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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

Computer Modeling in Engineering & Sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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Modeling Crowdedness of Emergency departments Leveraging Crowdsensing Mobility Data 19

Modeling Crowdedness of Emergency Departments Leveraging Cro...

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2022 IEEE smartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, smartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shou, Tieqi Wang, Zhiyuan Shi, Shang Yang, Dingqi Zhou, Binbin Wang, Cheng Chen, Longbiao Xiamen University SpAtial Sensing and Computing Lab Xiamen China University of Virginia School of Engineering and Applied Science Charlottesville United States Imperial College London Department of Mathematics London United Kingdom University of Macau State Key Laboratory of Internet of Things for Smart City China Zhejiang University City College Department of Computer Science and Computing Hangzhou China

ISBN: (纸本)9798350346558

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers. © 2022 IEEE.

关键词： Emergency services

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On the irrationality of moduli spaces of projective hyperkähler manifolds

arXiv

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arXiv 2023年

作者： Agostini, Daniele Barros, Ignacio Lai, Kuan-Wen Eberhard Karls Universität Tübingen Fachbereich Mathematik Auf der Morgenstelle 10 Tübingen72072 Germany Department of Mathematics Universiteit Antwerpen Middelheimlaan 1 Antwerpen2020 Belgium Department of Smart Computing and Applied Mathematics Tunghai University No. 1727 Sec. 4 Taiwan Blvd. Xitun Dist. Taichung City407224 Taiwan

The aim of this paper is to estimate the irrationality of moduli spaces of hyperkähler manifolds of types K3[n], Kumn, OG6, and OG10. We prove that the degrees of irrationality of these moduli spaces are bounded from above by a universal polynomial in the dimension and degree of the manifolds they parametrize. We also give a polynomial bound for the degrees of irrationality of moduli spaces of (1, d)-polarized abelian surfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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Modeling Crowdedness of Emergency departments Leveraging Crowdsensing Mobility Data

Modeling Crowdedness of Emergency Departments Leveraging Cro...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Tieqi Shou Zhiyuan Wang Shang Shi Dingqi Yang Binbin Zhou Cheng Wang Longbiao Chen spAtial Sensing and Computing Lab Xiamen University Xiamen China School of Engineering and Applied Science University of Virginia Charlottesville USA Department of Mathematics Imperial College London London United Kingdom State Key Laboratory of Internet of Things for Smart City University of Macau Macao China Department of Computer Science and Computing Zhejiang University City College Hangzhou China

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Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image 7

Machine-Learning-Scheme to Detect Choroidal-Neovascularizati...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Taniar, David Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Monash University Faculty of Information Technology ClaytonVIC3800 Australia Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes;pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study. © 2021 IEEE.

关键词： Optical tomography

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Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features 7

Breast-Cancer Detection using Thermal Images with Marine-Pre...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Rajinikanth, Venkatesan Kadry, Seifedine Taniar, David Damasevicius, Robertas Rauf, Hafiz Tayyab St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Monash University Faculty of Information Technology ClaytonVIC3800 Australia Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom

ISBN: (纸本)9781665441261

Breast-cancer (BC) is one of the major diseases in women group and the early diagnosis and treatment is necessary to cure the disease. Early detection of BC is very essential to implement appropriate treatment and the proposed research aims to develop an automated BC detection system using Breast-Thermal-Images (BTI). The executed approach is as follows;(i) Recording the image for various breast orientation, (ii) Extracting the healthy/DCIS image patches, (iii) Treating the patches with image processing scheme, (iv) Feature extraction, (v) Feature optimization with Marine-Predators-Algorithm (MPA), and (vi) Two-class classification and validation. In this work, the essential image features, such as GLCM and LBP with varied weights are considered to classify the clinically collected BTI into healthy/DCIS class using a chosen two-class classifier. The result of this study confirms that the Decision-Tree (DT) classifier helps to achieve enhanced accuracy (>92%) compared to other methods adopted in this research. © 2021 IEEE.

关键词： Diseases

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FROBENIUS NONCLASSICAL HYPERSURFACES

arXiv

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arXiv 2022年

作者： Asgarli, Shamil Duan, Lian Lai, Kuan-Wen Department of Mathematics and Computer Science Santa Clara University Santa ClaraCA95050 United States Institute of Mathematical Sciences ShanghaiTech University No.393 Middle Huaxia Road Pudong New District Shanghai China Department of Smart Computing and Applied Mathematics Tunghai University No. 1727 Sec. 4 Taiwan Blvd. Xitun Dist. Taichung City407224 Taiwan

A smooth hypersurface over a finite field Fq is called Frobenius nonclassical if the image of every geometric point under the q-th Frobenius endomorphism remains in the unique hyperplane tangent to the point. In this paper, we establish sharp lower and upper bounds for the degrees of such hypersurfaces, give characterizations for those achieving the maximal degrees, and prove in the surface case that they are Hermitian when their degrees attain the minimum. We also prove that the set of Fq-rational points on a Frobenius nonclassical hypersurface form a blocking set with respect to lines, which indicates the existence of many Fq-points. Copyright © 2022, The Authors. All rights reserved.

关键词： Geometry

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Advanced numerical modeling for nonlinear responses of sandwich multiphase composite plates with viscoelastic damping core

引用

Advances in Engineering Software 2025年 208卷

作者： Ho-Nam Vu Duy-Khuong Ly Umut Topal Tan Nguyen T. Nguyen-Thoi Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Smart Computing in Civil Engineering Research Group Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

This study introduces an advanced numerical model for the nonlinear dynamic analysis of sandwich multiphase composite plates composed of carbon nanotubes (CNT), carbon fibers, and epoxy, featuring a viscoelastic core modeled using the Golla–Hughes–McTavish (GHM) method. The proposed framework uniquely combines the Cell-Based Smoothed Discrete Shear Gap Method (CS-DSG3) with the sinusoidal–zigzag shear deformation theory, pioneering their integration to improve layerwise modeling and viscoelastic damping analysis. The sinusoidal–zigzag theory effectively captures the continuous in-plane displacement distributions, yielding superior predictions of the layered structure’s mechanical response compared to classical theories. By incorporating the von Kármán displacement–strain relationship, the model effectively captures the passive damped dynamic behavior of viscoelastic core plate under large deformations. The Newmark time integration scheme and Picard’s methods are employed to efficiently solve the resulting nonlinear equations of motion at each time step. Validation against benchmark studies demonstrates the model’s accuracy and reliability in capturing the complex dynamic responses of laminated systems. A comprehensive parametric investigation further explores the impact of material properties, including the volume fractions and configurations of CNTs and carbon fibers, on the nonlinear dynamic behavior. These advancements position the model as a computationally efficient and high-fidelity tool for analyzing the nonlinear dynamics of complex laminated composite structures.

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Numerical simulation of intense precipitation in Moscow region: A case study of a heavy rainfall event on June 30, 2017 11

Numerical simulation of intense precipitation in Moscow regi...

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11th International Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems, ENVIROMIS 2020

作者： Yarinich, Yu I Varentsov, M.I. Platonov, V.S. Stepanenko, V.M. Lomonosov Moscow State University Faculty of Geography Department of Meteorology and Climatology Leninskie Gory1 bld.1 GSP-1 Moscow119991 Russia A. M. Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences Pyzhevsky ave. 3 Moscow119017 Russia Lomonosov Moscow State University Research Computing Center GSP-1 Leninskie gory1 bld.4 Moscow199991 Russia Hydrometeorological Research Center of Russian Federation 11-13 Bolshoi Predtechensky ave. Moscow123242 Russia Moscow Center of Fundamental and Applied Mathematics GSP-1 Leninskie gory 1 bld.1 Moscow199991 Russia RUDN University Smart Urban Nature Laboratory Miklukho-Maklaya str. 6 Moscow117198 Russia

This paper considers physical and synoptic mechanisms of an extreme rainfall on June 30, 2017 in the central part of European Russia, which was the maximum of daily precipitation sum in Moscow (65 mm) since 1970. Based on meteorological observations, radar data, and ERA5 reanalysis data, we show that the rainfall was associated with three mesoscale convective systems (MCSs) that formed in the warm sector of a cyclone in a strip of anomalously high moisture content in the considered region, which developed further due to advection and evaporation. A numerical simulation with mesoscale model COSMO shows a significant contribution to the precipitation intensity of evaporation from the Earth's surface: a 10-times decrease in soil the moisture in the initial conditions leads to a 3-times decrease in the amount of precipitation and its intensity. Additionally, we consider urban-induced effects on this rainfall event by switching on and off urban parametrization TERRA_URB. The application of the urban surface parametrization has not changed the average amount of precipitation in Moscow region. It causes, however, a redistribution of precipitation sums within it. © Published under licence by IOP Publishing Ltd.

关键词： Evaporation

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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