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Chaotic dynamics of a fractional order glucose-insulin regulatory system

Frontiers of Information Technology & Electronic engineering 2020年第7期21卷 1108-1118页

作者： Karthikeyan RAJAGOPAL Atiyeh BAYANI Sajad JAFARI Anitha KARTHIKEYAN Iqtadar HUSSAIN Center for Nonlinear Dynamics Defence UniversityBishoflu 1041Ethiopia Institute of Energy Mekelle UniversityMekelle 231Ethiopia Department of Biomedical Engineering Amirkabir University of TechnologyTehran 159163-4311Iran Nonlinear Systems and Applications Faculty of Electrical and Electronics EngineeringTon Due Thang UniversityHo Chi Minh City 700010Vietnam Department of Mathematics Statistics and PhysicsQatar UniversityDoha 2713Qatar

The fractional order model of a glucose-insulin regulatory system is derived and presented. It has been extensively proved in the literature that fractional order analysis of complex systems can reveal interesting and unexplored features of the system. In our investigations we have revealed that the glucose-insulin regulatory system shows multistability and antimonotonicity in its fractional order form. To show the effectiveness of fractional order analysis, all numerical investigations like stability of the equilibrium points, Lyapunov exponents, and bifurcation plots are derived. Various biological disorders caused by an unregulated glucose-insulin system are studied in detail. This may help better understand the regulatory system.

关键词： Diabetes mellitus Chaos Bifurcation Multistability Antimonotonicity

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Toward Resilient Multi-Agent Actor-Critic Algorithms for Distributed Reinforcement Learning

Toward Resilient Multi-Agent Actor-Critic Algorithms for Dis...

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American Control Conference

作者： Yixuan Lin Shripad Gade Romeil Sandhu Ji Liu Department of Applied Mathematics and Statistics Stony Brook University Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Departments of Bioinformatics and Computer Science Stony Brook University Department of Electrical and Computer Engineering Stony Brook University

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper considers a distributed reinforcement learning problem in the presence of Byzantine agents. The system consists of a central coordinating authority called "master agent" and multiple computational entities called "worker agents". The master agent is assumed to be reliable, while, a small fraction of the workers can be Byzantine (malicious) adversaries. The workers are interested in cooperatively maximize a convex combination of the honest (non-malicious) worker agents' long-term returns through communication between the master agent and worker agents. A distributed actor-critic algorithm is studied which makes use of entry-wise trimmed mean. The algorithm's communication-efficiency is improved by allowing the worker agents to send only a scalar-valued variable to the master agent, instead of the entire parameter vector, at each iteration. The improved algorithm involves computing a trimmed mean over only the received scalar-valued variable. It is shown that both algorithms converge almost surely.

关键词： Agents algorithms Workers Learning Distribute Personnel

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ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

arXiv

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arXiv 2023年

作者： Zheng, Zhiling Zhang, Oufan Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States School of Information University of California BerkeleyCA94720 United States KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information-an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines. Copyright © 2023, The Authors. All rights reserved.

关键词： Data mining

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Which random is the best random? A study on sampling methods in Fourier surrogate modeling

Which random is the best random? A study on sampling methods...

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Congress on Evolutionary Computation

作者： Marco S. Nobile Simone Spolaor Paolo Cazzaniga Daniele M. Papetti Daniela Besozzi Daniel A. Ashlock Luca Manzoni Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Eindhoven The Netherlands Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Mathematics and Statistics University of Guelph Guelph ON Canada Department of Mathematics and Geosciences University of Trieste Trieste Italy

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Global optimization problems can be effectively solved by means of Computational Intelligence methods. However, there are several areas in which the effectiveness of these algorithms can be hampered by the computational costs of the fitness evaluations, or by specific features of the fitness landscape that can be characterized by noise and by the presence of several (even infinite) local optima. These issues bring about the necessity of defining specific techniques to replace the original problem with a surrogate representation. Fourier surrogate modeling represents a novel and effective approach to generate smoother, and possibly easier to explore, fitness landscapes, and to reduce the computational effort. Fourier surrogates require an initial sampling of the search space that must be performed to calculate the Fourier transforms. In this paper we investigate the impact on the quality of the surrogate models of the hyper-parameters of the methodology, and of several methods that can be employed for the initial sampling of the fitness landscape (i.e., pseudorandom numbers, low discrepancy sequences, a logistic map in chaotic regime, true random positions generated by a quantum computer, and point packing). Our results show that semistructured approaches like quasi-random sequences and point packing can outperform the other sampling methods.

关键词： Computational modeling Generators Sampling methods Optimization Fourier transforms Logistics Quantum computing

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Realizations of Isostatic Material Frameworks

arXiv

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arXiv 2021年

作者： Sadjadi, Mahdi Hagh, Varda F. Kang, Mingyu Sitharam, Meera Connelly, Robert Gortler, Steven J. Theran, Louis Holmes-Cerfon, Miranda Thorpe, M.F. Department of Physics Arizona State University TempeAZ85287-1604 United States CISE department University of Florida GainesvilleFL32611-6120 United States Department of Mathematics Cornell University IthacaNY14853 United States School of Engineering and Applied Sciences Harvard University MA United States School of Mathematics and Statistics University of St Andrews St AndrewsKY16 9SS United Kingdom Courant Institute of Mathematical Sciences New York University NY United States

This paper studies the set of equivalent realizations of isostatic frameworks in two dimensions, and algorithms for finding all such realizations. We show that an isostatic framework has an even number of equivalent realizations that preserve edge lengths and connectivity. We enumerate the complete set of equivalent realizations for a toy framework with pinned boundary in two dimensions and study the impact of boundary length on the emergence of these realizations. To ameliorate the computational complexity of finding a solution to a large multivariate quadratic system corresponding to the constraints;alternative methods - based on constraint reduction and distance-based covering map or Cayley parameterization of the search space - are presented. The application of these methods is studied on atomic clusters, a model two-dimensional glasses and jamming. © 2021, CC BY.

关键词： Toys

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Modern approaches to predicting vaccine hesitancy: A scoping review

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Vaccine: X 2025年 24卷

作者： Gandhi, Keshav Alahmadi, Sami Hanneke, Rosie Gutfraind, Alexander Department of Mathematics Statistics and Computer Science University of Illinois at Chicago 851 S. Morgan St Chicago 60607 IL United States Georgetown University School of Medicine 3900 Reservoir Rd. N.W Washington 20007 DC United States Library of the Health Sciences University of Illinois at Chicago 1750 W. Polk St Chicago 60612 IL United States The Program for Experimental & Theoretical Modeling Division of Hepatology Department of Medicine Stritch School of Medicine Loyola University Chicago 2160 S. First Ave. Maywood IL United States 60153 Division of Epidemiology and Biostatistics School of Public Health University of Illinois at Chicago 1603 W. Taylor St. Chicago 60612 IL United States

Introduction: Motivated by the disproportionate burden of infectious diseases on vulnerable populations and the risk of future pandemics, we conducted a scoping review to analyze the state of the literature about “vaccine uptake indices,” defined as models that predict vaccination rates by geospatial area. We analyzed novel vaccine uptake indices created in response to the recent COVID-19 pandemic. The aim of this scoping review is to survey the state of the literature regarding vaccine uptake indices relating to COVID-19 and other infectious diseases. Methods: We followed Arksey and O'Malley's scoping review framework and reported results according to PRISMA-ScR guidelines. Searches used controlled vocabulary and keywords across four conceptual domains: quantitative indices, geospatial analysis, infectious disease (COVID-19), and vaccination or vulnerability outcomes. Our systematic search strategy identifies relevant articles from the databases Embase, PubMed, and Web of Science with title and abstract screening, full-text review, and data extraction. Results: Database searches resulted in 3615 potential articles, of which 229 reports were included. Fifteen studies (7 %) were determined to be methodologically advanced vaccine uptake indices that had at least three of the following characteristics: the use of individual- and population-level predictor variables (100 [44 %]), geo-spatiotemporal analysis (58 [25 %]), data usage agnostic to vaccine specificity (50 [22 %]), or sociobehavioral frameworks of health (such as the Health Belief Model and Theory of Planned Behavior) (30 [13 %]). Conclusion: This scoping review offers suggestions for future research of next-generation vaccine uptake indices before use in vaccination campaigns of recurring or novel infectious diseases. Areas to pursue include utilizing individual-level data about vaccination behaviors in conjunction with administrative data, solving the challenge of implementing small-area spatiotemporal analy

关键词： COVID-19 Health equity Spatio-temporal analysis Vaccination hesitancy Vaccines

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

SSRN

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SSRN 2022年

作者： Cerna, Fernando Vladimir Naderi, Ehsan Marzband, Mousa Contreras, Javier Coêlho, John K. Fantesia, Maurício P. Department of Electrical Engineering Federal University of Roraima Boa Vista69310-000 Brazil Postgraduate Program in Natural Resources Research Area in Management and Dynamics of Natural Resources – Renewable Energies Federal University of Roraima Av. Nova Iorque – Aeroporto Boa Vista69310-000 Brazil School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL62901 United States Department of Mathematics Physics and Electrical Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Industrial Engineering University of Castilla-La Mancha Ciudad Real13071 Spain

The load factor (LF) of an electricity network (EN) is an indicator related to the efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers together with the operation of distributed resources (DREs), such as renewable sources (RSs) and capacitor banks (CBs) represents a promising alternative. To address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DREs in the EN. Operational constraints related to the performance of EN, RSs, and CBs are considered. Uncertainties in the habitual demand profiles and solar radiation incidence during the day are represent through simulation algorithms. In order to evaluate our proposal, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring the efficient EN operation. © 2022, The Authors. All rights reserved.

关键词： Integer programming

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Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

SSRN

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SSRN 2022年

关键词： Integer programming

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