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arXiv 2024年

作者： Laeremans, Wout Segers, Midas Voorspoels, Aderik Carlon, Enrico Hooyberghs, Jef Soft Matter and Biological Physics Department of Applied Physics Institute for Complex Molecular Systems Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Soft Matter and Biophysics Unit KU Leuven Celestijnenlaan 200D Leuven3001 Belgium UHasselt Faculty of Sciences Data Science Institute Theory Lab Agoralaan Diepenbeek3590 Belgium

Coarse-grained models have emerged as valuable tools to simulate long DNA molecules while maintaining computational efficiency. These models aim at preserving interactions among coarse-grained variables in a manner that mirrors the underlying atomistic description. We explore here a method for testing coarse-grained vs. all-atom models using stiffness matrices in Fourier space (q-stiffnesses), which are particularly suited to probe DNA elasticity at different length scales. We focus on a class of coarse-grained rigid base DNA models known as cgDNA and its most recent version cgDNA+. Our analysis shows that while cgDNA+ follows closely the q-stiffnesses of the all-atom model, the original cgDNA shows some deviations for twist and bending variables which are rather strong in the q → 0 (long length scale) limit. The consequence is that while both cgDNA and cgDNA+ give a suitable description of local elastic behavior, the former misses some effects which manifest themselves at longer length scales. In particular, cgDNA performs poorly on the twist stiffness with a value much lower than expected for long DNA molecules. Conversely, the all-atom and cgDNA+ twist is strongly length scale dependent: DNA is torsionally soft at a few base pair distances, but becomes more rigid at distances of a few dozens base pairs. Our analysis shows that the bending persistence length in all-atom and cgDNA+ is somewhat overestimated. © 2024, CC BY.

关键词： Stiffness

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Interpreting Industrial IoT data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big data Monitoring Production Real-time systems

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia Computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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R and python benchmarking for geographical applications 20

R and python benchmarking for geographical applications

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20th International Multidisciplinary Scientific Geoconference: Informatics, Geoinformatics and Remote Sensing, SGEM 2020

作者： Verbeurgt, Jeffrey Stal, Cornelis de Sloover, Lars Deruyter, Greet de Wulf, Alain Ghent University Department of Geography 3D Data Acquisition Research Unit Belgium University College Ghent Center for Applied Data Science Belgium Ghent University Department of Geography CartoGIS Research Unit Belgium Ghent University Department of Civil Engineering Belgium

ISBN: (纸本)9786197603125

Two commonly used programming languages in geosciences are R and Python. Both languages come with inherent advantages and disadvantages. While Python is a general-purpose language with a readable syntax, R is built by statisticians and encompasses their specific language. In contrast with more nominative and static programming languages, like C++ or Java, both R and Python are heavily used in non-informatics disciplines, due to their relative ease of use. Notwithstanding the lower performance of both languages compared to the aforementioned languages, they allow fast prototyping and implementation of algorithms in research contexts. However, depending on the aim of the research, (geo-)scientists will frequently decide between using Python, favoring replicability and accessibility, or R, containing fast-programmable cutting-edge reporting tools. One of the main differences between both languages is that, for Python, with five libraries one can complete most research (Numpy, Pandas, Scipy, Scikit-learn, and Matplotlib), while for R, one should find the right package in the +12.000 available packages on CRAN. The availability of the many packages for R is at the same time one of its greatest strengths. In this research, three common analyses in geosciences are performed in both R and Python. The first analysis highlights the unsupervised classification of (Sentinel-2) satellite imagery. The second series of analysis concerns the transformation of planimetric coordinates from one coordinate reference system to another system. As a final analysis, a series of standard topographic parameters are derived for an equidistant digital elevation model extracted from ASTER- and SRTM- data (EU-DEM). Next to the quantitative benchmarking of the processing time for each analysis, the output of each calculation is investigated as a qualitative benchmark, with a focus on the comprehensibility of the numerical/character output and the visualizations. © 2020 International Multidiscipli

关键词： Python

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Intra-datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： Computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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Native spaces and generalization of Wu functions

arXiv

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arXiv 2023年

作者： Huang, Yixuan Wu, Zongmin Zhu, Shengxin School of Mathematics Beijing Normal University China Laboratory of Mathematics and Complex Systems Ministry of Education China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Shanghai Center for Mathematical Sciences Shanghai China School of Big Data and Statistics Anhui University Hefei China Research Center for Mathematics Advanced Institute of Natural Science Beijing Normal University Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

We prove that the native space of a Wu function is a dense subspace of a Sobolev space. An explicit characterization of the native spaces of Wu functions is given. Three definitions of Wu functions are introduced and proven to be equivalent. Based on these new equivalent definitions and the so called f-form tricks, we can generalize the Wu functions into the even-dimensional spaces R2k, while the original Wu functions are only defined in the odd-dimensional spaces R2k+1. Such functions in even-dimensional spaces are referred to as the ‘missing Wu functions’. Furthermore we can generalize the Wu functions into ‘fractional’-dimensional spaces. We call all these Wu functions the generalized Wu functions. The closed form of the generalized Wu functions are given in terms of hypergeometric functions. Finally we prove that the Wu functions and the missing Wu functions can be written as linear combinations of the generalized Wendland *** Codes 65J05, 33C90, 41A05, 41A30, 41A65, 41A63 Copyright © 2023, The Authors. All rights reserved.

关键词： Sobolev spaces

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A Novel Framework Predicting Anxiety in Chronic Disease Using Boosting Algorithm and Feature Selection Techniques 1

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Future of Information and Communication Conference, FICC 2024

作者： Qarmiche, N. Otmani, N. Tachfouti, N. Amara, B. Akasbi, N. Berrady, R. El Fakir, S. Laboratory of Artificial Intelligence Data Science and Emerging Systems National School of Applied Sciences Fez Sidi Mohamed Ben Abdellah University Fez Morocco Biostatistics and Informatics Unit Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Pneumology Department Hassan II University Hospital Fez Morocco Rheumatology Department Hassan II University Hospital Fez Morocco Department of Gastro Hepato-Enterology - CHU Hassan II Fez Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

Introduction: Chronic diseases represent a major public health challenge and contribute to a substantial number of deaths. Individuals living with chronic illnesses frequently experience anxiety due to the long-term impact on their overall well-being. The development of a predictive model to accurately identify anxiety in these patients is of great importance for healthcare providers. This study aimed to create a predictive model focused on anxiety among patients with chronic diseases within the Moroccan population. Methods and materials: In this study, a comparative analysis was performed using five different machine learning algorithms. The researchers utilized a cross-sectional dataset comprising 938 patients with chronic diseases. These patients were under monitoring at the Hassan II University Hospital center in Fez, Morocco, from October 2019 to December 2020. Anxiety levels were evaluated using the validated Moroccan version of the Hospital Anxiety and Depression Scale (HADS). Results: To assess the models’ performance, several metrics were calculated, including accuracy, AUC, precision, recall, and F1-measure. The CatBoost algorithm achieved the highest accuracy of 0.7 in the evaluation. It also obtained an AUC of 0.68, a precision of 0.7, a recall of 0.55, and an F1-measure of 0.6. Based on its accuracy performance, the CatBoost algorithm is considered the optimal model in this study. Conclusion: This study created a CatBoost model to predict anxiety in patients with chronic diseases, offering valuable insights for anxiety prevention. Early psychological support and intervention are beneficial for high-risk patients, aiding in anxiety management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of Gaussians

arXiv

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arXiv 2023年

作者： Park, Core Francisco Pérez-Carrasco, Manuel Nasr, Maya Walker, Eleanor Finkbeiner, Douglas Garraffo, Cecilia Department of Physics Harvard University CambridgeMA02138 United States Data Science Unit University of Concepcion Biobio Chile John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States AstroAI Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States

Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT. © 2023, CC BY.

关键词： Maximum likelihood

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Analysis of temporal variation of cosmic ray intensity observed with global networks of neutron monitors and muon detectors 38

Analysis of temporal variation of cosmic ray intensity obser...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Munakata, K. Kozai, M. Kato, C. Hayashi, Y. Masuda, Y. Kataoka, R. Kadokura, A. Miyake, S. Iwai, K. Mendonça, R.R.S. Echer, E. Dal Lago, A. Rockenbach, M. Schuch, N.J. Bageston, J.V. Braga, C.R. Al Jassar, H.K. Sharma, M.M. Duldig, M.L. Humble, J.E. Sabbah, I. Mangeard, P.-S. Kuwabara, T. Evenson, P. Department of Physics Shinshu University Nagano Matsumoto390-8621 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa Tokyo190-0014 Japan National Institute of Polar Research Tachikawa Tokyo190-8518 Japan Department of Polar Science School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tachikawa Tokyo190-8518 Japan Ibaraki College 866 Nakane Hitachinaka-shi Ibaraki-ken312-8508 Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Nagoya464-8601 Japan São José dos Campos12227-010 Brazil Southern Space Coordination National Institute for Space Research P.O. Box 5021-97110-970 RS Santa Maria Brazil George Mason University 4400 University Drive FairfaxVA22030 United States Physics Department Kuwait University P.O. Box 5969 Safat13060 Kuwait School of Natural Sciences University of Tasmania HobartTAS7001 Australia Department of Laboratory Technology College of Technological Studies The Public Authority for Applied Education and Training Shuwaikh72853 Kuwait Bartol Research Institute Department of Physics and Astronomy University of Delaware 217 Sharp Laboratory NewarkDE19716 United States

The temporal variation of cosmic ray intensity recorded by a ground-based detector includes contributions from varying cosmic ray density (or isotropic intensity) and anisotropy in space. We deduce these contributions separately and accurately, each as a function of primary cosmic ray rigidity and time, by analyzing the cosmic ray intensity observed with global networks of neutron monitors and multidirectional muon detectors altogether. In such analyses, however, we need to pay special attention to local effects including atmospheric effects which are superposed on the signal temporal variation of cosmic ray density and anisotropy. This is particularly important in analyses of the data during the "quiet period" when only small signal variation is expected. We present our analyses of the data during two sample periods in January-February and July-August, 2012, during each of which an extended cosmic ray decrease lasting for ~five days with a strong anisotropy was observed. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Anisotropy

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data mining of cosmic-ray anisotropy observed with the Global Muon Detector Network 38

Data mining of cosmic-ray anisotropy observed with the Globa...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Kozai, M. Hayashi, Y. Kato, C. Munakata, K. Masuda, Y. Iwai, K. Rockenbach, M. Dal Lago, A. Mendonça, R.R.S. Echer, E. Bageston, J.V. Braga, C.R. Al Jassar, H.K. Sharma, M.M. Duldig, M.L. Humble, J.E. Kadokura, A. Kataoka, R. Miyake, S. Sabbah, I. Mangeard, P.-S. Kuwabara, T. Evenson, P. Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa Tokyo190-0014 Japan Department of Physics Shinshu University Nagano Matsumoto390-8621 Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Nagoya464-8601 Japan São José dos Campos12227-010 Brazil Southern Space Coordination National Institute for Space Research P.O. Box 5021-97110-970 RS Santa Maria Brazil George Mason University 4400 University Drive FairfaxVA22030 United States Physics Department Kuwait University P.O. Box 5969 Safat13060 Kuwait School of Natural Sciences University of Tasmania HobartTAS7001 Australia National Institute of Polar Research Tachikawa Tokyo190-8518 Japan Department of Polar Science School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tachikawa Tokyo190-8518 Japan Ibaraki College Ibaraki Hitachinaka312-0011 Japan Department of Laboratory Technology College of Technological Studies The Public Authority for Applied Education and Training Shuwaikh72853 Kuwait Bartol Research Institute Department of Physics and Astronomy University of Delaware 217 Shar Laboratory NewarkDE19716 United States

Ground-based muon detectors are sensitive to anisotropies of cosmic rays at approximately 50 GeV and have been operated with great stability for over 10 years. The anisotropy is controlled by the space environment in various time scales and is an attractive target of data-driven approaches. We report on unsupervised machine-learning of anisotropy data accumulated by the Global Muon Detector Network (GMDN). It is expected to provide a comprehensive and statistical picture of cosmic-ray anisotropies free from biases such as data selections relying on a visual inspection. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic ray detectors

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