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Where Do CABs Exist? Verification of a specific region containing concave Actin Bundles (CABs) in a 3-Dimensional confocal image

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In Silico Biology 2023年第1-2期15卷 1-10页

作者： Park, Doyoung Department of Mathematics Computer Science Information State University of New York at Old Westbury Old Westbury NY United States

CABs (Concave Actin Bundles) are oriented against the scaffold transversally in a manner different from traditional longitudinal F-actin bundles. CABs are present in a specific area, and do not exist in random areas. Biologically, CABs are developed to attach cells to fibers firmly so that CABs are found near cells. Based on this knowledge, we closely examined 3D confocal microcopy images containing fiber scaffolds, actin, and cells. Then, we assumed that the areas containing high values of compactness of fiber, compactness of actin, and density of cells would have many numbers of CABs. In this research, we wanted to prove this assumption. We first incorporated a two-point correlation function to define a measure of compactness. Then, we used the Bayes' theorem to prove the above assumption. As the assumption, our results verified that CABs exist in an area of high compactness of a fiber network, high compactness of actin distribution, and high density of cells. Thus, we concluded that CABs are developed to attach cells to a fibrillar scaffold firmly. This finding may be further verified mathematically in future studies. © 2023 - The authors.

关键词： bayes' theorem CABs concave actin bundles fiber network two-point correlation function

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Radical Sylvester-Gallai Theorem for Tuples of Quadratics 23

Radical Sylvester-Gallai Theorem for Tuples of Quadratics

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38th Computational Complexity Conference, CCC 2023

作者： Garg, Abhibhav Oliveira, Rafael Peleg, Shir Sengupta, Akash Kumar Cheriton School of Computer Science University of Waterloo Canada Blavatnik School of Computer Science Tel Aviv University Israel Department of Mathematics Columbia University New YorkNY United States

ISBN: (纸本)9783959772822

We prove a higher codimensional radical Sylvester-Gallai type theorem for quadratic polynomials, simultaneously generalizing [20, 36]. Hansen's theorem is a high-dimensional version of the classical Sylvester-Gallai theorem in which the incidence condition is given by high-dimensional flats instead of lines. We generalize Hansen's theorem to the setting of quadratic forms in a polynomial ring, where the incidence condition is given by radical membership in a high-codimensional ideal. Our main theorem is also a generalization of the quadratic Sylvester-Gallai Theorem of [36]. Our work is the first to prove a radical Sylvester-Gallai type theorem for arbitrary codimension k ≥ 2, whereas previous works [36, 29, 30, 28] considered the case of codimension 2 ideals. Our techniques combine algebraic geometric and combinatorial arguments. A key ingredient is a structural result for ideals generated by a constant number of quadratics, showing that such ideals must be radical whenever the quadratic forms are far apart. Using the wide algebras defined in [28], combined with results about integral ring extensions and dimension theory, we develop new techniques for studying such ideals generated by quadratic forms. One advantage of our approach is that it does not need the finer classification theorems for codimension 2 complete intersection of quadratics proved in [36, 16]. © Abhibhav Garg, Rafael Oliveira, Shir Peleg, and Akash Kumar Sengupta;licensed under Creative Commons License CC-BY 4.0.

关键词： Polynomials

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Reliance on Episodic vs. Procedural Systems in Decision-Making Depends on Individual Differences in Their Relative Neural Efficiency

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Computational Brain and Behavior 2024年第3期7卷 420-436页

作者： Yang, Yuxue C. Sibert, Catherine Stocco, Andrea Department of Psychology University of Washington Seattle 98195 WA United States Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands

Experiential decision-making can be explained as a result of either memory-based or reinforcement-based processes. Here, for the first time, we show that individual preferences between a memory-based and a reinforcement-based strategy, even when the two are functionally equivalent in terms of expected payoff, are adaptively shaped by individual differences in resting-state brain connectivity between the corresponding brain regions. Using computational cognitive models to identify which mechanism was most likely used by each participant, we found that individuals with comparatively stronger connectivity between memory regions prefer a memory-based strategy, while individuals with comparatively stronger connectivity between sensorimotor and habit-formation regions preferentially rely on a reinforcement-based strategy. These results suggest that human decision-making is adaptive and sensitive to the neural costs associated with different strategies. © Society for Mathematical Psychology 2024.

关键词： Computational models Decision-making Declarative memory Episodic memory fMRI Functional connectivity Human connectome project Individual differences Procedural memory Reinforcement learning

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Dependence of thermodynamic quantities at freeze-out on pseudorapidity and collision energy in p-p collisions at LHC energies

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Chinese Physics C 2024年第10期48卷 235-246页

作者： Murad Badshah Yahia A.H.Obaidat Haifa I.Alrebdi M.Waqas Muhammad Ajaz Refka Ghodhbani Department of Physics Abdul Wali Khan University MardanMardan 23200Pakistan Department of Physics College of ScienceUniversity of BishaBisha 61922Saudi Arabia Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia School of Mathematics Physics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyan 442002China Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafhaSaudi Arabia

The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies(√s)of 0.9 TeV and 2.36 TeV,as measured by the CMS detector at the Large Hadron Collider(LHC),have been analyzed within various pseudorapidity classes utilizing the thermodynamically consistent Tsallis *** fitting procedure resulted in the key parameters,namely,effective temperature(T),non-extensivity parameter(q),and kinetic freezeout volume(V).Additionally,the mean transverse momentum()and initial temperature(T_(i))of the particle source are determined through the fit function and string percolation method,*** alternative method is employed to calculate the kinetic freezeout temperature(T_(0))and transverse flow velocity(β_(T))from ***,thermodynamic quantities at the freezeout,including energy density(ε),particle density(n),entropy density(s),pressure(P),and squared speed of sound(C_(s)^(2)),are computed using the extracted T and *** is also observed that,with a decrease in pseudorapidity,all thermodynamic quantities except V and q *** trend is attributed to greater energy transfer along the mid pseudorapidity.q increases towards higher values of pseudorapidity,indicating that particles close to the beam axis are far from ***,V remains nearly independent of *** excitation function of these parameters(q)shows a direct(inverse)correlation with collision *** ε,n,s,and P show a strong dependence on collision energies at low *** verification of the thermodynamic inequality ε≥3P suggests the formation of a highly dense droplet-like Quark-Gluon Plasma(QGP).Additionally,the inequality T_(i)>T>T_(0)is explicitly confirmed,aligning with the evolution of the produced fireball.

关键词： charged hadrons tsallis statistics effective temperature non-extensivity parameter pseudorapidity kinetic freeze-out stage multiple freeze-out scenario QGP QCD matter squared speed of sound

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IoT-enabled monitoring and comparative analysis of machine learning techniques for detection of plant diseases in marigolds

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International Journal of Information Technology (Singapore) 2024年 1-8页

作者： Jomsri, Pijitra Prangchumpol, Dulyawit Poonsilp, Kittiya Department of Information Technology Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand Department of Computer Science Faculty of Science and Technology Suan Sunandha Rajabhat University 1 U-Thong Nok Road Dusit Bangkok Thailand

Ornamental flower cultivation is highly popular and a critical career that creates income for the farmers. Among these marigold flower is a top-selling flowering plant across most of the countries. Further, this plant has many insect-repellent properties. Despite farming these plants economic benefits, multiple risks are associated with cultivating them. Plant disease plays a critical role which reduces the quality and quantity of flower production flowers and contributes primarily to the loss of production or exports. Manual disease detection strategies are expensive and highly prone to error. Thus, multiple early detection of plant diseases by the Internet of Things (IoT) and Artificial Intelligence (AI) technologies are introduced to reduce the burden of the disease. Most of them are complex and cost high. In conjunction with these, a sensor model suitable for collecting data on marigold leaf diseases is presented in this research. A Machine Learning (ML) technique based on the latest Neural Network (NN) called EFFICIENTNET is employed to isolate the diseased plants from the healthier plants. Experiments conducted and comparisons with existing K-Nearest Neighbor (KNN) techniques showed that the EFFICIENTNET-B0 technique had shown remarkable performance with 97.70% accuracy. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： EfficientNet-B0 and K-Nearest Neighbor algorithm Images Analysis Machine Learning Marigold Leaf Disease

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Hyperbolic Hopfield Neural Network for Enhanced Solar Power Generation Forecasting 4

Hyperbolic Hopfield Neural Network for Enhanced Solar Power ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Naveen, K.N. Sukumar, P. Yamini, C. Raghapriya, N.R. Dwivedi, Vijay Kumar Department of Physics Bihar India Dayananda sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Department of Computer Science and Engineering Erode India Psna College of Engineering and Technology Department of Mathematics Dindigul India Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India Vishwavidyalaya Engineering College Department of Mathematics Surguja India

ISBN: (纸本)9798350352931

Solar power is growing more essential for renewable energy, but measuring its output is difficult due to irradiance, temperature, and weather fluctuations. Planning and controlling energy distribution requires accurate short-term estimates. An innovative Hyperbolic Hopfield Neural Network (HHNN) model for short-term solar power prediction is presented in this paper. The procedure includes preprocessing, feature extraction, and model training. Remove linearly dependent characteristics and filter nighttime data during preprocessing. The HHNN model is trained after feature selection is optimised using PCA and wavelet decomposition. The new HHNN model surpassed the HNN and DNN models in solar power forecasting with an average accuracy of 92.41%.HHNN is a solid method for calculating solar power output. This model has improved solar power management systems by increasing projected accuracy. © 2024 IEEE.

关键词： hyperbolic hopfield neural network (HHNN) principal component analysis (PCA) solar power generation forecasting

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VR Training for Laboratory Environments 30

VR Training for Laboratory Environments

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Wermann, Josef Pohn, Birgit University of Applied Sciences Technikum Wien Faculty Computer Science & Applied Mathematics Austria University of Applied Sciences Tech-nikum Wien Department of Computer Science Austria

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

VR apps in the categories of gaming, leisure, social, fitness, work and education are becoming increasingly popular. The aim of this paper is to determine the potential for increasing efficiency and conserving resources in laboratory processes. For this purpose, literature research was conducted on the one hand, and on the other hand, a virtual simulation environment in the field of cell culture was created within a Laminar Flow Hood (Flow) using the Unity development environment and the Oculus Quest 2 VR goggles. Outstanding features of the app are that, in addition to the possibility of being used via cable using a PC (PCVR), it can also be operated wireless as an Android standalone package, and that it can be used with controllers as well as with hand tracking, i.e., freehand. The Laminar Flow application was extensively tested on a group of 10 employees and students of the department of Biomedical Engineering. The data obtained from the app, as well as questionnaires completed by the test persons, show that a usability-friendly application can be used to train workflows efficiently and, in a resource saving manner. Furthermore, the more often the workflow has been worked through by a person, a greater improvement in the workflow was achieved. In addition, a strong acceptance and high commitment of the users in the technical environment can be seen. Regarding comparable studies and although the test group is subject to some limitations the enormous potential that IVR offers to support location-based training sessions in companies and especially in laboratories and university facilities can be seen. In view of the decreasing prices, the technical advancement of VR devices and the paradigm shift of work and education towards increased home office driven by COVID-19, intensified future research in the field of IVR can be justified in combination with these positive results. © 2022 University of Split, FESB.

关键词： Laboratories

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Improving the Performance of K-Means Clustering to Reduce Repetitive Work by Addressing the Gap in Findings 27

Improving the Performance of K-Means Clustering to Reduce Re...

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27th International computer science and Engineering Conference, ICSEC 2023

作者： Chayangkoon, Narongsak Kasemtaweechok, Chatchai Suratthani Rajabhat University Faculty of Science and Technology Department of Digital Technology Suratthani Thailand Kasetsart University Faculty of Science at Sriracha Department of Computer Science and Informatics Sriracha Campus Chonburi Thailand

ISBN: (纸本)9798350342109

The k-means clustering algorithm calculates the distances between data points within each cluster and determines new centroids for each cluster until the data points remain unchanged within their respective groups. This process affects the iteration of the k-means algorithm, leading to an increase in computation time. Consequently, the overall execution time of the algorithm is prolonged. This research presents a process that utilizes the farthest first and canopy algorithms to reduce the number of iterations in the k-means clustering. This improvement focuses on enhancing the centroid initialization method for each cluster. The research study utilized ten datasets from the UCI machine learning repository to test the effectiveness of improving the k-means clustering algorithm. It compares the number of iterations and the sum of squares error as performance metrics. The results showed that the farthest first and canopy algorithms significantly reduce the number of iterations in the k-means clustering when compared to random centroid initialization. These research findings confirm the hypothesis that the method of centroid initialization for each cluster impacts the iteration process of k-means clustering. © 2023 IEEE.

关键词： K-means clustering

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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Building a Stronger Case: Combining Evidence and Law in Scenario-Based Bayesian Networks 3

Building a Stronger Case: Combining Evidence and Law in Scen...

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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024

作者： Van Leeuwen, Ludi Verbrugge, Rineke Verheij, Bart Renooij, Silja Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands Department of Information and Computing Sciences Utrecht University Netherlands

ISBN: (纸本)9781643685229

Existing approaches to modelling legal cases in Bayesian networks focus either on correctly representing an empirical probabilistic model of evidence traces, or on modeling alternative scenarios that can explain what happened in a case. However, neither approach legally interprets, or qualifies, aspects of a scenario as a normative legal fact. Hence, the fact that a Bayesian network representing a scenario assigns a high posterior probability to a certain victim having been killed by a certain suspect, does not imply that that suspect is guilty of murder in the legal sense, because the events in the scenario cannot be qualified as legal facts. This paper proposes an architecture for concrete legal fact idioms that qualify events in a narrative Bayesian network. This bridges the gap between the real world and the normative legal world through so-called counts-as rules. By modeling the legal facts explicitly in the Bayesian network, we can show whether a narrative completes one or more legal fact idioms. This is demonstrated using a case study. The proposed architecture may help judges and lawyers decide on which narratives they should investigate further and which narratives are stronger than others with regard to both the evidence and the legal facts. © 2024 The Authors.

关键词： Bayesian networks

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