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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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Comparative Analysis of GaN HEMT on β-Ga2O3 and conventional substrates for RF Applications 12

Comparative Analysis of GaN HEMT on β-Ga2O3 and conventiona...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Shakya, Arzoo Pal, Praveen Kabra, Sneha Modeling and Simulation Research Laboratory Shaheed Rajguru College of Applied Sciences for Women University of Delhi New Delhi India Nanolab Department of Electrical Engineering Indian Institute of Technology Uttar Pradesh Kanpur India Department of Instrumentation Shaheed Rajguru College of applied Sciences for Women University of Delhi New Delhi India

ISBN: (纸本)9798350387315

This work presents performance investigation of the impact of different substrates on GaN-HEMTs, with an emphasis on important parameters including drain current, transconductance, and cut-off frequency. GaN-HEMT device with gate length of 200 nm and gate width of 100 μm was used to study the device performance using different substrates such as 6H-SiC, Al2O3, GaN, and β-Ga2O3. It has been observed that β-Ga2O3, shows excellent device performance with a maximum drain current of 1.21 A/mm, a peak transconductance of 262.46 mS/mm, and a cut-off frequency of 50.59 GHz. These findings demonstrate that β-Ga2O3 can be used as a suitable substrate for improving GaN-HEMT performance, especially for high frequency high power application. © 2024 IEEE.

关键词： III-V semiconductors

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Biomechanical modeling of cell chirality and symmetry breaking of biological systems

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Mechanobiology in Medicine 2024年第1期2卷 22-30页

作者： Tasnif Rahman Frank D.Peters Leo Q.Wan Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Center for Biotechnology&Interdisciplinary Studies Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Biological Sciences Rensselaer Polytechnic InstituteTroyNY 12180USA d Center for Modeling Simulationand Imaging in MedicineRensselaer Polytechnic InstituteTroyNY 12180USA

Accumulating evidence strongly suggests that cell chirality plays a pivotal role in driving left-right(LR)symmetry breaking,a widespread phenomenon in living *** embryos and excised organs have historically been employed to investigate LR symmetry breaking and have yielded exciting *** recent years,in vitro engineered platforms have emerged as powerful tools to reveal cellular chiral biases and led to uncovering molecular and biophysical insights into chiral morphogenesis,including the significant role of the actin *** a link between observed in vivo tissue chiral morphogenesis and the determined chiral bias of cells in vitro has become increasingly *** this regard,computational mathematical models hold immense value as they can explain and predict tissue morphogenic behavior based on the chiral biases of individual ***,we present the formulations and discoveries achieved using various computational models spanning different biological scales,from the molecular and cellular levels to tissue and organ ***,we offer insights into future directions and the role of such models in advancing the study of asymmetric cellular mechanobiology.

关键词： symmetry breaking chiral

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Federated Learning With Client Clustering Selection and Quality-Aware Model Aggregation(2024)

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IEEE Internet of Things Journal 2025年

作者： Peng, Y. Wang, C. Shi, H. Ma, R. Guan, H. Yang, Hanbo Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

The Industrial Internet of Things (IIoT) is revolutionizing industries through device interconnectivity, enabling real-time data collection and transmission for enhanced monitoring, control, and automation. This has led to improvements in predictive maintenance, production optimization, and supply chain management. The integration of machine learning (ML) and deep learning (DL) within IIoT has been accelerated by increased data availability, computational advancements, and algorithmic innovations, with applications ranging from image processing to autonomous vehicles. Foundation models, such as ChatGPT, are becoming prevalent in IIoT for their capabilities in natural language processing and computer vision. However, the growth of IIoT and foundation models presents challenges, including data volume, real-time processing requirements, computational costs, and security vulnerabilities. Federated learning (FL) addresses these issues by allowing distributed model training without raw data transfer, enhancing privacy and security. FL is particularly beneficial for IIoT’s decentralized architecture and real-time decision-making needs. Despite the advantages, FL faces challenges such as data heterogeneity and communication overhead. To overcome these, we propose a federated learning framework, Fed-CCSQMA, which includes client selection and model aggregation modules to mitigate data heterogeneity’s impact. The client selection module uses Principal Component Analysis (PCA) and clustering to select clients with diverse yet representative data, while the model aggregation module assigns weights based on model accuracy to ensure faster global model convergence. We test our proposed framework on FMNIST and CIFAR-10 datasets, with accuracy surpassing baseline methods and faster convergence, demonstrating an improvement in overall generalizability and learning efficiency. © 2014 IEEE.

关键词： Decision making

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Performance Analysis of Distributed Learning in Edge Computing on Handwritten Digits Dataset 1

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9th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2023

作者： Vo, Tinh Phuc Nguyen, Viet Anh Nguyen, Xuyen Bao Le Dang, Duc Ngoc Minh Tran, Anh Khoa Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Computing Fundamental Department FPT University Ho Chi Minh City Viet Nam

ISBN: (数字)9783031473593

ISBN: (纸本)9783031473586

Deep learning models often consist of millions or even billions of parameters, making it challenging to deploy them on devices with limited resources. Therefore, this study presents scenarios to assess the computational capability of edge devices to provide an evaluation of the learning performance of distributed learning methods. It focuses on using Deep Neural Network and the handwritten digit dataset (MNIST) in edge computing to evaluate the performance of distributed learning methods (no-offloading, full-offloading, split computing, and federated computing) in both ideal and realistic conditions. The performance evaluations are based on Precision, Recall, Accuracy, F1-score, and Estimated time complexity. The findings indicate that the full-offloading method achieved the highest performance in ideal conditions. However, in realistic situations, the split computing and federated computing methods performed better than the others. © 2023, © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Deep neural networks

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Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records

arXiv

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arXiv 2025年

作者： Yilmaz, Baris Akagündüz, Erdem Tileylioglu, Salih Dept. of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara Turkey Civil Engineering Department Kadir Has University Istanbul Turkey

This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface (Vs30) at strong motion recording stations in Türkiye. Vs30 is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of Vs30. We believe the study provides valuable insights into improving Vs30 predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this repo: https://***/brsylmz23/CNNLSTM_DeepEQ © 2025, CC BY.

关键词： Shear waves

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Significant chlorine emissions from biomass burning affect the long-term atmospheric chemistry in Asia

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National Science Review 2024年第9期11卷 213-222页

作者： Di Chang Qinyi Li Zhe Wang Jianing Dai Xiao Fu Jia Guo Lei Zhu Dongchuan Pu Carlos A.Cuevas Rafael P.Fernandez Weigang Wang Maofa Ge Jimmy C.H.Fung Alexis K.H.Lau Claire Granier Guy Brasseur Andrea Pozzer Alfonso Saiz-Lopez Yu Song Tao Wang Atmospheric Chemistry Department Max Planck Institute for Chemistry Department of Atmospheric Chemistry and Climate Institute of Physical Chemistry Blas CabreraCSIC Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Environment Research Institute Shandong University Division of Environment and Sustainability The Hong Kong University of Science and Technology Environmental Modeling Group Max Planck Institute for Meteorology Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua University Environmental Central Facility Institute for the Environment The Hong Kong University of Science and Technology Atmospheric Chemistry Modeling& Remote Sensing Research Group Southern University of Science and Technology Institute for Interdisciplinary Science (ICB) National Research Council (CONICET)FCEN-UNCuyo Institute of Chemistry Chinese Academy of Sciences NOAA Chemical Sciences Laboratory/CIRES University of Colorado Laboratoire d’Aerologie CNRSUniversity of Toulouse UPS Atmospheric Chemistry Observation & Modeling Laboratory National Center for Atmospheric Research State Key Joint Laboratory of Environmental Simulation and Pollution Control Department of Environmental Science Peking University

Biomass burning（BB） is a major source of trace gases and particles in the atmosphere,influencing air quality,radiative balance,and *** studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on *** chlorine chemistry has significant effects on atmospheric chemistry and air ***,quantitative information on chlorine emissions from BB,particularly the long-term trend and associated atmospheric impacts,is limited both on regional and global ***,we report a long-term（20 01-2018） high-resolution BB emission inventory for the major chlorine-containing compounds（HCl,chloride,and CH3Cl） in Asia based on satellite *** estimate an average of 730 Gg yr-1chlorine emitted from BB activity in Asia,with China contributing the largest share at 24.2%(177 Gg yr-1),followed by Myanmar at 18.7% and India at 18.3%.Distinct seasonal patterns and significant spatial and interannual variability are observe d,mainly driven by human-mediated changes in agricultural *** incorporating the newly developed chlorine emission inventory into a global chemistry-climate model（CAM-Chem）,we find that the BB-chlorine emissions lead to elevated levels of HCl and CH3Cl(monthly average up to 2062 and 1421 parts per trillion by volume（pptv）,respectively),subsequently resulting in noticeable changes in oxidants（up to 3.1% in O3and 17% in OH radicals）.The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants,suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously *** light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide,the contribution of BB emissions may become fundamental to air quality composition in the future.

关键词： biomass burning emission chlorine emission inventory tropospheric halogen chemistry atmospheric oxidants CAM-Chem model

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Increased Need for Data Analytics Education in Support of Verification and Validation

Increased Need for Data Analytics Education in Support of Ve...

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2021 Winter simulation Conference, WSC 2021

作者： Lynch, Christopher J. Gore, Ross Collins, Andrew J. Cotter, T. Steven Grigoryan, Gayane Leathrum, James F. Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA United States Old Dominion University Department of Engineering Management and Systems Engineering NorfolkVA United States Old Dominion University Department of Computation Modeling and Simulation Engineering Department NorfolkVA United States

ISBN: (纸本)9781665433112

Computational simulation studies utilize data to assist in developing models, including conducting verification and validation (V V). Input modeling and V V are, historically, difficult topics to teach and they are often only offered as a cursory introduction, leaving the practitioner to pick up the skills on the job. This problem of teaching is often a result of the inability to introduce realistic datasets into class examples because 'real world' data tends to come in poorly formed datasets. In this paper, a case is made that teaching data analytics can help ease this problem. Data analytics is an approach that includes data wrangling, data mining, and exploratory analyses through visualization and machine learning. We provide a brief discussion on how data analytics has been be applied to computational modeling and simulation in the context of verification and validation, but also for input modeling as that gives a basis for validation. © 2021 IEEE.

关键词： Data Analytics

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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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Islanding Detection In Bulk Power Systems: A Data-Driven Approach

Islanding Detection In Bulk Power Systems: A Data-Driven App...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Hêmin Golpîra Igor Kuzle Department of Electrical Engineering Power Systems Modeling & Simulation Lab. University of Kurdistan Sanandaj Iran Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

An attempt is made to propose a data-driven approach for detecting islanding in large-scale power systems. The method utilizes data collected by phasor measurement units (PMUs) to develop an equivalent model of the system. In this model, the system is represented by multiple centers of inertia (COIs) linked to a central point known as the center of gravity (COG) through fictitious reactances. These reactances, which are interpreted as the electrical distances between the local COIs and the COG, serve as a valuable indicator for detecting islanding. The occurrence of islanding can be identified by comparing the temporal trends of electrical distance variations across different areas. The effectiveness of the proposed methodologies is evaluated using simulated data from the 73-bus IEEE test system.

关键词： Islanding Europe Market research Phasor measurement units Smart grids Power system reliability Reliability Transient analysis Monitoring Gravity

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