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Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty
Published 2008“…Experiments studying the behavior of agent-based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. …”
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The Impact of the Remote Flipped Learning Approach on Perceptions of Learning and Students’ Engagement during COVID-19: A Mixed Method Case Study Research of an Undergraduate Pharm...
Published 2021“…The study was focused on an undergraduate pharmacy course taught through the RFC approach in a private UAE university. The study utilized Reeve’s Learning Engagement Scale (LES) Questionnaire as the quantitative tool to analyze the student's overall engagement across Behavioral, Emotional, Cognitive, and Agentic constructs. …”
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Measuring the Acoustic Release of a Chemotherapeutic Agent from Folate-Targeted Polymeric Micelles
Published 2018“…Simulated and experimental results are used to verify the performance of the above-mentioned estimators. The proposed methods demonstrate the utility and high-accuracy of using estimation methods in modeling this drug delivery technique. …”
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Tumor lysis syndrome in chronic lymphocytic leukemia: conventional treatment versus novel agents A protocol for systematic review and meta-analysis
Published 2020“…The aims of this planned systematic review and meta-analysis are therefore</p><ul><li>(1) assess the TLS incidence reported in clinical trials for the novel or targeted agents comparing to conventional chemotherapeutic agents used to treat patients with CLL</li><li>(2) to identify the TLS prophylaxis strategies that are utilized in clinical trials of the novel or targeted agents for CLL if it was fully reported or under-reported and</li><li>(3) to compare the mortality among patients with TLS in conventional versus novel agents.…”
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Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods
Published 2024“…Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. …”
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Photoactivated carbon dots immobilized on cellulose for antibacterial activity and biofilm inhibition
Published 2025“…Carbon dots (CCM-PBA-NH₂) were synthesized and examined for their potential as photo-activated antimicrobial agents to address this issue. Various characterization methods were employed to investigate the structure and morphology of the CCM-PBA-NH₂ carbon dots (CDs). …”
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Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams
Published 2021“…<p dir="ltr">This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. …”
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Reinforcement Learning-Based Control of Signalized Intersections Having Platoons
Published 2022“…This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. …”
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The efficacy of colchicine in the management of coronavirus disease 2019
Published 2020“…Colchicine being an anti-inflammatory agent, might mitigate this dysregulated response. …”
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Deep Learning for the Accurate Prediction of Triggered Drug Delivery
Published 2024“…The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. …”
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Novel platinum(II)-based anticancer complexes and molecular hosts as their drug delivery vehicles
Published 2007“…We have developed a new purification method using Sep-Pak R C-18 reverse phase columns, which means these metal complexes can be made faster and cheaper compared to published methods. …”
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ECP: Error-Aware, Cost-Effective and Proactive Network Slicing Framework
Published 2024“…The framework proactively predicts different slice loads while considering prediction errors in optimizing future slices with varied KPIs in a cost-efficient manner. Specifically, our method utilizes historical load data per service and employs AI-based forecasts for service load prediction. …”
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The role of Reinforcement Learning in software testing
Published 2023“…There is a shortage of papers using advanced RL techniques in addition to multi-agent RL. Several challenges were presented in this study.…”
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A Decision-Making Framework for Benchmarking Virtual Commerce Applications for the Metaverse using Spherical Linear Diophantine Fuzzy Sets
Published 2024“…Subsequently, the proposed method is utilized to determine the significance of these attributes and rank the applications. …”
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Knowledge Fusion by Harnessing Support Vector Machines for Collaborative Uncertain Data Classification in Multiagent Systems
Published 2024“…Our investigation demonstrates that the performance of MAS-DDM surpasses that of DDM strategies that do not incorporate communicative processes, even when all MAS-DDM agents utilize the same methodology. We present empirical evidence demonstrating that the precision of the categorization job is significantly enhanced through the exchange of knowledge among agents.…”
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An enhanced consensus-based distributed secondary control for voltage regulation and proper current sharing in a DC islanded microgrid
Published 2023“…MATLAB-Simulink simulations are conducted on two medium-voltage DC (MVDC) microgrids to validate the efficiency of the proposed control method. The results confirmed that the proposed control method can effectively maintain voltage stability and enhance the precise distribution of current among agents by 8%.…”
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