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Computational insights into graphene-based materials for arsenic removal from wastewater: a hybrid quantum mechanical study

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Discover Water 2024年第1期4卷 1-12页

作者： Ayeni, Olusola Ibraheem Oyegoke, Toyese CAD-Engineering of Processes and Reactive Materials Group Chemical Engineering Department Ahmadu Bello University Zaria Nigeria Green Science Forum-Modeling and Simulation Pencil Team Ahmadu Bello University Zaria Nigeria

The discharge of industrial wastewater, particularly from chemical and mining industries, poses significant threats to the environment, public health, and safety due to high concentrations of pollutants leading to serious illnesses and the loss of aquatic life. It is therefore essential and urgent to devise measures for mitigating these threats. To advance the understanding of graphene membranes for Arsenic (As) removal from wastewater, this research investigates As adsorption and its relative selectivity on graphene-based materials using computational approaches. Our study employed hybrid quantum mechanical calculations for energy and geometry optimization to explore As adsorption on pristine graphene membrane surfaces in vacuum and aqueous environments. We assessed the effect of different adsorption sites on the surface which includes the top (T), bridge (B), and hollow (H) sites across the edge (E) and center (C) regions of the absorbent surface, to identify the optimal site/mode of adsorption. Our results demonstrate that the edge sites are the most effective for adsorption, exhibiting strong adsorption energies in both vacuum (− 1.98 eV) and aqueous environments (− 1.97 eV). These values are significantly higher than the adsorption energies for water on the surface, which range from − 0.25 to − 0.26 eV. Geometrical analyses confirmed the bridge edge sites as the most preferred adsorption configuration. Our findings not only advance upon existing computational approaches for designing efficient adsorbents but also provide deeper insights into the adsorption mechanisms on graphene-based materials. Unlike previous studies, which focused primarily on experimental or theoretical aspects in isolation, this work integrates computational and theoretical approaches to optimize adsorption processes at the molecular level. By investigating membrane properties for As removal, this research offers a novel pathway for developing advanced ad

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A Decentralized Importance-based Multi-UAV Path Planning Algorithm for Wildfire Monitoring

A Decentralized Importance-based Multi-UAV Path Planning Alg...

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IEEE International Conference on Systems, Man and Cybernetics

作者： S M Towhidul Islam Xiaolin Hu Department of Computer Science Systems Integrated Modeling and Simulation (SIMS) Lab Georgia State University Georgia USA Department of Computer Science Georgia State University Georgia USA

Wildfire monitoring is a highly important task in preventing the potential damages caused by this disastrous event. In wildfire monitoring, Unmanned Aerial Vehicles (UAVs) has gained significant attention from researchers and practitioners worldwide in recent years. While different UAV-based wildfire monitoring systems have been proposed in the past, a few of them consider decentralized computation and take into account the uneven spatiotemporal propagation nature of wildfires in their proposed solutions. This paper presents a decentralized and importance-based multi-UAV path planning algorithm for wildfire monitoring. We describe the algorithm design, implementation, and use simulation to evaluate its performance. Experiment results show the effectiveness of the proposed decentralized algorithm for importance-based wildfire monitoring.

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Computation of heat transfer in magnetised Blasius flow of nano-fluids with suspended carbon nanotubes through a moving flat plate

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International Journal of Ambient Energy 2022年第1期43卷 7657-7665页

作者： Ferdows, M. Shamshuddin, M.D. Salawu, S.O. Reza, Motahar Research group of fluid flow modeling and simulation. Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Telangana Warangal India Department of Physical Science Mathematics Programme College of Pure and Applied Sciences Landmark University Kwara State Omu-aran Nigeria Department of Mathematics GITAM School of Science GITAM Deemed to be University Hyderabad Campus Telangana Hyderabad India

This paper examines a two-dimensional steady, incompressible and laminar hydro-magnetic flow along with the transfer of heat above a plane plate that contains water and ethylene glycol-based nano-fluids with multi- and single-wall CNTs. The plate surface is subjected to variable wall temperatures. The transfer of heat has been contemplated in suspended CNTs above a plane plate. Navier–Stokes and the concept of the Prandtl boundary layer have been used to determine the governing equations of heat transfer and flow. Since the attained governing equations representing energy and flow are non-linear PDEs, they are altered into non-linear ODEs accompanied by analogous boundary conditions by applying several appropriate similarity transformations. Hence, the ‘bvp4c’ function is used to solve them numerically. Various graphs with physical interpretation demonstrate the consequence of determining terms on the characteristics of the flow. The plate velocity parameter and volume fraction have a positive effect. Temperatures are elevated markedly for most of the governing parameters. The rate of heat transfer and temperature exponent are enhanced by a magnetic field. SWCNTs give more substantial heat transfer than MWCNTs. © 2022 Informa UK Limited, trading as Taylor & Francis group.

关键词： Nanoparticles

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Knee Detection in Bayesian Multi-Objective Optimization Using Thompson Sampling

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IEEE Transactions on Evolutionary Computation 2024年

作者： Heidari, Arash Qing, Jixiang Gonzalez, Sebastian Rojas Branke, Juergen Dhaene, Tom Couckuyt, Ivo Computer Science at IDLab Ghent University Ghent Belgium Imperial College London United Kingdom Department of Computational Mathematics Data Science Institute Belgium Warwick Business School University of Warwick Coventry United Kingdom Computer Modeling and Simulation Research Group University of Antwerp Antwerp Belgium Cluster in IDLab Ghent University Belgium

Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the decision maker preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this paper, we propose using Thompson Sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single-and multi-knee detection in multi-objective optimization. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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RETRACTED ARTICLE: Distributed energy management for networked microgrids in a three-phase unbalanced distribution network

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Frontiers in Energy 2023年第3期17卷 446-446页

作者： Guodong LIU Thomas BOLLIS Maximiliano F.FERRARIL Aditya SUNDARARAJAN Yang CHEN Mohammed M.OLAMA Kevin TOMSOVIC Grid Components and Controls Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Water Resource Science and Engineering Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Modeling and Simulation Group Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The Editor-in-Chief has retracted this article because of a substantial overlap with a different article by the same authors that was under submission at the same time[1]*** authors did not explicitly state whether they agree to this ***,'one of the authors'name was spelled wrong in the original article,i.e.,Maximiliano *** should be Maximiliano *** online version of this article contains the full text of the retracted article as electronic supplementary material.

关键词： material. network Ferrari

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Segment Anything Model Aided Beam Prediction for the Millimeter Wave Communication 24

Segment Anything Model Aided Beam Prediction for the Millime...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Raha, Avi Deb Adhikary, Apurba Munir, Md. Shirajum Qiao, Yu Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States Department of Artificial Intelligence Kyung Hee University Yongin-si17104 Korea Republic of

ISBN: (纸本)9788995004395

Millimeter wave (mmWave) and terahertz (THz) communication protocols employ extensive antenna arrays to ensure reliable signal reception. However, aligning these narrow beams incurs a significant cost in beam training, which increases proportionally with the number of antennas. While beam prediction methods based on semantics from the RGB images demonstrate initial feasibility, they are still low on accuracy. Motivated by the fact that the recently introduced Segment Anything Model (SAM) can generate very accurate masks, which can be considered semantic information in this domain, this study proposes a beam prediction solution based on the masks of SAM. The SAM can extract more accurate semantic data from visual sources. Then instead of using the RGB images directly, the semantics images have been used to predict the beamforming vector using a lightweight LeNet5 model. The experimental results show that the SAM-based proposed method can perform significantly better than the two state-of-the-art deep learning models. The proposed solution method can achieve near 100% in top-5 beam prediction accuracy in real-world communication scenarios. Copyright 2023 KICS.

关键词： Semantics

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Start State Selection for Control Policy Learning from Optimal Trajectories

Start State Selection for Control Policy Learning from Optim...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Christoph Zelch Jan Peters Oskar von Stryk Department of Computer Science TU Darmstadt Simulation Systems Optimization and Robotics Group Darmstadt Germany Department of Computer Science Intelligent Autonomous Systems Group TU Darmstadt Darmstadt Germany

Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Exploring Large Language Models for Analyzing Changes in Web Archive Content: A Retrieval-Augmented Generation Approach

Exploring Large Language Models for Analyzing Changes in Web...

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IEEE International Conference on Big Data

作者： Jhon G. Botello Lesley Frew Jose J. Padilla Michele C. Weigle Department of Computer Science Old Dominion University Norfolk Virginia USA Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk Virginia USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Websites typically display only their most recent content. However, the dynamic nature of the web leads to frequent updates and deletions. Web archives preserve snapshots of earlier versions for those interested in tracking changes over time. Analyzing these changes often requires a manual process that relies on traditional methods focused on terms or phrase-level differences. This study explores the capability of Large Language Models (LLMs), specifically GPT-4o, through a Retrieval-Augmented Generation (RAG) approach for detecting changes in archived web pages. Using WARC-GPT, a RAG pipeline to interact with Web ARChive (WARC) files, we identify and analyze changes across a small set of U.S. federal environmental web pages that changed between 2016 and 2020. Our findings show that GPT-4o can effectively be used to detect inconsistencies in web archive content, including consideration of the change and the semantic context upon which the changes occurred. Our exploration represents an initial step toward using Artificial Intelligence (AI) for deeper and scalable web change analysis.

关键词： Large language models Computational modeling Retrieval augmented generation Semantics Pipelines Web pages Focusing Manuals Big Data Data models

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Probabilistic Multi-Objective Optimal Power Flow: A Grey Wolf Optimizer Approach Considering Load and Wind Turbine Uncertainties

Probabilistic Multi-Objective Optimal Power Flow: A Grey Wol...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Shiva Amini Innocent Kamwa Shabbo Nahvi Hêmin Golpîra Department of Electrical and Computer Science Engineering Laval University Quebec Canada Department of Electrical and Computer Engineering University of Kurdistan Sanandaj Iran Department of Electrical and Computer Engineering Power Systems Modeling & Simulation Lab University of Kurdistan Sanandaj Iran

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This article presents a novel approach to tackle the challenge of Probabilistic Multi-Objective Optimal Power Flow (PMOOPF) by utilizing the Grey Wolf Optimizer (GWO). The methodology incorporates uncertainties related to both load and wind turbine generation, modeling load uncertainty with a normal distribution and wind speed uncertainty with a Weibull distribution and employs Monte Carlo simulation (MCS) to establish the probability distribution function (PDF) for both the load and power generated by the hybrid sources. The PMOOPF problem aims to concurrently minimize thermal generation costs, real power losses, and maximize voltage stability. The proposed approach is tested on the IEEE 26-bus system, and the outcomes are compared with the Gravitational Search Algorithm (GSA). The contribution of this paper lies in effectively addressing uncertainties in load and wind turbine generation, incorporating diverse PMOOPF objectives, and employing the GWO optimization technique. The simulation results demonstrate the method's efficacy and efficiency in achieving economic and technical benefits.

关键词： Uncertainty Costs Wind speed Voltage Probabilistic logic Stability analysis Wind turbines

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