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Studies of Critical Phenomena in Causal Dynamical Triangulations on a Torus

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arXiv 2023年

作者： Németh, Dániel Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Poland

This document contains my publications and results based on research done as a member of the Causal Dynamical Triangulations (CDT) group at the Jagiellonian university during my PhD studies. The field of my research was the four-dimensional CDT, which is a lattice regularization of the theory of quantum gravity, based on the formalism of Regge Calculus and Feynman path integrals. Due to mathematical complexity, analytical solutions to the model exists only in two dimensions. The four-dimensional theory is analyzed by numerical simulations. Earlier discoveries include dynamically emergent quantum de Sitter universes with emergent four-dimensional properties, scale-dependent spectral dimensions and a complex phase structure in which first- and higher-order phase transitions were shown to exist. The document describes the nature of previously not yet analyzed phase transitions, new ways to analyze triangulations and the impact of classical and dynamical (quantum) scalar fields in four-dimensional CDT with toroidal spatial topology. The main results of the dissertation are the six publications attached to the last chapter. This document is intended as an introduction to CDT and serves as a guide to the papers comprising the doctoral thesis. © 2023, CC BY.

关键词： Triangulation

Short-term solar eruptive activity prediction models based on machine learning approaches:A review

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science China Earth sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

CONSUMER TRANSACTIONS SIMULATION THROUGH GENERATIVE ADVERSARIAL NETWORKS UNDER STOCKS CONSTRAINTS IN LARGE-SCALE RETAIL

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arXiv 2024年

作者： Tkachuk, Sergiy Lukasik, Szymon Wróblewska, Anna Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 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 al. Mickiewicza 30 Kraków30-059 Poland

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability – an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

Gravitational wave resonance in ultralight dark matter halos

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Physical Review D 2023年第12期108卷 123539-123539页

作者： Paola C. M. Delgado Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Krakow Poland

Ultralight dark matter (ULDM) halos constituted by ultralight axions (ULAs) generate gravitational potentials that oscillate in time. In this paper, I show these potentials interact with gravitational waves, resonantly amplifying them. For all ULA masses considered, the resonance in the solar region is currently negligible, while in a denser dark matter environment, which may arise in different scenarios, it might become significant. The frequency of the amplified gravitational wave is equal to the ULA mass in the case of the first resonance band, which represents the most efficient scenario.

关键词： Gravitational waves Massive compact halo objects Particle dark matter

Influence of Ferromagnetic Interlayer Exchange Coupling on Current-induced Magnetization Switching and Dzyaloshinskii-Moriya Interaction in Co/Pt/Co Multilayer System

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arXiv 2022年

作者： Grochot, Krzysztof Ogrodnik, Piotr Mojsiejuk, Jakub Mazalski, Piotr Guzowska, Urszula Skowroński, Witold Stobiecki, Tomasz Institute of Electronics AGH University of Science and Technology Poland Faculty of Physics and Computer Science AGH University of Science and Technology Poland Faculty of Physics Warsaw University of Technology Warsaw Poland Faculty of Physics University of Bialystok Bialystok Poland

This paper investigates the relationship among interlayer exchange coupling (IEC), Dzyaloshinskii-Moriya interaction (DMI), and multilevel magnetization switching within a Co/Pt/Co heterostructure, where varying Pt thicknesses enable control over the coupling strength. Employing Brillouin Light Scattering to quantify the effective DMI, we explore its potential role in magnetization dynamics and multilevel magnetization switching. Experimental findings show four distinct resistance states under an external magnetic field and spin Hall effect related spin current. We explain this phenomenon based on the asymmetry between Pt/Co and Co/Pt interfaces and the interlayer coupling, which, in turn, influences DMI and subsequently impacts the magnetization dynamics. Numerical simulations, including macrospin, 1D domain wall, and simple spin wave models, further support the experimental observations of multilevel switching and help uncover the underlying mechanisms. Our proposed explanation, supported by magnetic domain observation using polar-magnetooptical Kerr microscopy, offers insights into both the spatial distribution of magnetization and its dynamics for different IECs, thereby shedding light on its interplay with DMI, which may lead to potential applications in storage devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Spin waves

A hybrid optimization approach for securing cloud-based e-health systems

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Multimedia Tools and Applications 2025年第16期84卷 16525-16560页

作者： Altherwi, Abdulhadi Ahmad, Mohammad Tauheed Alam, Md. Moddassir Mirza, Hidayath Sultana, Nazia Pasha, Amjad Ali Sultana, Nishat Khan, Asif Irshad Alam, Md Mottahir Azim, Rezaul Department of Industrial Engineering College of Engineering and Computer Science Jazan University PO Box. 706 Jazan45142 Saudi Arabia College of Medicine King Khalid University Abha Saudi Arabia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University Jazan45142 Saudi Arabia Government Medical College Siddipet Ensanpalli Siddipet District Telangana502114 India Aerospace Engineering Department King Abdulaziz University Jeddah21589 Saudi Arabia Department of Business Administration Applied College Jazan University P. O. Box 706 Jazan45142 Saudi Arabia Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz Jeddah21589 Saudi Arabia Department of Physics University of Chittagong Chattogram4331 Bangladesh

In modern healthcare, cloud-based e-health technology offers substantial benefits but faces significant security challenges. Sensitive patient data is vulnerable to cyber threats during transmission and storage, potentially enabling unethical collaboration between healthcare professionals and cloud storage providers. This paper aims to develop a robust security framework that safeguards patient privacy and prevents unauthorized access to sensitive medical information in remote patient health monitoring systems. The study presents an Artificial Hybrid Optimization with an Attribute-Based Encryption (AHO-ABE) model. The model leverages cryptographic techniques and hybrid optimization to enhance data security. Specifically, whale optimization (WO) is used for efficient key generation, while sheep flock optimization (SFO) is employed for continuous network surveillance. Experimental results demonstrate that the AHO-ABE model significantly enhances data security, outperforming existing RPM-MEC, AID, AI-HCS, and IoT-HMS approaches. The model exhibits superior resilience against unauthorized access and data breaches, achieving a 5 ms execution time, 850 kbps throughput, 0.121 mJ energy usage, and 4 s latency. Combining cryptographic techniques with hybrid optimization algorithms, the AHO-ABE model provides a robust framework for safeguarding sensitive medical information. This approach ensures the integrity and confidentiality of patient data in remote healthcare settings, offering an effective solution against high-performance cyber attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases

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Infectious Disease Modelling 2022年第1期7卷 170-183页

作者： Nurul Absar Md.Nazim Uddin Mayeen Uddin Khandaker Md.Habib Ullah Department of Computer Science and Engineering BGC Trust UniversityBangladeshChittagong4381Bangladesh Centre for Applied Physics and Radiation Technologies School of Engineering and TechnologySunway UniversityBandar Sunway47500SelangorMalaysia Department of Physics Faculty of Science and TechnologyAmerican International University-Bangladesh(AIUB)Dhaka1229Bangladesh

The coronavirus disease that outbreak in 2019 has caused various health *** to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the total confirmed,recovered,and death cases were 826922,766266 and 13118,*** to the emergence of COVID-19 in Bangladesh,the country is facing a major public health ***,the country does not have a comprehensive health policy to address this *** makes it hard to predict how the pandemic will affect the *** learning techniques can help us detect the disease's *** predict the trend,parameters,risks,and to take preventive measure in Bangladesh;this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term ***,we aim to predict the epidemic's progression for a period of more than a year under various scenarios in *** extracted the data for daily confirmed,recovered,and death cases from March 2020 to August *** obtained Root Mean Square Error(RMSE)values of confirmed,recovered,and death cases indicates that our result is more accurate than other contemporary *** study indicates that the LSTM model could be used effectively in predicting contagious *** obtained results could help in explaining the seriousness of the situation,also mayhelp the authorities to take precautionary steps to control the situation.

关键词： COVID-19 LSTM Recurrent neural networks(RNN) Prediction RMSE

Classification of Time Series Using FCM-Based Forecasting Models

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Classification of Time Series Using FCM-Based Forecasting Mo...

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Miłosz Wrzesień Mariusz Wrzesień Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland Faculty of Applied Informatics University of Information Technology and Management Rzeszów Poland

In this study, a method of time series classification is considered. Classification is performed using forecasting models. It is assumed that processed time series are of different natures, i.e., they belong to different classes. Each class has its forecasting model. Thus, an unknown time series is presented to the models to evaluate forecasting errors. The classified time series is assigned to the class with the winning forecasting model. In the study, Fuzzy Cognitive Maps are used to build forecasting models. Prior to forecasting, the processed raw time series are preprocessed. Six different error functions having the most significant influence on classification are used. The error functions come from root mean square error and mean percentage error.

关键词：

"To renormalize or not to renormalize ?" in the proton-deuteron scattering calculations

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arXiv 2024年

作者： Witala, H. Golak, J. Skibiński, R. Smoluchowski, M. Institute of Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University KrakówPL-30059 Poland

We discuss two approaches which, by applying the screening method, permit one to include the long range proton-proton (pp) Coulomb force in proton-deuteron (pd) momentum-space scattering calculations. In the first one, based on Alt-Grassberger-Sandhas (AGS) equation, presented in Phys. Rev. C71, 054005 (2005) and 73, 057001 (2006), one needs to renormalize elastic scattering amplitude before calculating observables. In the second treatment, proposed by us in Eur. Phys. Journal A 41, 369 (2009), 41, 385 (2009), and arXiv:2310.03433 [***], this renormalization is avoided. For the proton induced deuteron breakup reaction both approaches require renormalization of the corresponding transition amplitudes. We derive the basic equations underlying both methods under the assumption that all contributing partial wave states are included and explain why in our approach renormalization of the elastic scattering amplitude is superfluous. We show that in order to take into account in the screening limit all partial waves it is required that four additional terms, based on the 3-dimensional and partial-wave projected pp Coulomb t-matrices, identical for both approaches, must appear in transition amplitudes. We investigate importance of these terms for elastic pd scattering below the breakup threshold. © 2024, CC BY.

关键词： Elastic scattering