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Method for controlling phytoplankton distribution in fresh open water

International Journal of Environmental Studies 2024年第5期81卷 2130-2147页

作者： Moldasheva, Raushan Ismailova, Aisulu Turmukhanova, Gulnur Mailybayeva, Aiman Yelezhanova, Shynar Zhanuzakova, Zulfiya Zhamangara, Aizhan Department of Information Systems Faculty of Computer Systems and Vocational Education S. Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Software Engineering Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Department of Computer Science Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Laboratory of Palaeobotany Astana Botanical Garden branch of the Institute of Botany and Phytointroduction Astana Kazakhstan

This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in fresh open water using a gradient descent approach with the search for a starting point for research. The paper also presents the reconstructed biomass indicators for changes in water temperature, with model calibration based on results from 5 years of surface sampling. A special feature of the proposed computer data processing is the cubic spline interpolation, which allows a higher calculation accuracy. This method reduces the sample size required, providing a comprehensive picture of the condition of the infrastructure and allowing for a more efficient response to emerging ecosystem issues. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Phytoplankton

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Enhancement of the topological regime in elongated Josephson junctions

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Physical Review B 2025年第8期111卷 085416-085416页

作者： D. Kuiri P. Wójcik M. P. Nowak AGH University of Krakow Academic Centre for Materials and Nanotechnology al. A. Mickiewicza 30 30-059 Krakow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 30-059 Krakow Poland

We theoretically study topological superconductivity in elongated planar Josephson junctions. In the presence of spin-orbit coupling and an in-plane magnetic field, the Josephson junction can enter the topological phase and host zero-energy Majorana bound states over a range of the superconducting phase difference centered around π, with the span of this range determined by the strength of the magnetic field. We demonstrate that the topological superconducting phase range can be greatly increased by elongation of the junction, which causes an amplification of the Zeeman-induced phase shift of Andreev bound states. We show that the appearance of trivial in-gap states that occurs in elongated junctions can prohibit the creation of Majorana modes, but it can be mitigated by further proximitization of the junction with additional superconducting contacts. The topological transition in this system can be probed by measurements of the critical current and we show that the elongation of the junction leads to a linear decrease of the transition critical magnetic field beneficial for experimental studies.

关键词： Magnetic fields

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Ask the experts: Sourcing a high-quality nutrition counseling dataset through Human-AI collaboration

Ask the experts: Sourcing a high-quality nutrition counselin...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Balloccu, Simone Reiter, Ehud Li, Karen Jia-Hui Sargsyan, Rafael Kumar, Vivek Recupero, Diego Reforgiato Riboni, Daniele Dusek, Ondrej Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic Department of Computing Science University of Aberdeen United Kingdom FI CODE University of Bundeswehr Munich Germany Department of Mathematics and Computer Science University of Cagliari Italy

ISBN: (纸本)9798891761681

Large Language Models (LLMs) are being employed by end-users for various tasks, including sensitive ones such as health counseling, disregarding potential safety concerns. It is thus necessary to understand how adequately LLMs perform in such domains. We conduct a case study on ChatGPT in nutrition counseling, a popular use-case where the model supports a user with their dietary struggles. We crowdsource real-world diet-related struggles, then work with nutrition experts to generate supportive text using ChatGPT. Finally, experts evaluate the safety and text quality of ChatGPT's output. The result is the HAI-Coaching dataset, containing ∼2.4K crowdsourced dietary struggles and ∼97K corresponding ChatGPT-generated and expert-annotated supportive texts. We analyse ChatGPT's performance, discovering potentially harmful behaviours, especially for sensitive topics like mental health. Finally, we use HAI-Coaching to test open LLMs on various downstream tasks, showing that even the latest models struggle to achieve good performance. HAI-Coaching is available at https://***/uccollab/hai-coaching/. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A New Approach Towards Identification of Individuals Belonging to Different Subpopulations Using Machine Learning Models and CLC Data 24

A New Approach Towards Identification of Individuals Belongi...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Charytanowicz, Malgorzata Perzanowski, Kajetan Januszczak, Maciej Woloszyn-Galeza, Aleksandra Sobczuk, Maria Kulczycki, Piotr Lublin University of Technology Department of Computer Science Lublin Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland John Paul II Catholic University of Lublin Institute of Biological Sciences Lublin Poland Polish Academy of Sciences Museum and Institute of Zoology Ustrzyki Dolne Poland Warsaw University of Life Sciences Chair of Animal Genetics and Conservation Warsaw Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland

ISBN: (纸本)9798331530631

Spatial distributions of wisents (European bison) Bison bonasus (Linnaeus 1758), were studied in the Bieszczady Mountains (south-eastern Poland) on the basis of telemetric data obtained from two subpopulations: the western - inhabiting the forest districts of Baligród, Komańcza, Cisna and Lesko, and the eastern - dwelling within the districts of Lutowiska, Stuposiany, and in Bieszczadzki National park. Data was collected between 2002-2021. In our study, we propose a novel approach for classifying wisent subpopulations that utilizes machine learning methods and CLC data. For this purpose, the performances of eight algorithms: Naive Bayes, Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbors and Decision Tree were investigated. The algorithms were compared according to the following indicators: accuracy, Cohen's-Kappa, precision, recall and F1 score. Their assessment was enhanced through the application of statistical inference (the Friedman test with post-hoc analysis) and SHAP values. The lowest results were achieved by Naive Bayes and Logistic Regression methods (accuracy of 73.69% and 74.43%, respectively), whilst significantly higher results were achieved by eXtreme Gradient Boosting, k-Nearest Neighbors and Decision Tree classifiers, with the classification accuracy exceeding 90% (91.81%, 92.8%, 93.47%, respectively). Based on the results, we conclude that CLC data represents a valuable source of information regarding the affinity of wisents towards different habitat conditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Group Lasso for High-Dimensional Categorical Data 23rd

Improving Group Lasso for High-Dimensional Categorical Data

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23rd International Conference on Computational science, ICCS 2023

作者： Nowakowski, Szymon Pokarowski, Piotr Rejchel, Wojciech Soltys, Agnieszka Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Faculty of Physics University of Warsaw Warsaw Poland Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland

ISBN: (纸本)9783031360206

Sparse modeling or model selection with categorical data is challenging even for a moderate number of variables, because roughly one parameter is needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection of continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso, we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso;then, to choose the final model, we use an information criterion on a small family of models prepared by clustering levels of individual factors. In the consequence, our procedure reduces dimensionality of the Group Lasso and strongly improves interpretability of the final model. What is important, this reduction results only in the small increase of the prediction error. In the paper we investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as the real data sets and show that it outperforms the state of the art algorithms with respect to the prediction accuracy, model dimension and execution time. Our procedure is contained in the R package DMRnet and available in the CRAN repository. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Variational Monte Carlo approach for the Hubbard model applied to twisted bilayer WSe2 at half-filling

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Physical Review B 2024年第12期109卷 125144-125144页

作者： A. Biborski P. Wójcik M. Zegrodnik AGH University of Krakow Academic Centre for Materials and Nanotechnology Al. Mickiewicza 30 30-059 Krakow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 30-059 Krakow Poland

We consider an effective Hubbard model with spin- and direction-dependent complex hoppings t, applied to twisted homobilayer WSe2 using a variational Monte Carlo approach. The electronic correlations are taken into account by applying the Gutzwiller on-site correlator as well as long-range Jastrow correlators subjected to noninteracting part being of Pfaffian form. Our analysis shows the emergence of the Mott insulating state at the critical value of Hubbard interaction Uc1≈6.5|t|∼7|t| estimated by extrapolating the density-density equal-time two-particle Green's functions. The signatures of an intermediate insulating phase between Uc1 and Uc2≈9.5|t|∼10|t| are also discussed. Furthermore, we report the formation of the 120∘ in-plane Néel state indicated by the detailed analysis of the spin-spin correlation functions. As shown, switching between antiferromagnetic phases characterized by opposite chirality could be experimentally realized by the change of perpendicular electric field. In a proper range of electric fields, also a transition to the in-plane ferromagnetic state appears.

关键词： Antiferromagnetism Critical phenomena Electronic structure Flat bands Magnetic order Magnetism Spin-orbit coupling 2-dimensional systems Mott insulators Transition metal dichalcogenides Twisted heterostructures Approximation methods for many-body systems Electron-correlation calculations Many-body techniques Monte Carlo methods Second quantization Trial wave functions

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Attractive Multistep Reproducing Kernel Approach for Solving Stiffness Differential Systems of Ordinary Differential Equations and Some Error Analysis

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computer Modeling in Engineering & sciences 2022年第1期130卷 299-313页

作者： Radwan Abu-Gdairi Shatha Hasan Shrideh Al-Omari Mohammad Al-Smadi Shaher Momani Department of Mathematics Faculty of ScienceZarqa UniversityZarqa13132Jordan Department of Applied Science Ajloun CollegeAl-Balqa Applied UniversityAjloun26816Jordan Department of Physics and Basic Sciences Faculty of Engineering TechnologyAl-Balqa Applied UniversityAmman11134Jordan Nonlinear Dynamics Research Center(NDRC) Ajman UniversityAjman20550United Arab Emirates Department of Mathematics Faculty of ScienceThe University of JordanAmman11942Jordan

In this paper,an efficient multi-step scheme is presented based on reproducing kernel Hilbert space(RKHS)theory for solving ordinary stiff differential *** solution methodology depends on reproducing kernel functions to obtain analytic solutions in a uniform formfor a rapidly convergent series in the posed Sobolev *** the Gram-Schmidt orthogonality process,complete orthogonal essential functions are obtained in a compact field to encompass Fourier series expansion with the help of kernel properties ***,by applying the standard RKHS method to each subinterval,approximate solutions that converge uniformly to the exact solutions are *** this purpose,several numerical examples are tested to show proposed algorithm’s superiority,simplicity,and *** gained results indicate that themulti-step RKHSmethod is suitable for solving linear and nonlinear stiffness systems over an extensive duration and giving highly accurate outcomes.

关键词： Multi-step approach reproducing kernel Hilbert space method stiffness system error analysis numerical solution

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SURVEY OF ACTION RECOGNITION, SPOTTING AND SPATIO-TEMPORAL LOCALIZATION IN SOCCER - CURRENT TRENDS AND RESEARCH PERSPECTIVES

arXiv

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arXiv 2023年

作者： Seweryn, Karolina Wróblewska, Anna Lukasik, Szymon NASK - National Research Institute Warsaw Poland Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Physics and Applied Computer Science AGH University of Science and Technology Cracow Poland

Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a comprehensive overview of this task divided into action recognition, spotting, and spatio-temporal action localization, with a particular emphasis on the modalities used and multimodal methods. We explore the publicly available data sources and metrics used to evaluate models' performance. The article reviews recent state-of-the-art methods that leverage deep learning techniques and traditional methods. We focus on multimodal methods, which integrate information from multiple sources, such as video and audio data, and also those that represent one source in various ways. The advantages and limitations of methods are discussed, along with their potential for improving the accuracy and robustness of models. Finally, the article highlights some of the open research questions and future directions in the field of soccer action recognition, including the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer. © 2023, CC BY.

关键词： Deep learning

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Takagi-Sugeno Type Neuro Fuzzy System Model Based Fault Diagnostic in Photovoltaic System 21st

Takagi-Sugeno Type Neuro Fuzzy System Model Based Fault Diag...

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21st Mexican International Conference on Artificial Intelligence, MICAI 2022

作者： Douiri, Moulay Rachid Aouzale, Noureddine Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakesh Morocco

ISBN: (纸本)9783031194955

Exposing photovoltaic installations to the outdoors for a long time leads to various breakdowns. Accurate and quantitative diagnosis of defect severity is vital for decision-making given the cost of defect removal. In this paper, a Takagi-Sugeno type neuro-fuzzy approach for the detection, identification and location of faults in solar photovoltaic systems is proposed. Then, an algorithm for optimizing the structure of the neuro-fuzzy model is developed, in order to improve the convergence capacity, resolve the influence of random parameters on the predictive performances, and to reduce both the number of synaptic weights and membership functions. This approach accurately detects, identifies and locates photovoltaic faults based on measurements of fault current, photovoltaic voltage, irradiance level, temperature and weather. Various fault scenarios are experimentally verified and validated in a large-scale solar photovoltaic power plant in order to demonstrate the efficiency and accuracy of the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Membership functions

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Factorization breaking for higher moments of harmonic flow

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Physical Review C 2022年第3期105卷 034904-034904页

作者： Piotr Bożek Rupam Samanta AGH University of Science and Technology Faculty of Physics and Applied Computer Science Aleja Mickiewicza 30 30-059 Cracow Poland

We study correlations between harmonic flow vectors squared measured at different transverse momenta. One of the flow harmonics squared is taken at a fixed transverse momentum and correlated to the momentum-averaged harmonic flow squared of the same order. Such four-particle correlators, dependent on transverse momentum, have been recently measured experimentally. Factorization coefficients based on the ratio of such four-particle correlators allow the independent measurement of the flow vector and the flow magnitude factorization-breaking coefficient. Moreover, the correlation of the angles of flow harmonics as a function of transverse momentum can be extracted. Results are compared to preliminary data of the ALICE Collaboration. We also present predictions for the momentum-dependent factorization-breaking coefficient between mixed flow harmonics. The correlators with squares of mixed harmonics can serve as a way to independently measure the flow vector, the flow magnitude, and the flow angle correlations and could be used to gain additional information on the fluctuating initial state and the dynamics in heavy-ion collisions.

关键词： Collective flow Hydrodynamic models Particle correlations & fluctuations Relativistic heavy-ion collisions

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