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Upgrading Sustainability in Clean Energy: Optimization for Proton Exchange Membrane Fuel Cells Using Heterogeneous Comprehensive Learning Bald Eagle Search Algorithm
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Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number of agents is divided into two subagents. Each subagent is assigned to focus solely on either exploration or exploitation. The comprehensive learning strategy generates exemplars for both subgroups. In the exploration sub-agent, exemplars are generated using the personal best experiences of agents within that same exploration space. The exploitation subagent generates the exemplars using the personal best experiences of all agents. This separation preserves exploration diversity even if exploitation converges prematurely. The algorithm is applied to optimize parameters of the 250 W and 500 W PEMFC models under varying conditions. Simulation results demonstrate the outperformance of the HCLBES algorithm in terms of convergence speed, estimation accuracy, and robustness compared to recent optimization algorithms. The effectiveness of HCLBES was also verified through statistical metrics and different commercial PEMFC models, including BCS 500 W stacks, Horizon 500, and NedStack PS6. Experimental validation confirms that the proposed algorithm effectively captures the nonlinear behaviours of PEMFCs under dynamic operating conditions. This research aligns with the Sustainable Development Goals (SDGs) by promoting clean and affordable energy (SDG 7) through the enhanced efficiency and reliability of PEMFCs, thereby supporting sustainable industrialization and innovation (SDG 9).

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Publication Date
Sat Jan 01 2022
Journal Name
مجلة كلية الكوت الجامعة للعلوم الإنسانية
Student - Teacher Environmental Sustainability Values College of Education for Pure Sciences
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Research objective to identify the degree of environmental sustainability values of the student-teacher in the College of Education for Pure Sciences. In this research the methodology of relational descriptive research was adopted, and the research sample consisted of (116) students from the College of Education for Pure Sciences / Ibn Al-Haytham / University of Baghdad From the Department of Chemistry (fourth stage), that is, 54% of the research community were randomly selected. The research tool was prepared, represented by a measure of environmental sustainability values of (20) items, the results of the research showed: The poor degree of environmental sustainability values. The Statistical Portfolio of Social Sciences (SPSS) was adopte

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Publication Date
Wed Nov 04 2020
Journal Name
Advances In Mobility-as-a-service Systems
Community Participation Towards Sustainability Enhancement of Transportation Sector for Baghdad City
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Publication Date
Fri Jan 16 2026
Journal Name
F1000research
Update Quasi-Newton Algorithm for Training ANN
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The proposed design of neural network in this article is based on new accurate approach for training by unconstrained optimization, especially update quasi-Newton methods are perhaps the most popular general-purpose algorithms. A limited memory BFGS algorithm is presented for solving large-scale symmetric nonlinear equations, where a line search technique without derivative information is used. On each iteration, the updated approximations of Hessian matrix satisfy the quasi-Newton form, which traditionally served as the basis for quasi-Newton methods. On the basis of the quadratic model used in this article, we add a new update of quasi-Newton form. One innovative features of this form's is its ability to estimate the energy functio

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Publication Date
Sat Feb 11 2023
Journal Name
Applied Sciences
A Preliminary Study and Implementing Algorithm Using Finite State Automaton for Remote Identification of Drones
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Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the ene

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Publication Date
Thu Jun 01 2023
Journal Name
Ifip Advances In Information And Communication Technology
Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning
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Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes In Networks And Systems
Using Machine Learning to Control Congestion in SDN: A Review
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Publication Date
Tue Jan 30 2024
Journal Name
International Journal Of Pharmacy Practice
A Comprehensive Review of Drivers influencing Flu Vaccine Acceptance in the Middle East: Using Health Belief Model
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Rationale, aims and objectives: A review of studies published over the last six years gives update about this hot topic. In the middle of COVID-19 pandemic, this study findings can help understand how population may perceive vaccinations. The objectives of this study were to review the literature covering the perceptions about influenza vaccines and to determine factors influencing the acceptance of vaccination using Health Belief Model (HBM). Methods: A comprehensive literature search was performed utilizing PubMed and Google Scholar databases. Three keywords were used: Influenza vaccine, perceptions, and Middle East. Empirical studies that dealt with people/ HCW perceptions of influenza vaccine in the Middle East and writt

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Publication Date
Thu Dec 08 2022
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
Implementation of RWP and Gauss Markov Mobility Model for Multi-UAV Networks in Search and Rescue Environment
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Future generations of wireless networks are expected to heavily rely on unmanned aerial vehicles (UAVs). UAV networks have extraordinary features like high mobility, frequent topology change, tolerance to link failure, and extending the coverage area by adding external UAVs. UAV network provides several advantages for civilian, commercial, search and rescue applications. A realistic mobility model must be used to assess the dependability and effectiveness of UAV protocols and algorithms.  In this research paper, the performance of the Gauss Markov (GM) and Random Waypoint (RWP) mobility models in multi-UAV networks for a search and rescue scenario is analyzed and evaluated. Additionally, the two mobility models GM and RWP are descr

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Publication Date
Fri Jun 01 2018
Journal Name
Journal Of Engineering
Direct Contact Membrane Distillation for Separation of Hcl From A Mixture of Acids
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HCl is separated from HCl –H2SO4  solution by membrane distillation process(MD). The flat –sheet membranes made from polyvinylidene fluoride (PVDF) and polypropylene (pp.). Plate and frame these types of membrane where used in the process. The feed is a mixture of HCl and H2SO4 acids compositions depended on metals treated object.HCl concentration increased in the permeate during the process but sulfuric acid increased gradually in the feed .During the concentration of solution acids concentrations in the feed at the beginning were 50 g/dm3 of sulfuric acid and 50 g/dm3 of hydrochloric acid at 333K feed temperature the permeate flux was 71 dm

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Publication Date
Sat Jun 01 2019
Journal Name
2019 International Symposium On Networks, Computers And Communications (isncc)
An Interference Mitigation Scheme for Millimetre Wave Heterogeneous Cloud Radio Access Network with Dynamic RRH Clustering
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