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Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

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arXiv 2023年

作者： Marroquin, Russell D. Sadykov, Viacheslav Kosovichev, Alexander Kitiashvili, Irina N. Oria, Vincent Nita, Gelu M. Illarionov, Egor O'Keefe, Patrick M. Francis, Fraila Chong, Chun-Jie Kosovich, Paul Ali, Aatiya Department of Physics University of California San Diego La Jolla CA92093 United States Physics & Astronomy Department Georgia State University AtlantaGA30303 United States Physics Department New Jersey Institute of Technology NewarkNJ07102 United States Nasa Ames Research Center Moffett Field CA94035 United States Computer Science Department New Jersey Institute of Technology NewarkNJ07102 United States Department of Mechanics and Mathematics Moscow State University Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Moscow119234 Russia

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar lares and coronal mass ejections (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle lux enhancements, most widely known as Solar Energetic Particle events (SEPs). In this work, we utilize the data available for Solar Cycles 21-24, and the initial phase of Cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs (≈60%, or 108 out of 181 considered events) were generated from an AR classified with the 'k' Mcintosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing δ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs. © 2023, CC BY.

关键词： Weather forecasting

Machine Learning Technique for Analyzing the Behavior of Fish in an Aquarium 1st

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Machine Learning Technique for Analyzing the Behavior of Fis...

1st International Conference on Modeling, Machine Learning and Astronomy, MMLA 2019

作者： Bhaskaran, Rishabh Baskaran, Rajesh Kanna Vijayalakshmi, C. Software Engineer -II Walmartlabs Bengaluru India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Statistics and Applied Mathematics Central University of Tamil Nadu Thiruvarur India

ISBN: (纸本)9789813364622

Over the years lots of marine biologists and scientists have made efforts to study the various problem related to the ocean and its animals. One such problem faced by the fisheries is the change in the physical and mental state of the fishes when they are affected by natural calamities, external agents, change in environment, epidemic outbreak or many such factors. Under such circumstances it’s probable that the fishes in the region may get affected and due to a small amount of bad fish the whole population could suffer which is a huge loss both biologically and economically. There are numerous research and studies back on the fact that fishes tend to change their behaviour in the situation of distress. The behavioural change is just in response to the distress caused to them either physically or psychologically. The motion of the fish over a period of time interval can help us learn about the behaviour it is showing. And a significant change in its pattern of motion can alarm us that there is some issue with the fish. The main idea is to identify and establish the relationship between the movements and behaviour of the fish. The video footage of a Tilapia Genus fish in a standard size aquarium set up with the help of surveillance cameras, tracked the motion of the fish in 2-D space is collected and the movement of the fish over a period of time is categorised. Based on the output the losses can be reduced. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fish

The number of limit cycles for regularized piecewise polynomial systems is unbounded

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arXiv 2021年

作者： Huzak, R. Kristiansen, K. Uldall Hasselt University Campus Diepenbeek Agoralaan Gebouw D Diepenbeek3590 Belgium Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark

In this paper, we extend the slow divergence-integral from slow-fast systems, due to De Maesschalck, Dumortier and Roussarie, to smooth systems that limit onto piecewise smooth ones as ϵ → 0. In slow-fast systems, the slow divergence-integral is an integral of the divergence along a canard cycle with respect to the slow time and it has proven very useful in obtaining good lower and upper bounds of limit cycles in planar polynomial systems. In this paper, our slow divergence-integral is based upon integration along a generalized canard cycle for a piecewise smooth two-fold bifurcation (of type visible-invisible called V I3). We use this framework to show that the number of limit cycles in regularized piecewise smooth polynomial systems is unbounded. Copyright © 2021, The Authors. All rights reserved.

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Ab initio quantum many-body description of superconducting trends in the cuprates

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arXiv 2023年

作者： Cui, Zhi-Hao Yang, Junjie Tölle, Johannes Ye, Hong-Zhou Yuan, Shunyue Zhai, Huanchen Park, Gunhee Kim, Raehyun Zhang, Xing Lin, Lin Berkelbach, Timothy C. Chan, Garnet Kin-Lic Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry Columbia University New YorkNY10027 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Department of Mathematics University of California BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of several doped cuprate materials and structures within a purely electronic picture. We find that we can correctly capture two well-known trends: the pressure effect, where the pairing order and gap increase with intra-layer pressure, and the layer effect, where the pairing order and gap vary with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors for these trends. Our microscopic analysis further identifies that strong short-range spin fluctuations and multi-orbital charge fluctuations drive the development of the pairing order. Our work illustrates the possibility of a material-specific ab initio understanding of unconventional high-temperature superconducting materials. Copyright © 2023, The Authors. All rights reserved.

关键词： High temperature superconductors

Network Intrusion Detection System Using Principal Component Analysis Algorithm and Decision Tree Classifier

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Network Intrusion Detection System Using Principal Component...

International Conference on computational science and computational Intelligence (CSCI)

作者： Oyeyemi Osho Sungbum Hong Tor A. Kwembe Department of Computational Data-Enabled Science and Engineering(CDS&#x0026 E) Jackson State University Jackson MS USA Department of Electrical &#x0026 Computer Engineering and Computer Science Jackson State University Jackson MS USA Department of Mathematics &#x0026 Statistical Sciences Jackson State University Jackson MS USA

ISBN: (纸本)9781665458429

Network Intrusion Detection Systems (IDS) have become expedient for network security and ensures the safety of all connected devices. Network Intrusion Detection System (IDS) alludes to observing network data information swiftly, detecting any intrusion pattern and preventing any harmful effect of anomaly intrusion that will cost the network. To combat this issue, we present in this concept paper an IDS based on the Principal Component Analysis (PCA) and Decision Tree Classifier algorithm, a supervised machine learning model to detect intrusion in the Network.

关键词： Machine learning algorithms Costs Scientific computing Network intrusion detection Network security Classification algorithms Safety

From order to disorder of alkanethiol SAMs on complex Au (211), (221) and (311) surfaces: Impact of the substrate

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arXiv 2021年

作者： Stefanakis, Dimitrios Harmandaris, Vagelis Kopidakis, Georgios Remediakis, Ioannis Department of Materials Science & Technology University of Crete Vassilika Voutes Heraklion700 13 Greece Department of Mathematics & Applied Mathematics - University of Crete Vassilika Voutes Heraklion700 13 Greece Institute of Applied & Computational Mathematics Foundation for Research and Technology-Hellas 711 10 Heraklion Greece Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia2121 Cyprus Institute of Electronic Structure and Laser Foundation for Research and Technology-Hellas 711 10 Heraklion Greece

We investigate the impact of the substrate on the structural properties and the morphology of alkanethiol self-assembled monolayers (SAMs) on gold, using first principles calculations and atomistic molecular dynamics simulations. We consider hexadecanethiols on Au(211), Au(221) and Au(311) surfaces which contain few-atom wide terraces separated by monoatomic steps similar to the complex Au surfaces used in experiments. The structure of the SAMs is probed via several structural properties including tilt angles, mean C atom heights from the surface, precession angles, gauche defects, gyration tensors and relative shape anisotropy. Comparing these properties to those of the well-studied SAMs on Au(111), we observe similarities but also striking differences. A clear order to disorder transition is observed by changing the substrate: well-ordered SAMs on (111) and (211) surfaces become mixed ordered-disordered structures on (311) and fully disordered on (221). The presence of steps on the Au surfaces also results in preferential tilt orientations with long-range order. Our results show that in addition to the expected grafting density dependence, the transition from order to disorder crucially depends on substrate morphology. The onset of ordering behavior is related to the atomic structure of the surface. The key parameter that affects long-range order is the energy for changing the dihedral angle between Au-S-C(1) − C(2) of the adsorbed alkanethiol. © 2021, CC BY.

关键词： Molecular dynamics

Upper Hessenberg decomposition matrix utilized to build zero watermarking technique in YCbCr space

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AIP Conference Proceedings 2022年第1期2394卷

作者： Nada Sabeeh Mohammed Areej M. Abduldaim University Of Technology Applied Science Department Mathematics And Computer Applications Baghdad Iraq.

The properties of matrices as a subject in linear algebra are used in the topic of image processing via employing many algebraic methods in several areas, including security. The digital image watermarking techniques represent the security technique type addressed in this work. This paper aims to implement and demonstrate the possibility of successfully using the algebraic Hessemberge decomposition method (HDM) for the first time in building a zero watermarking technique as a transform to extract the features of the image without using any of the customary transformations. Also, the improvement of the results of the technique is performed by applying discrete wavelet transform (DWT) as an additional transform to increase the accuracy in the extraction of the watermark. Two techniques are adopted to achieve the aim. The basic idea behind both techniques is to make use of the algebraic Hessemberge decomposition method (HDM) as a mathematical tool to transform the image into another domain. In the first one, the YCbCr is applied to the color image to obtain the Y component. The HDM is carried out to create one matrix from the Hi matrices to get the master-secret. In the second technique, the DWT is utilized on the Y component to obtain the low-frequency band (LL). which is a low-resolution approximation of the original image. After that, the HDM is used to create one matrix from all the Hi matrices to get the master-secret. The experimental results show that the NC values under a lot of the attacks are improved in the second technique. But the NC values of salt and pepper attack in the first technique are higher. Some attacks retained the same values of the NC in both techniques.

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Single Cell Investigative Genetics: Single-Cell Data Produces Genotype Distributions Concentrated at the True Genotype Across All Mixture Complexities

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SSRN

SSRN 2023年

作者： Grgicak, Catherine M. Bhembe, Qhawe Sheth, Nidhi C. Duffy, Ken R. Lun, Desmond S. Department of Chemistry Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Program in Biomedical Forensic Sciences Boston University BostonMA02118 United States Department of Mathematics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Hamilton Institute Maynooth University Ireland Department of Computer Science Rutgers University CamdenNJ08102 United States

In the absence of a suspect or PoI the forensic aim is investigative, and the focus is one of discerning what candidate genotypes best explain the evidence. In traditional bulk systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor’s contribution and, as a result, possibly overlooked or inefficient investigative *** developments in single-cell analysis offer a way forward, by producing data capable of discriminating between genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster’s content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes’ rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least . This is the credible genotype set and is the one used to inform database search criteria and, thus, investigative *** demonstrate the salience of single cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most proba

关键词： DNA

One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions

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arXiv 2025年

作者： Schmid, Stefan P. Rajaonson, Ella Miray Ser, Cher Tian Haddadnia, Mohammad Leong, Shi Xuan Aspuru-Guzik, Alán Kristiadi, Agustinus Jorner, Kjell Strieth-Kalthoff, Felix Institute of Chemical and Bioengineering Department of Chemistry and Applied Biosciences ETH Zurich ZurichCH-8093 Switzerland Catalysis ETH Zurich ZurichCH-8093 Switzerland Department of Chemistry University of Toronto Toronto Canada Vector Institute Toronto Canada Department of Biological Chemistry & Molecular Pharmacology Harvard University CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singarpore Singapore Department of Computer Science University of Toronto Toronto Canada Department of Chemical Engineering and Applied Chemistry University of Toronto Toronto Canada Department of Materials Science and Engineering University of Toronto Toronto Canada Acceleration Consortium University of Toronto Toronto Canada Canada School of Mathematics and Natural Sciences University of Wuppertal Wuppertal Germany

General parameters are highly desirable in the natural sciences — e.g., chemical reaction conditions that enable high yields across a range of related transformations. This has a significant practical impact since those general parameters can be transferred to related tasks without the need for laborious and time-intensive re-optimization. While Bayesian optimization (BO) is widely applied to find optimal parameter sets for specific tasks, it has remained underused in experiment planning towards such general optima. In this work, we consider the real-world problem of condition optimization for chemical reactions to study how performing generality-oriented BO can accelerate the identification of general optima, and whether these optima also translate to unseen examples. This is achieved through a careful formulation of the problem as an optimization over curried functions, as well as systematic evaluations of generality-oriented strategies for optimization Copyright © 2025, The Authors. All rights reserved.

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