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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Krishna Chaitanya Sunkara Pareshwar Prasad Barmola Ravi K S Vishal Shukla Umang Soni Amol Sapatnekar Oracle Cloud Engineering Data Center Design & Modeling AL/ML Expert North Carolina USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering Vidyavardhaka College of Engineering Mysuru India Department of Mechanical Engineering Ramdeobaba University Nagpur India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid expansion of Internet of Things (IoT) ecosystems has increased the complexity and vulnerability of software systems, making them more susceptible to security breaches and performance inefficiencies. Traditional security and optimization techniques struggle to keep up with the dynamic nature of IoT networks. This calls for the development of advanced frameworks that can adapt in real-time to evolving threats and operational requirements. This paper introduces Cyber Twins, an AI-driven framework designed for real-time security and software optimization in IoT environments. Cyber Twins leverage artificial intelligence and digital twin technology to create dynamic cyber representations of physical IoT devices and software processes. The framework integrates reinforcement learning for adaptive optimization, convolutional neural networks (CNNs) for anomaly detection, and graph neural networks (GNNs) for real-time threat analysis. By continuously synchronizing with live IoT data, Cyber Twins enable proactive threat detection and autonomous system optimization, ensuring a secure and high-performing IoT ecosystem. The proposed framework combines Federated Learning (FL) for distributed intelligence and Reinforcement Learning (RL) for adaptive decision-making. Key advancements include (1) a 35% improvement in intrusion detection accuracy compared to traditional methods, (2) a 40% reduction in false positive rates, and (3) a 25% increase in dynamic software adaptation speed. Experiments conducted on a simulated IoT environment with 1,000 devices demonstrated scalability and resilience under varying network conditions. Additionally, the framework achieved a 20% decrease in latency during AI model updates and a 30% reduction in computational overhead, ensuring efficient resource utilization.

关键词： Ecosystems Reinforcement learning Software Real-time systems Threat assessment Internet of Things Security Artificial intelligence Optimization Resilience

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Computationally Efficient Molecular Integrals of Solid Harmonic Gaussian Orbitals Using Quantum Entanglement of Angular Momentum

arXiv

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arXiv 2024年

作者： Hu, Hang Peslherbe, Gilles Ooi, Hsu Kiang Hu, Anguang Centre for Research in Molecular Modeling Department of Chemistry and Biochemistry Concordia University MontrealQCH4B 1R6 Canada Data Science for Complex Systems National Research Council of Canada 222 College Street TorontoONM5T 3J1 Canada Suffield Research Center Defence Research Development Canada PO Box 4000 Main Station Medicine HatABT1A 8K6 Canada

Evaluating multi-center molecular integrals with Cartesian Gaussian-type basis sets has been a long-standing bottleneck in electronic structure theory calculation for solids and molecules. We have developed a vector-coupling and vector-uncoupling scheme to solve molecular Coulomb integrals with solid harmonics basis functions(SHGO). Solid harmonics are eigenstates of angular momentum, making it possible to factorize molecular integrals. By combining solid harmonic addition, differential and product rules, the computationally costly multi-center four-center integrals can be factored into an angular part and a radial component dependent on the atomic positions. The potential speed-up ratio in evaluating molecular nuclear Coulomb integrals in our method can reach up to four orders of magnitude for atomic orbitals with high angular momentum quantum numbers. The foundation underpinning the mathematical efficiency is the quantum angular momentum theory, where both vector-coupling and vector-uncoupling schemes correspond to unitary Clebsch-Gordan transformations that act on quantum angular momentum states, influencing their degree of entanglement. By incorporating quantum angular momentum through these transformations, the entanglement of the states can be reduced, and the less entanglement there is for a quantum system, the easier it is to simulate. The highly efficient method unveiled here opens new avenues for accelerated material and molecule design and discovery. © 2024, CC BY-NC-SA.

关键词： Molecules

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The networks of ingredient combination in cuisines around the world

arXiv

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arXiv 2024年

作者： Caprioli, Claudio Kulkarni, Saumitra Battiston, Federico Iacopini, Iacopo Santoro, Andrea Latora, Vito Department of Physics and Astronomy University of Catania Catania95125 Italy Department of Scientific Computing Modeling & Simulation Savitribai Phule Pune University Pune411007 India Department of Network and Data Science Central European University Vienna1100 Austria Network Science Institute Northeastern University London LondonE1W 1LP United Kingdom Department of Physics Northeastern University BostonMA02115 United States Neuro-X Institute École Polytechnique Fédérale de Lausanne Geneva1202 Switzerland School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom Complexity Science Hub Vienna1080 Austria

Investigating how different ingredients are combined in popular dishes is crucial to reveal the fundamental principles behind the formation of food preferences. Here, we use data from food repositories and network analysis to characterize worldwide cuisines. In our framework, each cuisine is represented as a network, where nodes correspond to ingredient types and weighted links describe how frequently pairs of ingredient types appear together in recipes. The networks of ingredient combinations reveal cuisine-specific patterns, highlighting similarities and differences in gastronomic preferences across different world regions. We find that popular ingredients, recurrent combinations, and the way they are organized within the backbone of the network provide a unique fingerprint for each cuisine. Hence, we demonstrate that networks of ingredient combinations are able to cluster global cuisines into meaningful geo-cultural groups, and can also be used to train models to uniquely identify a cuisine from a subset of its recipes. Our study advances our understanding of food combinations and helps uncover the geography of taste, paving the way for the creation of new and innovative recipes. Copyright © 2024, The Authors. All rights reserved.

关键词： Food ingredients

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Response of Growing Season Gross Primary Production to El Nino in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

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Advances in Atmospheric sciences 2021年第9期38卷 1580-1595页

作者： Yueyue LI Li DAN Jing PENG Junbang WANG Fuqiang YANG Dongdong GAO Xiujing YANG Qiang YU Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of ScienceBeijing 100049China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F UniversityShaanxi 712100China School of Life Sciences University of Technology SydneySydney 2007NSWAustralia

Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two *** consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this *** previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically *** paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.

关键词： East China Bayesian model averaging Gross primary production El Nino Pacific Decadal Oscillation Monsoon rainfall

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data-driven balancing of linear dynamical systems

arXiv

引用

arXiv 2021年

作者： Gosea, Ion Victor Gugercin, Serkan Beattie, Christopher Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA24061 United States

We present a novel reformulation of balanced truncation, a classical model reduction method. The principal innovation that we introduce comes through the use of system response data that has been either measured or computed, without reference to any prescribed realization of the original model. data are represented by sampled values of the transfer function or the impulse response corresponding to the original model. We discuss parallels that our approach bears with the Loewner framework, another popular data-driven method. We illustrate our approach numerically in both continuous-time and discrete-time *** Codes 37M99, 41A20, 65D30, 65K99, 93A15, 93B15 Copyright © 2021, The Authors. All rights reserved.

关键词： Impulse response

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data-driven modeling of linear dynamical systems with quadratic output in the AAA framework

arXiv

引用

arXiv 2021年

作者： Gosea, Ion Victor Gugercin, Serkan Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany Department of Mathematics and Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA24061 United States

We extend the AAA (Adaptive-Antoulas-Anderson) algorithm to develop a data-driven modeling framework for linear systems with quadratic output (LQO). Such systems are characterized by two transfer functions: one corresponding to the linear part of the output and another one to the quadratic part. We first establish the joint barycentric representations and the interpolation theory for the two transfer functions of LQO systems. This analysis leads to the proposed AAA-LQO algorithm. We show that by interpolating the transfer function values on a subset of samples together with imposing a least-squares minimization on the rest, we construct reliable data-driven LQO models. Two numerical test cases illustrate the efficiency of the proposed *** Codes 41A20, 93D25, 93C10 Copyright © 2021, The Authors. All rights reserved.

关键词： Interpolation

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Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

引用

science of the Total Environment 2024年 955卷 177169页

作者： Zhang, Jiang Chen, Huai Peng, Changhui Huang, Yuanyuan Wang, Yike Liu, Xinwei Wang, Meng Wang, Le Yu, Dongxue Zhu, Qiuan Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case Postale 8888 Succursale Centre-Ville MontrealQCH3C 3P8 Canada School of Geography Science Hunan Normal University Changsha410081 China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Changchun130024 China Key Laboratory of Soil and Water Processes in Watershed College of Geography and Remote Sensing Hohai University Nanjing210098 China National Earth System Science Data Center National Science & Technology Infrastructure of China Beijing100101 China

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios. © 2024 Elsevier B.V.

关键词： Greenhouse gas emissions

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The Bowen Ratio of an Alpine Grassland in Three-River Headwaters, Qinghai-Tibet Plateau, from 2001 to 2018

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Journal of Resources and Ecology 2021年第3期12卷 305-318页

作者： ZHAO Xuanlan WANG Junbang YE Hui MUHAMMAD Amir WANG Shaoqiang National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China College of Tourism and Geography JiuJiang UniversityJiujiangJiangxi 332005China

The Bowen ratio(β) is used to quantify heat transfer from the land surface into the air, which is becoming a hot topic in research on the biogeophysical effects of land use and cover changes. The Three-River Headwaters(TRH), as a sensitive and fragile region, was selected as the study area. The β for 2001–2018 was estimated from the evapotranspiration product(ETMOD16) of MODIS and the net radiation of the land surface through the albedo from GLASS. The ETMOD16 data were evaluated against the observation data(ETOBS) at two alpine grassland flux towers obtained from ChinaFLUX. The interannual trend of the β was analyzed by multiple linear regression(MLR) and structure model(SEM) with the multiple factors of precipitation, temperature, humidity, albedo, and normalized difference vegetation index(NDVI, MOD09 Q1). The results show that the ETMOD16 values were significantly correlated with ETOBS, with a correlation coefficient above 0.70(P < 0.01) for the two sites. In 2001–2018, the regional mean β was 2.52 ± 0.77 for the whole grassland, and its spatial distribution gradually increased from the eastern to western region. The interannual β showed a downward trend with a slope of-0.025 and a multiple regression coefficient(R^(2)) of 0.21(P = 0.056). Most of the variability(51%) in the interannual β can be explained by the linear regression of the above multiple factors, and the temperature plays a dominant role for the whole region. The SEM analysis further shows that an increasing NDVI results in a decreasing albedo with a path coefficient of-0.57, because the albedo was negatively correlated with NDVI(R^(2) = 0.52, P < 0.01), which indicates a negative and indirect effect on β from vegetation restoration. An obvious warming climate was found to prompt more evapotranspiration, and restoring vegetation makes the land surface receive more radiation, which both resulted in a decreasing trend in the annual β. This study revealed the biogeophysical mechanisms of vegetation re

关键词： alpine grassland Bowen ratio Three-River Headwaters MODIS evapotranspiration

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Preface

Springer Proceedings in Mathematics and Statistics

引用

Springer Proceedings in Mathematics and Statistics 2021年 370卷 v-vi页

作者： Suzuki, Takashi Centre for Mathematical Modeling & Data Science Osaka University Toyonaka Japan

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Improvement of ecosystem quality in National Key Ecological Function Zones in China during 2000–2015

引用

Journal of Environmental Management 2022年 324卷 116406页

作者： Zhang, Mengyu Zhang, Li He, Honglin Ren, Xiaoli Lv, Yan Niu, Zhong'en Chang, Qingqing Xu, Qian Liu, Weihua Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China National Ecosystem Science Data Center Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100190 China School of Resources and Environmental Engineering Ludong University Shandong 264025 China

Improving ecosystem quality is the ultimate goal of ecological restoration projects and sustainable ecosystem management. However, previous results of ecosystem quality lack comparability among different regions when assessing the effectiveness of ecological restoration projects on the regional or national scales, due to the influence of geographical and climatic background conditions. Here we proposed a new index, ecosystem quality ratio (EQR), by integrating the status of landscape structure, ecosystem services, ecosystem stability, and human disturbance relative to their reference conditions, and assessed the EQR changes in China's counties and National Key Ecological Function Zones (NKEFZs) from 1990 to 2015. The results showed that the average ecosystem quality of China's counties deviated from the reference condition by 28%. EQR decreased by 1.2% during 1990–2000 but increased by 3.7% during 2000–2015. Those counties with increasing EQR in 2000–2015 occupy 64.7%, with obviously increasing counties mainly located in the water conservation, biodiversity maintenance, and water and soil conservation types of NKEFZs. The EQR increase in counties within NKEFZs was 3.65 times that outside of NKEFZs. Remarkable improvement of ecosystem quality occurred in the forest region in Changbai Mountain, biodiversity and soil conservation region in Wuling Mountains, and hilly and gully region of Loess Plateau, where EQR increases mainly resulted from the conversion of farmland to forest or grassland and consequent increases in ecosystem services and stability. The magnitude of EQR enhancement showed a positive relationship with the increase in forest and grassland coverage in NKEFZs. Our results highlight the important role of ecological restoration projects in improving ecosystem quality in China, and demonstrate the feasibility of the new index (EQR) for the assessment of ecosystem quality in terms of ecosystem management and restoration. © 2022 Elsevier Ltd

关键词： Ecosystems

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