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arXiv 2024年

作者： Gili, Tommaso Avila, Bryant Pasquini, Luca Holodny, Andrei Phillips, David Boldi, Paolo Gabrielli, Andrea Caldarelli, Guido Zimmer, Manuel Makse, Hernán A. Networks Unit IMT Scuola Alti Studi Lucca Piazza San Francesco 15 Lucca55100- Italy CNR UoS Sapienza Rome00185 Italy Levich Institute and Physics Department City College of New York New YorkNY10031 United States Neuroradiology Service Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Neuroradiology Unit NESMOS Department Sant’Andrea Hospital La Sapienza University Rome00189 Italy Department of Neurology and Neuroscience Weill Medical College of Cornell University New YorkNY10021 United States Department of Radiology Weill Medical College of Cornell University New YorkNY10065 United States Division of Mathematics Computer and Information Systems Office of Naval Research ArlingtonVA22217 United States Department of Mechanical Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Computer Science University of Milan Milano Italy Via Panisperna 89A Rome00184 Italy Dipartimento di Ingegneria Civile Informatica e delle Tecnologie Aeronautiche Università degli Studi ‘Roma Tre’ Via Vito Volterra 62 Rome00146 Italy Department of Molecular Science and Nanosystems and ECLT Ca Foscari University of Venice Venice30123 Italy London Institute for Mathematical Sciences Royal Institution 21 Albemarle St LondonW1S 4BS United Kingdom Campus-Vienna-Biocenter 1 Vienna1030 Austria

In his book’A Beautiful Question’, physicist Frank Wilczek argues that symmetry is’nature’s deep design,’ governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain’s network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain’s network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles. © 2024, CC BY.

关键词： Synchronization

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ML-based intelligent management and prediction of heart disease

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AIP Conference Proceedings 2023年第1期2690卷

作者： Gnanaprakasam C. N. N. Arockia Rosy Vaishali Gajendra Shende N. B. Prakash P. V. Pramila S. Ramesh 1Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai - 600 119 Tamil Nadu India 2Department of Information Technology R.M.D. Engineering College Kavaraipettai Thiruvallurdistrict Tamil Nadu 601206 India 3Department of Information Science and Engineering East Point College of Engineering and Technology Avalahalli Bengaluru Karnataka 560049 India 4Department of Electrical and Electronics Engineering National Engineering College K.R.Nagar Thoothukudi District Kovilpatti-628503 Tamil Nadu India 5Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu - 600077 India 6Department of Electronics and Communication Engineering St. Mother Theresa Engineering College Vagaikulam Thoothukudi Dist. – 628102 Tamil Nadu India

Heart disease or Coronary illness, leads to many kinds of disease and deaths beyond a couple of many years. Dataset cleaning is simply an incorporated method for tackling gigantic data in the health care space. This examination paper presents different traits identified with coronary illness and the model on-premise of administered learning calculations as K-closest neighbour (KNN) and Support Vector Machine. The results depict that the most elevated exactness score is accomplished with SVM. Utilizing a profound learning approach, 96.07 % exactness was gotten. computerized reasoning strategies have been generally utilized in clinical choice emotionally supportive networks for expectation and finding of different infections with great exactness. These characterizing strategies are extremely successful in planning clinical emotionally supportive networks because of their capacity to get covered up examples and connections in clinical information given by clinical experts. Perhaps the main utilization of such a system is in the analysis of heart sicknesses since it is one of the main sources of passings from one side of the planet to the other. A framework dependent on such danger variables would assist clinical experts with welling it would give patients an admonition about the plausible presence of coronary illness even before the patient visits an emergency clinic or goes for exorbitant clinical check-ups. Thus, this paper presents a procedure for the expectation of coronary illness utilizing significant danger factors with the assistance of various Classifying Algorithms. In this paper, the accuracy of ML Algorithms like SVM and K-Nearest Neighbour is been compared and the results are depicted.

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Optimal locally repairable codes: An improved bound and constructions

arXiv

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arXiv 2020年

作者： Cai, Han Fan, Cuiling Miao, Ying Schwartz, Moshe Tang, Xiaohu Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva8410501 Israel School of Mathematics Southwest Jiaotong University Chengdu610031 China Faculty of Engineering Information and Systems University of Tsukuba Tennodai 1-1-1 Tsukuba305-8573 Japan School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China

We study the Singleton-type bound that provides an upper limit on the minimum distance of locally repairable codes. We present an improved bound by carefully analyzing the combinatorial structure of the repair sets. Thus, we show the previous bound is unachievable for certain parameters. We then also provide explicit constructions of optimal codes that show that for certain parameters the new bound is sharp. Additionally, as a byproduct, some previously known codes are shown to attain the new bound and are thus proved to be optimal. Copyright © 2020, The Authors. All rights reserved.

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Rule induction based on rough sets from possibilistic data tables 7th

Rule induction based on rough sets from possibilistic data t...

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7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019

作者： Nakata, Michinori Sakai, Hiroshi Faculty of Management and Information Science Josai International University 1 Gumyo ToganeChiba283-8555 Japan Department of Mathematics and Computer Aided Sciences Faculty of Engineering Kyushu Institute of Technology Tobata Kitakyushu804-8550 Japan

ISBN: (纸本)9783030148140

How to induce rules from data tables containing possibilistic information is described under using rough sets based on possible world semantics. A piece of possibilistic information is expressed in a normal and discrete possibility distribution. Under a degree of possibility, the incomplete data table is derived from a possibilistic data table. Rough sets and rules are derived from incomplete data tables. The rough sets and rules obtained under every degree of possibility are aggregated from the viewpoints of certainty and possibility. As a result, rough sets consist of objects with a degree expressed in an interval and an object also supports rules with a degree expressed in an interval value. Furthermore, a criterion is introduced to judge whether or not an object is regarded as validly supporting rules. © Springer Nature Switzerland AG 2019.

关键词： Rough set theory

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Numerical investigations of MHD Casson nanofluid flow over a wedge through porous medium

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AIP Conference Proceedings 2021年第1期2375卷

作者： M. Radha Madhavi W. Sridhar Puvvada Nagesh 1Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram AP India-522502 2Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India-522502

The aim of current Paper is to examine the effect/result of ‘heat source’ parameter over a wedge through porous-medium for electrically-conducting fluid flow for a magneto hydrodynamic Casson nanofluid with activation energy. The influence/impact of ‘second law’ analysis for electrically conducting fluid(s) with chemical-reaction also investigated. Dimension-less ordinary differential equations(ODE) are obtained/derived from the highly non- linear PDEs by using similarity transformations. The ‘Runge- Kutta –Fehlberg with shooting technique’ is utilized to solve remodelled ODEs numerically. The effect(s) of the ‘physical parameter’ on velocity, temperature and concentration are extensively discussed through several plots. Numerical result(s) for Nusselt number, skin friction coefficient & ‘Sherwood number’ for various parameter(s) are computed.

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Integration of smart grid with Industry 5.0: Applications, challenges and solutions

Measurement: Energy

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Measurement: Energy 2025年 5卷

作者： Sunawar khan Tehseen Mazhar Tariq Shahzad Muhammad Amir khan Ateeq Ur Rehman Habib Hamam Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar 62300 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia School of Computing Gachon University Seongnam 13120 Republic of Korea Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Introduction In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two. Objectives In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage. Method ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0. Findings and discussion In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures. Conclusion Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.

关键词： Smart grid Energy efficiency Industry 5.0 Haman-machine collaboration Advanced metering infrastructure (AMI) Real-time data analytics

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Scheduling task and offloading process based on KNN and NB algorithm on cloud

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AIP Conference Proceedings 2021年第1期2387卷

作者： J. Premalatha K. Sathya Vani Rajasekar S. E. Dharani S. Harivarshini R. Keerthana 1Department of Information Technology Kongu Engineering College Erode Tamilnadu India - 638060 2Department of Computer Technology-UG Kongu Engineering College Erode Tamilnadu India - 638060 3Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India - 638060

Cloud Computing is quickly developing and a lot more cloud suppliers are arising. This project says how the incoming tasks are scheduled in the cloud. In this paper, presenting a classifier algorithmic technique that integrates the K-Nearest Neighbor(KNN) algorithm with the Naive Bayes algorithm. Calculation offloading innovation offers a feasible arrangement by offloading some calculation serious assignments of the K-Nearest Neighbor algorithm and the Naive Bayes algorithm to edges or distant mists that are furnished with adequate assets. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this significant issue, we first respect the average response time of multi-task parallel scheduling as our streamlining objective. Finally, the K-Nearest Neighbor and Naive Bayes algorithm based applications are proposed to solve the problem. Offloading a task means how the task is scheduled in Virtual Machine, first, the task is separated into types(small, medium, large and extra-large) after that it sees which Virtual Machine is available to load the task. Suppose, if the incoming task is too-large then we have to offload it by separating the task into subtask as 6 operations (split, combine, merge, demerge, promote and demote). To avoid the burden in the Virtual Machine, We analyze the above operations by using the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm and then scheduled in the Virtual Machine. We want to show that the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm shows better performance than Deep Neural Network(DNN).

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Online food court payment processing using blockchain

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AIP Conference Proceedings 2021年第1期2387卷

作者： J. Premalatha K. Sathya Vani Rajasekar S. Nishanth S. Ramesh Kumar M. Praveena 1Department of Information Technology Kongu Engineering College Erode Tamilnadu India - 638060 2Department of Computer Technology UG Kongu Engineering College Erode Tamilnadu India - 638060 3Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India - 638060

It is a web-based application designed commonly for use in Food & beverage based enterprises. These applications tackle the issues of Manual Labour cost in the food court bringing about a rapid expansion in the Overall business Model. It also provides the proprietor of the restaurant to administer and make modifications as per the stip-ulations in their restaurant. The consumer can order their food by accessing the application through their mobile browser in an optimized manner. The retailer can easily complete the order in time, as there will be a streamlined check-in for the availability of ingredients. In this paper, let's exemplify the overall description and generalized soft-ware requirements specification of the entire systematic structure designed for use in different food courts. Our appli-cation will impart the restaurant employees to manage their online cuisines and keep track of all the orders placed by their consumers. Furthermore, the Customer can make Payment using the Existing Payment Gateways as well as they can go for blockchain-based Payment Methodologies. Eventually, this idea is projected to reduce the overheads of Payment gateways and improvise Data security in a precise manner.

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Implementation of the algorithm for constructing homogeneous approximations of nonlinear control systems

arXiv

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arXiv 2021年

作者： Sklyar, Grigory Barkhayev, Pavel Ignatovich, Svetlana Rusakov, Viktor Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71-210 Poland Institute of Mathematics University of Szczecin Wielkopolska 15 Szczecin70-451 Poland Department of Mathematical Sciences Norwegian University of Science and Technology Høgskoleringen 1 Trondheim7491 Norway B.Verkin Institute for Low Temperature Physics and Engineering Academy of Sciences of Ukraine Nauki Ave. 47 Kharkiv61103 Ukraine Department of Applied Mathematics V. N. Karazin Kharkiv National University Svobody Sq. 4 Kharkiv61022 Ukraine

We present a "calculator" for constructing a homogeneous approximation of nonlinear control systems, which is based on the algebraic approach developed by the authors in their previous papers. This approach mainly uses linear algebraic and combinatorial tools, so, it is perfectly adapted to computer realization. We describe the algorithm and discuss its capabilities and limitations. We present its implementation as a web application and show by example how this app works. © 2021, CC BY.

关键词： Algebra

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