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A Light Weight Multi-Objective Task Offloading Optimization for Vehicular Fog Computing
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Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli-Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.

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Publication Date
Mon Oct 01 2012
Journal Name
Applied Energy
Analysis of thermal and electrical performance of a hybrid (PV/T) air based solar collector for Iraq
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The electrical and thermal performance of a typical single pass hybrid photovoltaic/thermal (PV/T) air collector is modeled, simulated and analyzed for two selected case studies in Iraq. An improved mathematical thermo-electrical model is derived in terms of design, operating and climatic parameters of the hybrid solar collector to evaluate its important characteristics: collector flow and heat removal factors, PV maximum power point and its temperature coefficient, and overall power and efficiency. Unlike previous PV/T thermal models, the present model is obtained with some additions and corrections in radiation and convection heat coefficients for the top loss and for the air duct with more applicable sky temperature correlation. The well

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Publication Date
Mon Mar 31 2025
Journal Name
The Iraqi Geological Journal
Evaluation of Machine Learning Techniques for Missing Well Log Data in Buzurgan Oil Field: A Case Study
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The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
Challenges of Using Literary Translation and Artificial Intelligence for Postgraduate Students: A Case Study of Baghdad University
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Publication Date
Mon Nov 17 2025
Journal Name
International Journal Of Environmental Analytical Chemistry
A critical review of precision analytical methods for detecting heavy metals in alcoholic and non-alcoholic beverages
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Publication Date
Sat Apr 12 2025
Journal Name
Mustansiriyah Journal Of Sports Science
A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players
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The purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research problems, trends, and prospects, almost fifty papers were found and analysed. Summarising AI applications in football for performance and injury p

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Publication Date
Sun Dec 01 2019
Journal Name
Computers And Electronics In Agriculture
Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq
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Publication Date
Tue Apr 19 2022
Journal Name
Chemistryselect
A Continuous Flow Injection/Indirect Photometric Method for the Detection of Fosetyl Aluminum in Commercial Pesticide Formulations
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Abstract<p>Because of their Physico‐chemical characteristics and its composition, the development of new specific analytical methodologies to determine some highly polar pesticides are required. The reported methods demand long analysis time, expensive instruments and prior extraction of pesticide for detection. The current work presents a new flow injection analysis method combined with indirect photometric detection for the determination of Fosetyl‐Aluminum (Fosetyl‐Al) in commercial formulations, with rapid and highly accurate determination involving only construction of manifold system combined with photometric detector without need some of the pre‐treatments to the sample before the analysis such a</p> ... Show More
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Publication Date
Sat Jun 01 2024
Journal Name
Materials And Structures
Exploring the efficacy of sawdust ash as a mineral filler substitute for the production of asphalt mixtures
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Many waste materials can be repurposed effectively within asphalt concrete to enhance the performance and sustainability of pavement. One of these waste materials is sawdust ash (SDA). This study explores the beneficial use of SDA as a substitute for limestone dust (LD) mineral filler in asphalt concrete. The replacement rate was 0%, 15%, 30%, 45%, and 60% by weight of total mineral filler. Scanning electron microscopy (SEM) was employed to assess the surface morphology of Sawdust (SD), SDA, and LD. In addition, a series of tests, including Marshall stability and flow, indirect tensile strength,moisture susceptibility, and repeated uniaxial loading tests, were conducted to examine the performance characteristics of asphalt mixtures of diffe

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Publication Date
Wed Oct 29 2025
Journal Name
Clean Energy Science And Technology
Edge-intelligent leak detection in water distribution systems using CatBoost: A sustainable solution for reducing infrastructure losses
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Water supply networks are marred by serious risks of imperceptible pipeline leakage, posing ‎sustainability ‎and ‎performance ‎threats.  This article highlights the use of vibratory signal features to get around the drawbacks of traditional methods in a highly detailed framework for leak detection based on CatBoost. demonstrated excellent diagnostic performance and carried out a thorough test performance evaluation on five leakage configurations  . The expected system achieved an accuracy of 98.1% (variance (well within x/3% of expected):, beating traditional competitors such as Random Forest (97.3%) and Support Vector Machine (93.8%). ‎For example, the area under the receiver-operating characteristic curve was 0.995, in

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Publication Date
Wed Jul 01 2026
Journal Name
Next Materials
Waste-derived chitosan-functionalized poultry litter biochar as a sustainable adsorbent for congo red removal from wastewater
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The increasing discharge of dye-containing wastewater has become a serious environmental problem, requiring efficient and sustainable treatment technologies. In this study, poultry litter–derived biochar (BC) was prepared via pyrolysis at 550 °C and subsequently modified with chitosan to produce a composite adsorbent (BC/CS) for the removal of Congo Red (CR) from aqueous solutions. The materials were characterized using SEM, BET, XRD, FTIR, and XPS analyses. BET results showed that chitosan modification slightly decreased the specific surface area from 8.28 to 8.18 m²/g and pore volume from 0.035 to 0.030 cm³ /g, while introducing abundant amine and hydroxyl functional groups on the surface. Maximum adsorption occurred at pH 3, w

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