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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Favela, Luis H. Amon, Mary Jean University of Central Florida Cognitive Sciences Program Department of Philosophy OrlandoFL United States University of Central Florida School of Modeling Simulation and Training OrlandoFL United States

ISBN: (纸本)9798350318470

In recent years, generative artificial intelligence (AI) in the form of large language models (LLM) have sparked the interest of society at large. The perceived capabilities of such systems have reignited discussions concerning the actual or potential threats posed by AI. According to Daniel Dennett, these systems make possible the creation of counterfeit people, who can pass as real in digital environments like social media. Dennett claims that by undermining trust in relationships, counterfeit people pose a threat to democracy and human freedom. While the idea of counterfeit people is worrisome in the context of digital manipulation, we claim that human digital twins have the potential to facilitate human rights violations that may pose even greater challenges. High-fidelity human digital twins necessitate encroaching into features that constitute a human's personhood, such as physical aspects and mental contents. In view of that, their creation raises pressing issues of consent and violations of privacy rights. As a result, because rights to privacy are rights of persons, such violations will simultaneously be human rights violations. Even with consent to use an individual's data, human digital twins may still cause issues of personhood. The rapid adoption of technologies that facilitate counterfeit people and human digital twins demands that ethical issues not be treated as aside concerns, but at the forefront of technology development. © 2023 IEEE.

关键词： Ethical technology

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Epidemiological modeling of Influenza-Like Illness(ILI)transmission in Jakarta,Indonesia through cumulative generating operator on SLIR model

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生物安全和生物安保（英文） 2023年第4期5卷 135-145页

作者： Ilham Saiful Fauzi Imaniah Bazlina Wardani Nuning Nuraini Department of Accounting Politeknik Negeri MalangMalangIndonesia Study Program of Biology Education Faculty of Education and Teacher TrainingUIN Kiai Haji Achmad Siddiq JemberJemberIndonesia Department of Mathematics Faculty of Mathematics and Natural SciencesInstitut Teknologi BandungBandungIndonesia Center for Mathematical Modeling and Simulation Institut Teknologi BandungBandungIndonesia

Influenza-Like Illness(ILI)constitutes a significant global health concern characterized by its high infec-tion rates and widespread distribution *** influenza viruses,primarily types A and B,are primary contributors to ILI cases,other respiratory viruses also play a role in its ***,Indonesia's largest and densely populated city,has consistently reported a notable weekly number of ILI cases from 2016 to ***,this pattern of cases is irregular and does not exhibit a direct association with seasonal climate *** response to this complex scenario,we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function,obtained from weekly cumulative data to derive model solutions.A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 *** correlation analysis unveiled distinct temporal relationships within the model,revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment,along with a 2-week lag marking the incubation period between the latent and infected ***,the effective reproduction number displayed recurrent fluctuations around a threshold of 1,indicating the endemic characteristics where infection persists within the *** in-depth comprehension of ILI trans-mission dynamics and effective reproduction numbers plays a significant role in devising control mea-sures and informed policy-making decisions.

关键词： Influenza-like illness SLIR model Cumulative generating operator Sigmoid function Effective reproduction number

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The ethics of human digital twins: Counterfeit people, personhood, and the right to privacy

The ethics of human digital twins: Counterfeit people, perso...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Luis H. Favela Mary Jean Amon Department of Philosophy Cognitive Sciences Program University of Central Florida Orlando FL USA School of Modeling Simulation and Training University of Central Florida Orlando FL USA

In recent years, generative artificial intelligence (AI) in the form of large language models (LLM) have sparked the interest of society at large. The perceived capabilities of such systems have reignited discussions concerning the actual or potential threats posed by AI. According to Daniel Dennett, these systems make possible the creation of counterfeit people, who can pass as real in digital environments like social media. Dennett claims that by undermining trust in relationships, counterfeit people pose a threat to democracy and human freedom. While the idea of counterfeit people is worrisome in the context of digital manipulation, we claim that human digital twins have the potential to facilitate human rights violations that may pose even greater challenges. High-fidelity human digital twins necessitate encroaching into features that constitute a human’s personhood, such as physical aspects and mental contents. In view of that, their creation raises pressing issues of consent and violations of privacy rights. As a result, because rights to privacy are rights of persons, such violations will simultaneously be human rights violations. Even with consent to use an individual’s data, human digital twins may still cause issues of personhood. The rapid adoption of technologies that facilitate counterfeit people and human digital twins demands that ethical issues not be treated as aside concerns, but at the forefront of technology development.

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The Integrated Game Transformation Framework and Cyberwar: What 2×2 Games Tell Us About Cyberattacks 14th

The Integrated Game Transformation Framework and Cyberwar: W...

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14th International Conference on Social, Cultural, and Behavioral modeling, SBP-BRiMS 2021

作者： Fretz, Alexander L. Padilla, Jose J. Frydenlund, Erika F. Graduate Program in International Studies Old Dominion University Norfolk United States Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk United States

ISBN: (纸本)9783030803865

We apply a game theory framework to cyber war in the areas of Cyber Influence Operations (CIOs), Advanced Persistent Threats (APTs), and Traditional Cyber Attacks (TCAs). For greater generalizability, we rely on a set of actor typologies based on posture rather than individual countries. Our findings suggest that cyberwar will remain problematic going forward, especially with respect to APTs and that great powers will be the most likely offenders. They will also be the most stubborn actors when it comes to diminishing cyberwar in general, even when the base game can be transformed into one that favors cooperation over conflict. © Springer Nature Switzerland AG 2021.

关键词： Cybersecurity

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Assessing Media’s Representation of Frustration Towards Venezuelan Migrants in Colombia 16th

Assessing Media’s Representation of Frustration Towards Ven...

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16th International Conference on Social Computing, Behavioral-Cultural modeling and Prediction and Behavior Representation in modeling and simulation, SBP-BRiMS 2023

作者： Llinas, Brian Huseynli, Guljannat Frydenlund, Erika Palacio, Katherine Llinas, Humberto Padilla, Jose J. Computer Science Department Old Dominion University NorfolkVA United States Graduate Program of International Studies Old Dominion University NorfolkVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA United States Universidad del Norte Kilómetro 5 Vía Atlántico Puerto Colombia Colombia

ISBN: (纸本)9783031431289

This research utilizes statistical path modeling to examine the relationship between the number of Venezuelans migrating to Colombia due to economic collapse and the media's coverage of specific topics. We focus on media coverage expressing frustration regarding migration, infrastructure, government, and geopolitics. Our approach combines quantitative analysis of code frequencies from qualitative content analysis of 1,360 regional newspaper articles on migration-related issues. Our findings indicate that an increase in the Venezuelan population in Colombia leads to more negative media coverage of infrastructure impacts, such as hospitals and schools. This negative coverage, referred to as "frustration," indirectly contributes to increased coverage of infrastructure frustration. This suggests an opportunity for intervention by balancing media coverage to reduce the excessive use of negative language and stereotypes, potentially mitigating anti-migrant sentiment fueled by frustration towards infrastructure. Furthermore, we observe that as migration increases, coverage of frustration towards geopolitics decreases, while frustration towards the Colombian government rises. This shift may reflect a transition from contemplating the causes of migration to focusing on the humanitarian response (or lack thereof) by the host government in accommodating the new arrivals. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Combined Deep Learning and Classical Potential Approach for modeling Diffusion in UiO-66

Combined Deep Learning and Classical Potential Approach for ...

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2023 AIChE Annual Meeting

作者： Achar, Siddarth K. Wardzala, Jacob J. Bernasconi, Leonardo Zhang, Linfeng Johnson, J. Karl Department of Chemical & Petroleum Engineering University of Pittsburgh PittsburghPA15261 United States Computational Modeling & Simulation Program University of Pittsburgh PittsburghPA15260 United States Center for Research Computing Department of Chemistry University of Pittsburgh PittsburghPA15260 United States DP Technology Beijing100080 China AI for Science Institute Beijing100080 China

modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal−organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal− organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate−adsorbate interactions were modeled with Lennard−Jones (LJ) potentials, the adsorbent−adsorbent interactions were described by the DP, and the adsorbent−adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent−adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP−classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for ads

关键词： Molecular dynamics

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Assessing the impact of booster vaccination on diphtheria transmission:Mathematical modeling and risk zone mapping

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Infectious Disease Modelling 2024年第1期9卷 245-262页

作者： Ilham Saiful Fauzi Nuning Nuraini Ade Maya Sari Imaniah Bazlina Wardani Delsi Taurustiati Purnama Magdalena Simanullang Bony Wiem Lestari Department of Accounting Politeknik Negeri MalangMalangIndonesia Department of Mathematics Faculty of Mathematics and Natural SciencesInstitut Teknologi BandungBandungIndonesia Center for Mathematical Modeling and Simulation Institut Teknologi BandungBandungIndonesia Study Program of Biology Education Faculty of Education and Teacher TrainingUIN Kiai Haji Achmad Siddiq JemberJemberIndonesia West Java Provincial Health Office West JavaIndonesia Department of Public Health Faculty of MedicineUniversitas PadjadjaranBandungIndonesia Department of Internal Medicine Radboud Institute for Health SciencesRadboud University Medical CentreNijmegenthe Netherlands

The COVID-19 pandemic caused significant disruptions in the healthcare system,affecting vaccinations and the management of diphtheria *** a consequence of these disruptions,numerous countries have experienced a resurgence or an increase in diphtheria *** Java province in Indonesia is identified as one of the high-risk areas for diphtheria,experiencing an upward trend in cases from 2021 to *** analyze the situation,we developed an SIR model,which integrated DPT and booster vaccinations to determine the basic reproduction number,an essential parameter for infectious *** spatial analysis of geo-referenced data,we identified hotspots and explained diffusion in diphtheria case *** calculation of R0 resulted in an R0=1.17,indicating the potential for a diphtheria outbreak in West *** control the increasing cases,one possible approach is to raise the booster vaccination coverage from the current 64.84%to 75.15%,as suggested by simulation ***,the spatial analysis revealed that hot spot clusters were present in the western,central,and southern regions,posing a high risk not only in densely populated areas but also in rural *** diffusion pattern of diphtheria clusters displayed an expansion-contagious *** the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.

关键词： Diphtheria Vaccination Mathematical modeling SIR model Hot spot analysis

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Towards a deep learning potential for anhydrous proton transport

Towards a deep learning potential for anhydrous proton trans...

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2020 AIChE Annual Meeting

作者： Achar, Siddarth Johnson, J. Karl Computational Modeling and Simulation Program University of Pittsburgh United States Department of Chemical Engineering University of Pittsburgh United States

ISBN: (纸本)9780816911141

Standard density functional theory (DFT) molecular dynamic (MD) simulations are prohibitively expensive in terms ofcompute time and memory requirements when the size of the physical system is larger than several hundred *** alternate approach is to develop a deep learning neural network potential that can accurately reproduce DFTenergies within an error of several 10s of meVs. Neural network potentials thus allow large-scale MD simulations formuch longer times than can be achieved with DFT-MD, but with comparable accuracy. We have used the recentlypublished DeePMD [1] formalism to develop potentials for systems relevant to modeling proton transport onfunctionalized graphane. We have used DFT-MD calculations for small structures of graphane and graphanol astraining data to build reliable machine-learned potentials. Apart from energies and forces, vibrational and thermalproperties such as phonon dispersion relations and thermal expansion coefficients from DFT-MD were used to validatethe accuracy of our trained DeePMD potentials. Our goal is to develop a machine learning potential that is able toaccurately predict proton conduction on graphanol in the complete absence of water. Previous DFT calculations haveshown that graphanol a promising candidate for anhydrous proton exchange membrane applications [2]. We seek toidentify large-scale and long-time proton hopping behavior for this system through the use of machine learningpotentials. © 2020 American Institute of Chemical Engineers. All rights reserved.

关键词： Density functional theory

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Using machine learning to increase model performance for a gas turbine system

Using machine learning to increase model performance for a g...

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2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019

作者： Hipple, Samuel M. Reinhart, Zachary T. Bonilla-Alvarado, Harry Pezzini, Paolo Bryden, Kenneth Mark Simulation Modeling and Decision Science Program Ames Laboratory 1620 Howe Hall AmesIA50011 United States

ISBN: (纸本)9780791883747

With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations. This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physicsbased model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations. Copyright © 2020 ASME.

关键词： Gas turbines

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Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset

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Physical Review Materials 2022年第4期6卷 044004-044004页

作者： Amirreza Hashemi Ruiqiang Guo Keivan Esfarjani Sangyeop Lee Computational Modeling and Simulation Program University of Pittsburgh Pittsburgh Pennsylvania 15261 USA Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburgh Pennsylvania 15261 USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Physics University of Virginia Charlottesville Virginia 22904 USA Department of Physics and Astronomy University of Pittsburgh Pittsburgh Pennsylvania 15261 USA

Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires extensive size of the training dataset, making it a less feasible approach. Here, we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. We employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. In this paper, we show that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycry

关键词： Phonons Graphene Landauer formula Machine learning

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