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A deterministic model of COVID-19 with differential infectivity and vaccination booster

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Decision Analytics Journal 2024年 10卷

作者： Tchoumi, Stephane Y. Schwartz, Elissa J. Tchuenche, Jean M. Department of Mathematics and Computer Sciences ENSAI University of NGaoundéré PO Box 455 Ngaoundéré Cameroon Department of Mathematics and Applied Mathematics University of Pretoria South Africa Department of Mathematics & Statistics and School of Biological Sciences Washington State University PO Box 643113 Pullman 99164-3113 WA United States School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa School of Computational and Communication Sciences and Engineering Nelson Mandela African Institution of Science and Technology Arusha Tanzania

Vaccine boosters have been recommended to mitigate the spread of the coronavirus disease 2019 (COVID-19) pandemic. A mathematical model with three vaccine doses and susceptibility is formulated. The model is calibrated using the cumulative number of hospitalized cases from Alberta, Canada. Estimated values from the fitting are used to explore the potential impact of the booster doses to mitigate the spread of COVID-19. Sensitivity analysis on initial disease transmission shows that the most sensitive parameters are the contact rate, the vaccine efficacy, the proportion of exposed individuals moving into the symptomatic and asymptomatic classes, and the recovery rate from asymptomatic infection. Simulation results support the positive population-level impact of the second and third COVID-19 vaccine boosters to reduce the number of infections and hospitalizations. Public health policy and decision-makers should continue advocating and encouraging people to get booster doses. As the end of the pandemic is in sight, there should be no complacency before it resolves. © 2023 The Author(s)

关键词： Basic reproduction number COVID-19 Non-linear mathematical model Sensitivity analysis Simulation Vaccination

Nanoparticle aggregation effect on nonlinear convective nanofluid flow over a stretched surface with linear and exponential heat source/sink

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International Journal of Thermofluids 2023年 19卷

作者： Rawat, Sawan Kumar Yaseen, Moh Shafiq, Anum Kumar, Manoj Al-Mdallal, Qasem M. Department of Mathematics Graphic Era Deemed to be University Uttarakhand Dehradun248 002 India Department of Applied Science Meerut Institute of Engineering and Technology Uttar Pradesh Meerut250 005 India School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing China Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and Technology Nanjing210044 China Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Uttarakhand Pantnagar263 145 India Department of Mathematical Sciences College of Science UAE University Al-AinP.O. Box 17551 United Arab Emirates

A nanofluid flow (TiO2/Ethylene Glycol) over a stretched surface caused by the considerable nonlinear convection is investigated with the nanoparticle aggregation effect in the presence of quadratic thermal radiation. The modified Maxwell model and Bruggeman model are utilized, which depict the nanoparticle aggregation effect. In addition, the system employs two separate variable heat sources i.e., linear heat source and exponential heat source. The similarity transformations are utilized to convert the basic governing PDEs to ODEs. The "bvp4c function" in MATLAB is used to numerically handle the resultant nonlinear system. The impact of the involved parameters is explored on the non-dimensional fields of velocity boundary layer patterns, velocity, streamlines, temperature, and heat transfer rate. Graphs compare and display the control of pertinent parameters on the distinct flow properties. A comparison of the behavior of the solution profiles with and without the influence of nanoparticle aggregation is illustrated. The nanoparticle aggregates significantly enhance the velocity and heat transmission mechanism. Furthermore, the influence of varied heat sources (linear and exponential) aids in growing the thermal boundary layer. Nanofluid flow with nanoparticle aggregation has a 5.36% higher Nusselt number value in comparison to flow without aggregation effect when the volume fraction of nanoparticles is varied from 0 to 0.1. The results of the study will be helpful in the fields where applications of a continuously stretching/shrinking surface are used like in many engineering processes and industries, glass-fiber production, aerodynamic extrusion of plastic sheets, and manufacturing of paper and rubber sheets. © 2023 The Author(s)

关键词： Titanium dioxide

Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis

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arXiv 2021年

作者： Huang, Junyi Strome, Maxwell Benjamin Jenkins, Ian Williams, Parker Feng, Bo Wang, Yaning Wang, Roman Bagri, Vaibhav Cheng, Newman Drori, Iddo Department of Mathematics Columbia University United States Department of Computer Science Columbia University United States Department of Applied Physics and Math Columbia University United States Department of Electrical Engineering Columbia University United States Department of Electrical Engineering and Computer Science MIT United States

Kinship verification is the task of determining whether a parent-child, sibling, or grandparent-grandchild relationship exists between two people and is important in social media applications, forensic investigations, finding missing children, and reuniting families. We demonstrate high quality kinship verification by participating in the 2021 Recognizing Families in the Wild challenge which provides the largest publicly available dataset in the field. Our approach is among the top 3 winning entries in the competition. We ensemble models written by both human experts and a foundation model, OpenAI Codex, trained on text and code. We use Codex to generate model variants, and also demonstrate its ability to generate entire running programs for kinship verification tasks of specific relationships. © 2021, CC BY.

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

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A BCLC Staging System for Hepatocellular Carcinoma using Swi...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

AI-driven Gene Expression Analysis on HPC Platforms: Challenges and Solutions

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AI-driven Gene Expression Analysis on HPC Platforms: Challen...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： V.S Anusuya Devi N. Sreevani Amandeep Nagpal Susmita Kalyani K.Pushpa Rani Ali Albawi Departmento of Applied Sciences New Horizon College of Engineering Bangalore India Department of CSE(Cyber Security) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

HPC technology’s rapid rise has simplified gene expression research. A study proposes AI-GEM, which stands for “AI-Driven Gene Expression on High-Performance Computing Platforms.” We devised an approach to circumvent the issues associated with gene expression studies. We provide a full gene expression research strategy to improve accuracy, scalability, and comprehension. AI-GEM examined six methods: GEA-HPC, Expression AI, Genomic Solver, HPC-GENE, Bio AI-Express, and Gene Xcel. All approaches were tested. The findings suggest that AI-GEM outperforms other approaches. Our new approach averages 10.8 seconds, whereas the previous one took 45.7 seconds. The previous way is faster. AI-GEM is 95.2% accurate. This approach is far more accurate than the standard $\mathbf{7 1. 2 \%}$. This differs greatly from proper procedures. AI-GEM has far more potential than prior approaches, which could only handle 20,850 genes. AI-GEM outperforms other gene expression approaches in interpretability ($\mathbf{9. 2} \mathbf{v s}$. 4.4). One other reason: AI-GEM works better than conventional approaches. The AI-GEM system achieves $\mathbf{9 3 \%}$ client privacy, whereas traditional approaches obtain 58%. AI-GEM uses computer resources well, scoring $\mathbf{8 5 \%}$ vs. $\mathbf{6 6 \%}$ for other approaches. The final preprocessing time for AI-GEM is under 3.2 seconds. The previous best was 13.4 seconds, a significant improvement. AI-GEM is a novel gene expression data analysis tool for powerful computers. AI-GEM is a novel gene expression data analysis tool that is faster, more accurate, more scalable, simpler to comprehend, better at preserving privacy, more efficient in using resources, and reduces difficulties in pre-processing chores. This technology might aid biology and genetics in many ways.

关键词： Privacy Accuracy Data analysis Scalability Genomics Transforms Gene expression Reliability Bioinformatics Standards

A Fitness Movement Evaluation System Using Deep Learning

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A Fitness Movement Evaluation System Using Deep Learning

International Conference on Knowledge and Systems engineering (KSE)

作者： Chin-Chih Chang Chi-Hung Wei Hao-Wei Wu Sean Hsiao Department of Computer Science and Information Engineering Chung Hua University Hsinchu City Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

This paper proposes a fitness movement evaluation system using deep learning. The system uses a deep convolutional neural network (CNN) to extract features from pictures of fitness movements. The features are then used to classify the movements into different categories. The system is evaluated on a dataset of pictures of fitness movements. The results show that the system can accurately classify the movements into different categories. The system is designed to provide feedback to users on their fitness movements. The proposed system is a valuable tool for fitness enthusiasts. The main contribution of this paper is to propose a way to give users a score for their fitness movement. It can help users improve their fitness and track their progress over time. The system is also a valuable tool for fitness professionals. It can help professionals develop new fitness programs and provide feedback to their clients.

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Exploring the role of parameters in variational quantum algorithms

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arXiv 2022年

作者： Anand, Abhinav Alperin-Lea, Sumner Choquette, Alexandre Aspuru-Guzik, Alán Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Chemical Engineering and Applied Chemistry University of Toronto Canada Department of Materials Science and Engineering University of Toronto Canada Toronto Canada

In this work, we introduce a quantum-control-inspired method for the characterization of variational quantum circuits using the rank of the dynamical Lie algebra associated with the hermitian generator(s) of the individual layers. Layer-based architectures in variational algorithms for the calculation of ground-state energies of physical systems are taken as the focus of this exploration. A promising connection is found between the Lie rank, the accuracy of calculated energies, and the requisite depth to attain target states via a given circuit architecture, even when using a lot of parameters which is appreciably below the number of separate terms in the generators. As the cost of calculating the dynamical Lie rank via an iterative process grows exponentially with the number of qubits in the circuit and therefore becomes prohibitive quickly, reliable approximations thereto are desirable. The rapidity of the increase of the dynamical Lie rank in the first few iterations of the calculation is found to be a viable (lower bound) proxy for the full calculation, balancing accuracy and computational expense. We, therefore, propose the dynamical Lie rank and proxies thereof as a useful design metric for layer-structured quantum circuits in variational algorithms. © 2022, CC BY.

关键词： Algebra

A Review of Gaps between Web 4.0 and Web 3.0 Intelligent Network Infrastructure

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arXiv 2023年

作者： Zhou, Zihan Li, Zihao Zhang, Xiaoshuai Sun, Yunqing Xu, Hao University of Glasgow GlasgowG12 8QQ United Kingdom Department of Computer Science McCormick School of Engineering and Applied Science Northwestern University EvanstonIL United States Shanghai Engineering Research Center for Blockchain Applications and Services Huawei Technologies Shanghai China

World Wide Web is speeding up its pace into an intelligent and decentralized ecosystem, as seen in the campaign of Web 3.0 and forthcoming Web 4.0. Marked by the Europe Commission's latest mention of Web 4.0, a race towards strategic Web 4.0 success has started. Web 4.0 is committed to bringing the next technological transition with an open, secure, trustworthy fairness and digital ecosystem for individuals and businesses in private and public sectors. Despite overlapping scopes and objectives of Web 3.0 and Web 4.0 from academic and industrial perspectives, there are distinct and definitive features and gaps for the next generation of WWW. In this review, a brief introduction to WWW development unravels the entangled but consistent requirement of a more vivid web experience, enhancing human-centric experience in both societal and technical aspects. Moreover, the review brings a decentralized intelligence prospect of view on native AI entities for Web 4.0, envisioning sustainable, autonomous and decentralized AI services for the entire Web 4.0 environment, powering a self-sustainable Decentralized Physical and Software Infrastructure for Computing Force Network, Semantic Network, Virtual/Mixed Reality, and Privacy-preserving content presumption. The review aims to reveal that Web 4.0 offers native intelligence with focused thinking on utilizing decentralized physical infrastructure, in addition to sole requirements on decentralization, bridging the gap between Web 4.0 and Web 3.0 advances with the latest future-shaping blockchain-enabled computing and network routing protocols. © 2023, CC BY.

关键词： Blockchain

Neural FIM for learning Fisher information metrics from point cloud data

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arXiv 2023年

作者： Fasina, Oluwadamilola Huguet, Guillaume Tong, Alexander Zhang, Yanlei Wolf, Guy Nickel, Maximilian Adelstein, Ian Krishnaswamy, Smita Applied Math Program Yale University New HavenCT United States Mila - Quebec AI Institute MontrealQC Canada Department of Mathematics and Statistics Université de Montréal MontrealQC Canada Department of Computer Science and Operations Research Université de Montréal MontrealQC Canada FAIR Meta AI United States Department of Math Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings