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Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks
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    Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed to detect complex biological communities with high quality. Secondly, the variability in the capability of PSO to extract community structure in biological networks is studied when different types of crossover operators are used. Finally, to reduce the computational time needed to solve this problem, especially when detecting complex communities in large-scale biological networks, we have implemented parallel computing to execute the algorithm. The performance of the proposed algorithm was tested and evaluated on two real biological networks. The experimental results showed the effective performance of the proposed algorithm when using single-point crossover operator, and its superiority over other counterpart algorithms. Moreover, the use of parallel computing in the proposed algorithm representation has greatly reduced the computational time required for its execution.

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Publication Date
Wed Jan 01 2025
Journal Name
E3s Web Of Conferences
Assessment of radiological biological risks in samples of medicinal plants in Iraq
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The aim of this research sought to assess the background radiati on levels of medicinal plant samples in Iraq.The D, AED, and ELCRThe D values are 0.164 ± 0.006 µSv/h.The radiation dose outputs from the afore mentioned research were juxtaposed with the safety threshold of 0.247 µSv/h set by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) in 2008 and the National Council on Radiation Pr otection and Measurements (NCRP) in 2009.The radiation exposure for all samples in this investigation is within the permissible limits.Additionally, t he AED values obtained from microsieverts per hour are (1.31 ±0.0326 mS v/y), indicating variability, with a maximum permissible limit of 2.4 mSv/y ear, as per t

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Publication Date
Thu Jun 23 2022
Journal Name
American Scientific Research Journal For Engineering, Technology, And Sciences
A Review of TCP Congestion Control Using Artificial Intelligence in 4G and 5G Networks
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In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Engineering And Sustainable Development
Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
A Comparison between Ericson's Formulae Results and Experimental Data Using New Formulae of Single Particle Level Density
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The partial level density PLD of pre-equilibrium reactions that are described by Ericson’s formula has been studied using different formulae of single particle level density . The parameter  was used from the equidistant spacing model (ESM) model and the non- equidistant spacing model (non-ESM) and another formula of  are derived from the relation between  and level density parameter . The formulae used to derive  are the Roher formula, Egidy formula, Yukawa formula, and Thomas –Fermi formula. The partial level density results that depend on  from the Thomas-Fermi formula show a good agreement with the experimental data.

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Publication Date
Sun Sep 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Removal of Emulsified Paraffine from Water: Effect of Bubble Size and Particle Size on Kinetic of Flotation
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This paper  studied  kinetics  of flotation   of  emulsified  paraffine  in  water  in  bubble  column  with  sodium .dodecylsulphate as a collector agent. The effects of oil drops and air bubble diameters on the flotation rate constant were studied. The removal rate for each oil drop size was first order with respect to oil drop concentration. An experimental procedure permitting determination of the first order rate constants for  removal due to bubble/drop interaction was developed, decreasing bubble diameter by adding NaCl and increasing oil drop diameter increased the rate constants. A comparison between the experimental and theoretical rate constants showed

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Publication Date
Sun Oct 01 2023
Journal Name
Baghdad Science Journal
Biological peculiarities of Leucozonella retteri
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The aim of the research is to study the biology, life cycles, distribution and structure of the reproductive organ of Leucozonella retteri in natural conditions. Zoological and malacological methods are used in the work. The collection of the material was carried out according to A.A. Shileiko method. According to the results of the conducted studies, the differences between Leucozonella retteri and other species in the structure of the reproductive organ were manifested in the following. The lower part of the sperm is straight, the ovary is slightly curved. The paw pad is 8, located in 4x2 positions. The stylophore is large spherical. The vagina is cylindrical, its length is 5-6 times greater than the width. The penis is large and conve

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Publication Date
Tue Jul 01 2025
Journal Name
Applied Thermal Engineering
Efficient thermal management of PEM fuel cells using cascaded multi-layer phase change materials: Analysis of series and parallel configurations
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Despite their potential as a sustainable energy technology, the operation of proton exchange membrane fuel cells (PEMFCs) in sub-freezing conditions remains a critical challenge due to the risk of ice formation and performance degradation. This study introduces a new passive thermal management technique using strategically arranged multi-layer phase change materials (PCMs) to address this challenge. A numerical model was developed to evaluate the thermal behavior across various PCM configurations, incorporating one, two, and three layers arranged both in parallel and series with distinct melting points ranging from 55 to 65 ◦C. The results show that multi-layer PCM configurations provide significant improvements over the single-layer base

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
DYNAMIC MODELING FOR DISCRETE SURVIVAL DATA BY USING ARTIFICIAL NEURAL NETWORKS AND ITERATIVELY WEIGHTED KALMAN FILTER SMOOTHING WITH COMPARISON
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Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication
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Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things,

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Publication Date
Fri Oct 04 2024
Journal Name
Analytical And Bioanalytical Chemistry Research
Optimization and Validation of a GC-FID/QuEChERS Method for Quantitative Determination of Spiromesifen Residues in Tomato Fruits, Leaves and Soil Matrices
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Pesticides serve a crucial function in contemporary farming practices, safeguarding agricultural crops against pest infestations and boosting production outputs. However, indiscriminate use has caused environmental and human health damage. This study aimed to develop and validate a gas chromatography-flame ionization detection (GC-FID) methodology for the direct and routine analysis of spiromesifen residues in soil, leaves, and tomato fruits. The proposed method prioritizes simplicity by avoiding derivatization steps, offering advantages over existing approaches that utilize lengthy multi-step extraction or derivatization prior to GC analysis. A key novelty of this work is the development of a QuEChERS extraction coupled directly to GC-FID

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