low false–positive rate 0.001 and accuracy 99.9

This research introduces a Machine Learning-Powered Intrusion Prevention System ML-IPS as a robust solution to address the challenges posed by evolving cyber threats. The ML-IPS combines timely threat detection with enhanced accuracy for real-time attack prevention, offering a resilient defense against a broad spectrum of cyber-attack. This study delves into the comprehensive evaluation of a machine learning-powered IPS that ingeniously harnesses the power of advanced algorithms to facilitate real-time threat detection and significantly enhance the overall accuracy of the system. The efficacy of intrusion prevention systems IPS that employ machine learning ML is greatly dependent on the choice of suitable ML algorithms and the evaluation of their precision and inference duration. This research embarks on an in-depth evaluation of ML models for IPS applications, focusing on a comprehensive comparison of their accuracy and inference time metrics. Additionally, the methodology employs a supervised learning approach using a labeled dataset CICIDS2017 containing both benign and malicious network traffic, providing a realistic and practical approach. The simulation results demonstrate that Decision tree and random forest algorithms can improve the prevention of attack in real-time by achieving 99.88% accuracy and about 10ms for time of detection. The findings demonstrate that the Intrusion Prevention System IPS is adept at promptly identifying and reacting to assaults, thereby furnishing a stronger and more durable safeguard against the ever-changing landscape of cyber hazards

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Cytokines are immune elements that have been found as marker either for tumor-promoting or inhibiting effects on breast tumor development, depending evidently on their exact quantities as well as the existence of additional modifying variables. The study aims to evaluate the relationship between breast cancer tissue and the expression level of IL-19 and IL-33. This study includes using breast cancer tissue (30 specimens) and normal breast tissue (20 specimens) to analyze the expression level of IL-19 and IL-30. Using immunohistochemical technique, the results indicated that lL-33 and also IL-19 were strongly elevated in patients with stage II and stage III of BC, compared to stage I. Also, IL-33 and IL-19 were highly expressed in patients with grade II and grade III BC, compared to grade I. In cancer tissues, expression of IL-33 and IL-19 was higher than in normal breast tissues. These data imply that IL-33 and IL-19 may play a significant role in the progress of BC and may be helpful biomarkers to identify the progress and metastasis of BC.

Background: chronic hepatitis C virus (HCV) infection is a major health problem. Females of childbearing age who are infected with hepatitis C are exposed to premature aging of the ovaries, which leads to an imbalance in the levels of sex hormones leptin hormone is associated with all stages of reproductive age. The aim of the present study is to observe levels the liver enzyme , Albumin , total bilirubin , estrogen , progesterone and leptin hormone. Also to evaluate the correlation between ( leptin hormone liver enzyme , Albumin , total bilirubin , estrogen and progesterone ).Methods: Serum liver enzyme , Albumin , total bilirubin , estrogen , progesterone and leptin hormone levels were determined in 33 patients with HCV and 32 healthy subjects. Results: When compared to the control group, the levels of liver enzyme , total bilirubin , estrogen , progesterone and leptin hormone were significantly higher in patients with hepatitis C virus (P≤0.05). On in contrary , levels of Albumin indicated a considerable decrease in patients having hepatitis C virus compared with control subjects (P≤0.05). The present study has reported a positive correlation between leptin hormone and (liver enzyme, total bilirubin , estrogen and progesterone ). Also it has revealed negative correlation between leptin hormone and Albumin