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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
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Examining the Asymmetric Impacts of Interest and Exchange Rate on Investment in Egypt for the Period 1976-2020: Applying NARDL Model
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Most of the studies conducted in the past decades focused on the effect of interest rates and exchange rates on domestic investment under the assumption that the independent variables have the same effect on the dependent variable, but there were limited studies that investigated the unequal effects of changes in interest rates and exchange rates, both positive and negative, on domestic investment.  This study used a nonlinear autoregressive distributed lag (NARDL) model to assess the unequal effects of the real interest rate and real exchange rate variables on domestic investment in Egypt for the period 1976 - 2020.  The results revealed that positive and negative shocks for both exchange rates have unequal effects on

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Optimal placement and sizing for integration of renewable energy sources in distribution networks: A review
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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
Concepts of statistical learning and classification in machine learning: An overview
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Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

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Publication Date
Thu Oct 01 2015
Journal Name
Engineering And Technology Journal
Genetic Based Optimization Models for Enhancing Multi- Document Text Summarization
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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Using Genetic Algorithm to Estimate the Parameters of the Gumbel Distribution Function by Simulation
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In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

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Publication Date
Mon Jun 01 2026
Journal Name
Chemical Physics Impact
High-density perovskite oxide ceramics with enhanced proton stopping power and gamma-ray shielding efficiency
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Publication Date
Fri Jun 29 2018
Journal Name
Journal Of Engineering
Preparation and Characterization of AgNp/PVDF Cmposite Ultrafiltration Membrane
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In this study, polymeric ultrafiltration (UF) membranes were prepared by phase inversion method to obtain both antibacterial and organic antifouling properties. The membranes were cast from a solution of polyvinylidene fluoride (PVDF) and formative silver (Ag) nanoparticles were successfully immobilized on a polymer. This was done using a solvent N, N-dimethylformamide (DMF) which is a solvent for the PVDF polymer meanwhile it is a reducing agent for silver ion. The effect of silver nanoparticles additives on the performance of polymeric ultrafiltration membrane was verified. Chemical composition and morphology of the surfaces of the membranes were characterized by Fourier transform infrared spectroscopy

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Publication Date
Tue Oct 10 2023
Journal Name
Journal Of Craniofacial Surgery
Cone Beam Computed Tomographic Evaluation of Schneiderian Membrane Thickness
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This study aimed to determine the measurements and classification of Schneider membrane thickness correlated to age and sex factors using cone beam computed tomography (CBCT). Methods: The study included CBCT images for 100 maxillary sinuses of 50 consecutive patients, and the thickness of the maxillary sinus membrane (Schneiderian membrane) was measured in coronal view from the lowest point in the floor of the maxillary sinus to the highest point. The thickness of the Schneiderian membrane was classified into 4 types. Results: The study result revealed that out of the total cases, 45% of sinus membranes were classified as type 2, while only 10% were classified as type 4. The most frequent type of membrane thickness diagnosed in the age gro

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Publication Date
Sat Sep 30 2023
Journal Name
Tikrit Journal Of Administrative And Economic Sciences
The impact of Outward bank transfers on exchange rates in Iraq
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The depreciation of the Iraqi dinar against the US dollar, reaching low levels and causing disruptions in the local markets, has had detrimental effects on individuals and companies, particularly those with limited income and the poor. The local currency approached around 1600 dinars per dollar, after the official exchange rate had stabilized at around 1450 dinars per US dollar. This depreciation in the value of the Iraqi dinar can be attributed to financial speculation among currency traders, which directly affected exchange rates and illicit dollar smuggling operations. Bank transfers are also important alongside financial transactions, especially in light of current economic developments in the 21st century. To prevent currency s

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Publication Date
Sat Apr 04 2026
Journal Name
Engineering, Technology & Applied Science Research
Experimental Optimization of PLC-Integrated Multi-Loop PID Control for a Three-Level Electro-Hydraulic Elevator Using PSO and Cheetah Optimizer
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This study presents the design, modeling, and experimental evaluation of a three-level electro-hydraulic elevator system controlled by a Delta DVP-20SX2 Programmable Logic Controller (PLC) equipped with an integrated Proportional–Integral–Derivative (PID) module. The PLC, programmed in Ladder Logic using ISPSoft 2.46, regulates cabin motion across all three levels. A detailed MATLAB/Simulink model was developed, incorporating three PID controllers: one for displacement regulation via a proportional directional control valve and two for dynamic pressure regulation using a proportional pressure relief valve. Conventional PID parameters were initially tuned using the trial-and-error method based on time-domain performance indices a

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